Time series forecasting requires architectures that simultaneously achieve three competing objectives: (1) strict temporal causality for reliable predictions, (2) sub-quadratic complexity for practical scalability, and (3) multi-scale pattern recognition for accurate long-horizon forecasting. We introduce AutoHFormer, a hierarchical autoregressive transformer that addresses these challenges through three key innovations: 1) Hierarchical Temporal Modeling: Our architecture decomposes predictions into segment-level blocks processed in parallel, followed by intra-segment sequential refinement. This dual-scale approach maintains temporal coherence while enabling efficient computation. 2) Dynamic Windowed Attention: The attention mechanism employs learnable causal windows with exponential decay, reducing complexity while preserving precise temporal relationships. This design avoids both the anti-causal violations of standard transformers and the sequential bottlenecks of RNN hybrids. 3) Adaptive Temporal Encoding: a novel position encoding system is adopted to capture time patterns at multiple scales. It combines fixed oscillating patterns for short-term variations with learnable decay rates for long-term trends. Comprehensive experiments demonstrate that AutoHFormer 10.76X faster training and 6.06X memory reduction compared to PatchTST on PEMS08, while maintaining consistent accuracy across 96-720 step horizons in most of cases. These breakthroughs establish new benchmarks for efficient and precise time series modeling. Implementations of our method and all baselines in hierarchical autoregressive mechanism are available at https://github.com/lizzyhku/Autotime.

Time series anomaly detection aims to identify samples that deviate from a normal sample distribution in a time series, enabling various web-centric applications. Most existing approaches are static, targeting pre-defined types of anomalies. These methods thus fail to work well on streaming time series with changing data distributions and anomaly formats. To contend with such streaming time series and to accommodate memory constraints, we propose the first data-efficient streaming time series anomaly detection framework, called DESS. To accumulate historical knowledge, DESS includes a novel evolving proxy generation module to synthesize a small but informative proxy summarizing the historical data, facilitating data efficiency. Next, DESS employs an innovative heterogeneous temporal feature extraction module to explicitly capture correlations of multi-level time series semantics. Finally, DESS enables fast streaming anomaly detection by employing a parameter-efficient training scheme that only activates a subset of lightweight parameters while ensuring performance. Extensive experiments on real data offer insight into the effectiveness and efficiency of DESS, showing that it is able to outperform the best baselines by up to 17.53% while reducing the training time by up to 64.88%.

Next Point-of-interest (POI) recommendation has been widely used in real scenarios to predict the next possible location based on user behavior patterns. However, existing methods predominantly rely on spatio-temporal associations and check-in sequence relationships between users and POIs, which fall short for users with limited interactions with POIs. Moreover, user preferences are inherently multi-dimensional, rendering user selections often influenced by multiple factors such as location categories and multi-modal information. To mitigate these issues, we introduce aMulti-Modal Knowledge GraphModeling ofMulti-Dimensional User Preferences for Next-POIRecommendation (M4Recfor short). First, we define a multi-modal knowledge graph to organize the relationships among users, locations, categories, and multi-modal information. Subsequently, we use the multi-modal knowledge graph-based relation-aware network to derive comprehensive entity representations from the constructed knowledge graph. Next, employing the temporal knowledge prediction method, we predict the user's next-POI category and next-POI. Finally, the final recommendation results are obtained by enhancing the corresponding location prediction scores through category semantics. Extensive experimentation conducted on real-world datasets validates the superiority of our proposed method over state-of-the-art competitors.

Spatio-temporal data proliferates in numerous real-world domains, such as transportation, weather, and energy. Spatio-temporal deep learning models aims to utilize useful patterns in such data to support tasks like prediction, imputation, and anomaly detection. However, previousone-to-onedeep learning models designed for specific tasks typically require separate training for each use case, leading to increased computational and storage costs. To address this issue,one-to-manyspatio-temporal foundation models have emerged, offering a unified framework capable of solving multiple spatio-temporal tasks. These foundation models achieve remarkable success by learning general knowledge with spatio-temporal data or transferring the general capabilities of pre-trained language models. While previous surveys have explored spatio-temporal data and methodologies separately, they have ignored a comprehensive examination of how foundation models are designed, selected, pre-trained, and adapted. As a result, the overall pipeline for spatio-temporal foundation models remains unclear. To bridge this gap, we innovatively provide an up-to-date review of previous spatio-temporal foundation models from the pipeline perspective. The pipeline begins with an introduction to different types of spatio-temporal data, followed by details of data preprocessing and embedding techniques. The pipeline then presents a novel data property taxonomy to divide existing methods according to data sources and dependencies, providing efficient and effective model design and selection for researchers. On this basis, we further illustrate the training objectives of primitive models, as well as the adaptation techniques of transferred models. Overall, our survey provides a clear and structured pipeline to understand the connection between core elements of spatio-temporal foundation models while guiding researchers to get started quickly. Additionally, we introduce emerging opportunities such as multi-objective training in the field of spatio-temporal foundation models, providing valuable insights for researchers and practitioners.

Time series anomaly detection is typically used to identify data that deviates significantly from normal data, often indicating faults or failures in the underlying system, thus facilitating system stability and safety. Most existing methods focus on detecting anomalies after they occur, while research on predicting future anomalies remains scarce. Before anomalies manifest themselves, there are often subtle precursors exhibiting slight deviations from normal behavior, with varying reaction times and intensities. Next, the setting is often characterized by a lack of labeled data, which complicates model training. To address these challenges, we propose a time series anomaly prediction framework, TAP. It can adapt flexibly to varying reaction times of anomaly precursors across different variables using a period-aware multi-scale module, and it is able to strengthen the distinction between precursors and normal sequences via a dual-branch framework that combines reconstruction and contrastive learning. The contrastive branch employs a controlled generation strategy within the multi-scale patching to produce diverse hard negative samples for precursor identification. The reconstruction branch complements this by evaluating fluctuation magnitudes to ensure sensitivity to subtle variations. We report on experiments on eight datasets from diverse domains, finding that TAP is capable of competitive or superior performance compared to baseline methods for both anomaly detection and prediction.

With the proliferation of GPS-equipped edge devices, huge trajectory data are generated and accumulated in various domains, driving numerous urban applications. However, due to the limited data acquisition capabilities of edge devices, many trajectories are often recorded at low sampling rates, reducing the effectiveness of these applications. To address this issue, we aim to recover high-sample-rate trajectories from low-sample-rate ones enhancing the usability of trajectory data. Recent approaches to trajectory recovery often assume centralized data storage, which can lead to catastrophic forgetting, where previously learned knowledge is entirely forgotten when new data arrives. This not only poses privacy risks but also degrades performance in decentralized settings where data streams into the system incrementally. To enable decentralized training and streaming trajectory recovery, we propose aLightweight incremental framework for federatedTrajectoryRecovery, called LightTR+, which is based on a client-server architecture. Given the limited processing capabilities of edge devices, LightTR+ includes a lightweight local trajectory embedding module that enhances computational efficiency without compromising feature extraction capabilities. To mitigate catastrophic forgetting, we propose an intra-domain knowledge distillation module. Additionally, LightTR+ features a meta-knowledge enhanced local-global training scheme, which reduces communication costs between the server and clients, further improving efficiency. Extensive experiments offer insight into the effectiveness and efficiency of LightTR+.

Time series anomaly detection aims to identify samples that deviate from a normal distribution in a time series, which is practically important to a variety of real-world applications. Existing approaches are mostly centralized and domain-specific, and thus they are hard to generalize to time series of different domains that are decentralized due to the privacy concerns and the resulting data silos across institutions. To bridge this gap, we propose FAST-MAD, the first resource-aware framework for efficient federated time series anomaly detection. Operating under a client-server architecture, different clients in FAST-MAD can handle time series from distinct domains. In particular, FAST-MAD first employs a multi-resolution transformation module to capture hierarchical local semantics, frequency-oriented patching as well as inter-time-series interaction. An LLM serves as the main body of the local model for each client, owing to its strong knowledge transfer capabilities. Further, an adaptive modularized separation mechanism is integrated with sharded federated training to reduce computational costs, which innovatively splits the LLM into a U-shaped architecture. To address data heterogeneity across different clients, we propose a decomposed client-server alignment mechanism, featuring a tailored low-rank parameter decomposition that extracts domain-common knowledge. Extensive experiments on multiple cross-domain time series datasets offer insight into the effectiveness and efficiency of FAST-MAD, which outperforms SOTA baselines by up to 10.25% in terms of F1-score and reduces the training time by 40.93%.

In the field of material science, the analysis of the properties of crystalline materials is of key importance. Recently, machine learning has become a prominent tool for predicting the properties of materials based on their structure. However, the application of machine learning to crystal property prediction faces two significant challenges. The first is the scarcity of labeled data, due to the time-consuming and resource-intensive process of crystal property labeling. The second is the importance of leveraging specialized knowledge when performing crystal structure analysis, which requires adapting machine learning methods specifically for the crystal domain. In this paper, we propose a new semi-supervised framework, aKnowledge-BasedSemi-Supervised crystal property prediction (KBSS) framework, which employs consistency regularization to leverage both labeled and unlabeled data while incorporating crystal knowledge guidance. Specifically, to use unlabeled data efficiently, the KBSS framework incorporates two key modules: a knowledge-guided augmentation (KGA) module and an adaptive pseudo-label filtering (APF) module. The KGA module utilizes the Monte Carlo method to leverage knowledge from the crystal domain to guide weak and strong augmentations of crystal structures. The APF module enhances the pseudo-labeling process for unlabeled crystal data by enabling task-guided uncertainty adjustment and category-aware pseudo-label selection. The experimental results show that KBSS achieves state-of-the-art performance. All code is publicly available athttps://github.com/HaominYu0/KBSS.

Spatio-temporal data intelligence, which includes sensing, managing, and mining large-scale data across space and time, plays a pivotal role in understanding complex systems in real-world applications, such as urban computing and smart cities. With the rapid evolution of foundation models and their growing potential to transform spatio-temporal analytics, we propose a comprehensive half-day workshop (with at least 5 accepted papers, 3 keynote talks, 1 panel discussion, and over 50 attendees) at CIKM 2025, catering to professionals, researchers, and practitioners who are interested in spatio-temporal data intelligence and foundation models to address real-world challenges. The workshop will not only offer a platform for knowledge exchange but also acknowledge outstanding contributions through a distinguished Best Paper Award. A dedicated panel discussion will explore recent advances, emerging trends, and open challenges in integrating spatio-temporal data and emerging machine learning techniques, fostering dialogue between academia and industry. Note that this will be the eleventh time that our core members have organized a similar workshop. The previous 10 workshops were hosted in top-tier data mining and management venues, e.g., SIGKDD, WWW, and IJCAI, each of which attracted over 60 participants and 25 submissions on average.

The deployment of Internet of Things and cyber-physical technologies leads to increased collection of spatiotemporal data. In the transportation domain, this data is being used in an expanding range of applications, e.g., to enhance road safety. The capture of changes to traffic signs, e.g., due to wear, road construction, accidents, or theft, helps ensure well-functioning and safe road networks. However, this process has so far been largely manual, making it costly and meaning that changes are often captured with considerable delays. To capture changes more cost-effectively and more frequently, we introduce AutoTracker, an automated reporting system that exploits low-cost cameras in vehicles to capture changes. The system features a three-layer architecture—perception, analysis, and storage—that incorporates vehicle motion dynamics for improved performance. The analysis layer integrates physical modeling, R-tree-based retrieval, and calibration components to refine detection results. We also introduce new datasets and evaluation metrics to assess the performance of AutoTracker. Experiments show that AutoTracker is capable of state-of-the-art accuracy and efficiency in traffic sign change reporting.

Spatiotemporal kernel density visualization (STKDV) is used extensively for many geospatial analysis tasks, including traffic accident hotspot detection, crime hotspot detection, and disease outbreak detection. However, STKDV is a computationally expensive operation, which does not scale to large-scale datasets, high resolutions, and a large number of timestamps. Although a recent approach, the sliding-window-based solution (SWS), reduces the time complexity of STKDV, it (i) is unable to reduce the time complexity for supporting STKDV-based exploratory analysis, (ii) is not theoretically efficient, and (iii) does not provide optimization techniques for bandwidth tuning. To eliminate these drawbacks, we propose a prefix-set-based solution (PREFIX) that encompasses three methods, namely PREFIXsingle(addressing (i)), PREFIXmultiple(addressing (ii)), and PREFIXtuning(addressing (iii)). We offer theoretical and practical evidence that PREFIX is capable of outperforming the state-of-the-art solution (SWS). In particular, PREFIX achieves at least 115x to 1,906x speedups and is the first solution that can efficiently generate multiple high-resolution STKDVs for the large-scale New York taxi dataset with 13.6 million data points.

Movement trajectory similarity computation is important when supporting functionalities such as outlier detection and prediction that may, in turn, fuel a variety of transportation-related applications. Recent trajectory similarity learning solutions often assume that trajectories are available at a central location. Yet, we are witnessing the decentralized collection of increasingly massive volumes of trajectories due to the deployment of edge devices. To enable decentralized training and improved privacy, we propose a federated trajectory similarity learning framework that features privacy-preserving clustering based on a client-server architecture. The framework encompasses local, client-side trajectory preprocessing and representation learning. This is combined with a novel privacy-preserving clustering mechanism that ensures consistent model updates between clients and the server, thus alleviating the effects of trajectory heterogeneity across clients. In addition, the framework features a hierarchical central aggregation mechanism that supports clustered federated learning. Experiments on real data offer evidence that the effectiveness of the proposed framework performs as intended.

Time series forecasting has important applications across diverse domains. EasyTime, the system we demonstrate, facilitates easy use of time-series forecasting methods by researchers and practitioners alike. First, EasyTime enables one-click evaluation, enabling researchers to evaluate new forecasting methods using the suite of diverse time series datasets collected in the preexisting time series forecasting benchmark (TFB). This is achieved by leveraging TFB's flexible and consistent evaluation pipeline. Second, when practitioners must perform forecasting on a new dataset, a nontrivial first step is often to find an appropriate forecasting method. EasyTime provides an Automated Ensemble module that combines the promising forecasting methods to yield superior forecasting accuracy compared to individual methods. Third, EasyTime offers a natural language Q&A module leveraging large language models. Given a question like “Which method is best for long term forecasting on time series with strong seasonality?”, EasyTime converts the question into SQL queries on the database of results obtained by TFB and then returns an answer in natural language and charts. By demonstrating EasyTime11https://decisionintelligence.github.io/EasyTime, we aim to show how it simplifies the use of time-series forecasting and facilitates the development of new generations of time series forecasting methods.

Time series data captures properties that change over time. Such data occurs widely, ranging from the scientific and medical domains to the industrial and environmental domains. When the properties in time series exhibit spatial variations, we often call the data spatio-temporal. As part of the continued digitalization of processes throughout society, increasingly large volumes of time series and spatio-temporal data are available. In this tutorial, we focus on data-driven decision making with such data, e.g., enabling greener and more efficient transportation based on traffic time series forecasting. The tutorial adopts the holistic paradigm of “data-governance-analytics-decision.” We first introduce the data foundation of time series and spatio-temporal data, which is often heterogeneous. Next, we discuss data governance methods that aim to improve data quality. We then cover data analytics, focusing on five desired characteristics: automation, robustness, generality, explainability, and resource efficiency. We finally cover data-driven decision making strategies and briefly discuss promising research directions. We hope that the tutorial will serve as a primary resource for researchers and practitioners who are interested in value creation from time series and spatio-temporal data.

Blockchain technology has transformed financial services sectors by providing security, transparency, and immutability through decentralized ledger systems. However, while blockchain data can support a range of applications-such as user quality analysis, illegal activity detection, and transaction pattern identification-existing systems are restricted to basic queries on blocks and transactions due to their sequential data storage. To support queries more generally, we propose VGQ, the first verifiable graph query (VGQ) framework that enables efficient graph queries on blockchain systems without altering blockchain storage structures. VGQ integrates a query layer with an external graph database system and represents blockchain data as a directed transaction graph to improve the efficiency of graph query execution. To ensure reliable results, VGQ includes result verification with three key performance enhancing optimizations: (i) computing connected components to exclude irrelevant vertices and edges during verification; (ii) merging information from edges to accelerate completeness verification; and (iii) employing a dual pointer algorithm for efficient soundness verification. Experiments offer evidence that VGQ can improve on the state-of-the-art framework in terms of query efficiency by up to one order of magnitude and in terms of verification efficiency by up to two orders of magnitude.

Graph stream summarization refers to the process of processing a continuous stream of edges that form a rapidly evolving graph. The primary challenges in handling graph streams include the impracticality of fully storing the ever-growing datasets and the complexity of supporting graph queries that involve both topological and temporal information. Recent advancements, such as PGSS and Horae, address these limitations by using domainbased, top-down multi-layer structures in the form of compressed matrices. However, they either suffer from poor query accuracy, incur substantial space overheads, or have low query efficiency. This study proposes a novel item-based, bottom-up hierarchical structure, called HIGGS. Unlike existing approaches, HIGGS leverages its hierarchical structure to localize storage and query processing, thereby confining changes and hash conflicts to small and manageable subtrees, yielding notable performance improvements. HIGGS offers tighter theoretical bounds on query accuracy and space cost. Extensive empirical studies on real graph streams demonstrate that, compared to state-of-the-art methods, HIGGS is capable of notable performance enhancements: it can improve accuracy by over 3 orders of magnitude, reduce space overhead by an average of 30%, increase throughput by more than 5 times, and decrease query latency by nearly 2 orders of magnitude.

We study the problem of time series anomaly prediction, which is relevant to a range of real-world applications. Existing anomaly prediction methods rely on labeled training data for achieving acceptable accuracy. However, such data may be difficult to obtain; and in real-time deployments, anomalies can occur that were not seen in labeled data, thus making them difficult to predict. We provide a theoretical analysis and propose an Importance-based Generative Contrastive Learning method (IGCL) for unsupervised anomaly prediction. IGCL employs a controlled diffusion module to produce anomaly precursor patterns. Next, ICGL learns contextual representations to extract temporal dependencies from pairs of normal time series and anomaly precursors. IGCL is then able to predict anomalies by identifying anomaly precursors that will evolve into future anomalies. To address challenges caused by potentially complex precursor combinations involving multiple variables, we propose a memory bank with importance scores that stores representative samples adaptively and generates more complex anomaly precursors. Extensive experiments on nine benchmark datasets offer evidence that the proposed method is able to outperform state-of-the-art baselines.

Time Series Forecasting (TSF) is key functionality in numerous fields, such as financial investment, weather services, and energy management. Although increasingly capable TSF methods occur, many of them require domain-specific data collection and model training and do not generalize well when applied in other domains. Time Series Foundation Models (TSFMs) that are pre-trained on massive heterogeneous time series data aim to overcome these limitations. The prospects for generalizability have spurred the development of a new generation of TSFMs. This study proposes a benchmark, TSFM-Bench, to facilitate comprehensive and unified evaluation of TSFMs. TSFM-Bench covers a wide range of TSFMs, including those based on large language models and those pre-trained on time series data. TSFM-Bench supports multiple forecasting scenarios, including zero-shot, few-shot, and full-shot, enabling assessment across the full range of adaptation strategies. TSFM-Bench also provides a standardized experimental protocols for critical evaluation processes such as dataset splitting, loading, normalization, and few-shot sampling, facilitating consistency and fairness. We report on an extensive evaluation of TSFMs across a diverse range of datasets spanning multiple domains and exhibiting varied statistical characteristics. Specifically, we identify pros and cons and inherent limitations of existing TSFMs, and we propose potential directions for new model designs.

With the proliferation of GPS-enabled devices such as smartphones, the querying of moving objects has attracted substantial attention, with studies covering joins, range and kNN queries, similarity queries, etc. Challenges arise due to variable sampling frequencies, potential inaccuracies in location samples, and the unavailability of locations between samples. Existing similarity joins often rely on discrete location samples, which fail to capture movement uncertainty and may miss meaningful interactions. To address this limitation, we propose Intersection Similarity Join (IS-Join), a novel approach that identifies object pairs based on the overlap of their motion ranges rather than location-based proximity. We define motion ranges as the spatial regions an object may traverse within a given time period, and introduce an intersection similarity measure that quantifies their overlap. To efficiently process IS-Join queries, we develop a Hybrid Ball-tree indexing structure with a repartitioning strategy, enabling scalable candidate filtering. Additionally, we introduce pre-checking and pruning techniques to further reduce computational overhead. Extensive experiments on two real-world trajectory datasets demonstrate that IS-Join significantly outperforms well-designed baselines, achieving up to a 3x reduction in runtime. Our work opens new opportunities for applications such as urban mobility analysis, traffic monitoring, wildlife tracking, and contact tracing.

Trajectory representation learning (TRL) maps trajectories to vectors that can be used for many downstream tasks. Existing TRL methods use either grid trajectories, capturing movement in free space, or road trajectories, capturing movement in a road network, as input. We observe that the two types of trajectories are complementary, providing either region and location information or providing road structure and movement regularity. Therefore, we propose a novel multimodal TRL method, dubbed GREEN, to jointly utilize Grid and Road trajectory Expressions for Effective representatioN learning. In particular, we transform raw GPS trajectories into both grid and road trajectories and tailor two encoders to capture their respective information. To align the two encoders such that they complement each other, we adopt a contrastive loss to encourage them to produce similar embeddings for the same raw trajectory and design a mask language model (MLM) loss to use grid trajectories to help reconstruct masked road trajectories. To learn the final trajectory representation, a dual-modal interactor is used to fuse the outputs of the two encoders via cross-attention. We compare GREEN with 7 state-of-the-art TRL methods for 3 downstream tasks, finding that GREEN consistently outperforms all baselines and improves the accuracy of the best-performing baseline by an average of 15.99%. Code and data are available at https://github.com/slzhou-xy/GREEN.

Spatio-temporal data captures complex dynamics across both space and time, yet traditional visualizations are complex, require domain expertise and often fail to resonate with broader audiences. Here, we propose MapMuse, a storytelling-based framework for interpreting spatio-temporal datasets, transforming them into compelling, narrative-driven experiences. We utilize large language models and employ retrieval augmented generation (RAG) and agent-based techniques to generate comprehensive stories. Drawing on principles common in cinematic storytelling, we emphasize clarity, emotional connection, and audience-centric design. As a case study, we analyze a dataset of taxi trajectories. Two perspectives are presented: a captivating story based on a heat map that visualizes millions of taxi trip endpoints to uncover urban mobility patterns; and a detailed narrative following a single long taxi journey, enriched with city landmarks and temporal shifts. By portraying locations as characters and movement as plot, we argue that data storytelling drives insight, engagement, and action from spatio-temporal information. The case study illustrates how MapMuse can bridge the gap between data complexity and human understanding. The aim of this short paper is to provide a glimpse to the potential of the cinematic storytelling technique as an effective communication tool for spatio-temporal data, as well as to describe open problems and opportunities for future research.

Accurate vessel trajectory prediction facilitates improved navigational safety, routing, and environmental protection.However, existing prediction methods are challenged by the irregular sampling time intervals of the vessel tracking data from the global AIS system and the complexity of vessel movement.These aspects complicate model learning and generalization.To address these challenges and improve vessel trajectory prediction, we propose Multi-modAl Knowledge-Enhanced fRamework (MAKER) for vessel trajectory prediction.To contend better with the irregular sampling time intervals, MAKER features a Large language model-guided Knowledge Transfer (LKT) module that leverages pre-trained language models to transfer trajectory-specific contextual knowledge effectively.To enhance the ability to learn complex trajectory patterns, MAKER incorporates a Knowledge-based Self-paced Learning (KSL) module.This module employs kinematic knowledge to progressively integrate complex patterns during training, allowing for adaptive learning and enhanced generalization.Experimental results on two vessel trajectory datasets show that MAKER can improve the prediction accuracy of state-of-the-art methods by 12.08%-17.86%.

Dynamic Graph Neural Networks (DGNNs) are effective at capturing multidimensional data and enable many important applications. As model training is computationally intensive, distributed DGNN training is employed to accommodate large data. Also, when training DGNNs, so-called sliding window training is used predominantly, as it enhances both accuracy and efficiency. However, current distributed frameworks-such as snapshot partitioning, chunk-based partitioning, and L -hop cache-based communication-free vertex partitioning-are inherently incompatible with sliding window training. While communication-based vertex partitioning supports sliding window training, its design for static graphs limits the effectiveness in distributed DGNN training. Specifically, existing partitioning strategies fail to optimize communication across snapshots, while existing cache reuse and communication scheduling strategies ignore opportunities for optimization between sliding windows. To support distributed sliding window training, we present SWASH, a scalable and flexible communication framework that utilizes a S liding W indow-based c A che SH aring technique. Specifically, we propose a flexible communication framework that supports ratio adjustment and timing selection, as well as hyperparameter settings and adaptive scheduling. We also propose a lightweight partitioning strategy tailored to sliding window-based DGNN training to reduce both partitioning and communication overheads. Finally, to alleviate decreases in accuracy due to reduced communication, we propose a cache-sharing technique based on sliding windows for sharing boundary vertex embeddings. Comprehensive experiments show that SWASH is capable of training speedups of an average of 9.44× over state-of-the-art frameworks while maintaining the accuracy of fully communicating, non-caching training frameworks.

Approximate nearest neighbor (ANN) search on high-dimensional vector data is core functionality in an increasing number of real-world applications. However, most existing methods only focus on accelerating search by means of indexing that assumes that the data is static. The few methods capable of contending with dynamic data often face challenges such as decreased query accuracy following updates and low update efficiency. In this study, we propose Wolverine, the first proposal that, to our knowledge, enables efficient monotonic search path repair, thereby solving the graph-based ANN index update problem. Wolverine repairs disrupted monotonic search paths by adding in-edges to the out-neighbors of a point to be deleted. To improve efficiency, Wolverine+ restricts the search space to be within the 2-hop neighbors of the point to be deleted. In addition, Wolverine++ employs a sophisticated candidate selection policy to find high-quality candidates in the reduced search space, simultaneously improving accuracy and efficiency. An experimental study on 9 real-world datasets demonstrates that Wolverine is capable of accelerating the deletion throughput by up to 11X and achieving more stable recall during updates compared to the state-of-the-art dynamic ANN search method.

Time series anomaly detection (TSAD) plays an important role in many domains such as finance, transportation, and healthcare. With the ongoing instrumentation of reality, more time series data will be available, leading also to growing demands for TSAD. While many TSAD methods already exist, new and better methods are still desirable. However, effective progress hinges on the availability of reliable means of evaluating new methods and comparing them with existing methods. We address deficiencies in current evaluation procedures related to datasets and experimental settings and protocols. Specifically, we propose a new time series anomaly detection benchmark, called TAB. First, TAB encompasses 29 public multivariate datasets and 1,635 univariate time series from different domains to facilitate more comprehensive evaluations on diverse datasets. Second, TAB covers a variety of TSAD methods, including Non-learning, Machine learning, Deep learning, LLM-based, and Time-series pre-trained methods. Third, TAB features a unified and automated evaluation pipeline that enables fair and easy evaluation of TSAD methods. Finally, we employ TAB to evaluate existing TSAD methods and report on the outcomes, thereby offering a deeper insight into the performance of these methods.

Cardinality estimation is a fundamental functionality in database systems. Most existing cardinality estimators focus on handling predicates over numeric or categorical data. They have largely omitted an important data type, set-valued data, which frequently occur in contemporary applications such as information retrieval and recommender systems. The few existing estimators for such data either favor high-frequency elements or rely on a partial independence assumption , which limits their practical applicability. We propose ACE, an Attention-based Cardinality Estimator for estimating the cardinality of queries over set-valued data. We first design a distillation-based data encoder to condense the dataset into a compact matrix. We then design an attention-based query analyzer to capture correlations among query elements. To handle variable-sized queries, a pooling module is introduced, followed by a regression model (MLP) to generate final cardinality estimates. We evaluate ACE on three datasets with varying query element distributions, demonstrating that ACE outperforms the state-of-the-art competitors in terms of both accuracy and efficiency.

Decentralized Knowledge Graph (DKG) platforms allow the sharing of knowledge with multiple owners. While data owners can share their data with others by encrypting their data before sharing it, this naïve approach prevents data encrypted by different owners from being queried together, as it compromises query verifiability, an essential DKG platform feature. We propose Pistis, the first DKG platform capable of preserving ownership while also enabling verifiable SPARQL queries. Two novel techniques facilitate this: owner-managed end-to-end encryption and collaborative query verification. In Pistis, data owners thus encrypt their data individually and collaborate to construct an authenticated data structure (ADS) with a global key by means of secret sharing and secure multi-party computation. Then, by indexing KG data as ciphertext over the ADS, Pistis offers a cryptographic scheme called VO-SPARQL that facilitates verifiable queries on encrypted KG data with multiple owners. Pistis provides succinct proofs for two-stage SPARQL queries, including subgraph queries based on the ADS and aggregation on encrypted intermediate results based on a key-aggregate cryptographic primitive. A theoretical analysis and an empirical study provide detailed insight into the performance of Pistis while offering provable security.

Accurate time series forecasting is crucial for optimizing resource allocation, industrial production, and urban management, particularly with the growth of cyber-physical and IoT systems. However, limited training sample availability in fields like physics and biology poses significant challenges. Existing models struggle to capture long-term dependencies and to model diverse meta-knowledge explicitly in few-shot scenarios. To address these issues, we propose MetaGP, a meta-learning-based Gaussian process latent variable model that uses a Gaussian process kernel function to capture long-term dependencies and to maintain strong correlations in time series. We also introduce Kernel Association Search (KAS) as a novel meta-learning component to explicitly model meta-knowledge, thereby enhancing both interpretability and prediction accuracy. We study MetaGP on simulated and real-world few-shot datasets, showing that it is capable of state-of-the-art prediction accuracy. We also find that MetaGP can capture long-term dependencies and can model meta-knowledge, thereby providing valuable insights into complex time series patterns.

In large venues like shopping malls and airports, knowledge on the indoor populations fuels applications such as business analytics, venue management, and safety control. In this work, we provide means of modeling populations in partitions of indoor space offline and of monitoring indoor populations continuously, by using indoor positioning data. However, the low-sampling rates of indoor positioning render the data temporally and spatially sparse, which in turn renders the offline capture of indoor populations challenging. It is even more challenging to continuously monitor indoor populations, as positioning data may be missing or not ready yet at the current moment. To address these challenges, we first enable probabilistic modeling of populations in indoor space partitions as Normal distributions. Based on that, we propose two learning-based estimators for on-the-fly prediction of population distributions. Leveraging the prediction-based schemes, we provide a unified continuous query processing framework for a type of query that enables continuous monitoring of populated partitions. The framework encompasses caching and result validity mechanisms to reduce cost and maintain monitoring effectiveness. Extensive experiments on two real data sets show that the proposed estimators are able to outperform the state-of-the-art alternatives and that the query processing framework is effective and efficient.

Vehicle movement is frequently captured in the form of GPS trajectories, i.e., sequences of timestamped GPS locations. Such data is widely used for various tasks such as travel-time estimation, trajectory recovery, and trajectory prediction. A universal vehicle trajectory model could be applied to different tasks, removing the need to maintain multiple specialized models, thereby reducing computational and storage costs. However, creating such a model is challenging when the integrity of trajectory features is compromised, i.e., in scenarios where only partial features are available or the trajectories are sparse. To address these challenges, we propose the Universal Vehicle Trajectory Model (UVTM), which can effectively adapt to different tasks without excessive retraining. UVTM incorporates two specialized designs. First, it divides trajectory features into three distinct domains. Each domain can be masked and generated independently to accommodate tasks with only partially available features. Second, UVTM is pre-trained by reconstructing dense, feature-complete trajectories from sparse, feature-incomplete counterparts, enabling strong performance even when the integrity of trajectory features is compromised. Experiments involving four representative trajectory-related tasks on three real-world vehicle trajectory datasets provide insight into the performance of UVTM and offer evidence that it is capable of meeting its objectives.

Spatiotemporal trajectories are sequences of timestamped locations, which enable a variety of analyses that in turn enable important real-world applications. It is common to map trajectories to vectors, called embeddings, before subsequent analyses. Thus, the qualities of embeddings are very important. Methods for pre-training embeddings, which leverage unlabeled trajectories for training universal embeddings, have shown promising applicability across different tasks, thus attracting considerable interest. However, research progress on this topic faces two key challenges: a lack of a comprehensive overview of existing methods, resulting in several related methods not being well-recognized, and the absence of a unified pipeline, complicating the development of new methods and the analysis of methods. We present UniTE, a survey and a unified pipeline for this domain. In doing so, we present a comprehensive list of existing methods for pre-training trajectory embeddings, which includes methods that either explicitly or implicitly employ pre-training techniques. Further, we present a unified and modular pipeline with publicly available underlying code, simplifying the process of constructing and evaluating methods for pre-training trajectory embeddings. Additionally, we contribute a selection of experimental results using the proposed pipeline on real-world datasets.

The widespread deployment of wireless and mobile devices results in a proliferation of decentralized spatio-temporal data. Many recent proposals that target deep learning for spatio-temporal prediction assume that all data is available at a central location and suffers from so-called catastrophic forgetting, where previously learned knowledge is entirely forgotten when new data arrives. Such proposals may face data privacy concerns and may experience deteriorating prediction performance when applied in decentralized settings where data streams into the system. To bridge the gap between decentralized training and spatio-temporal prediction on streaming data, we propose a unified federated continuous learning framework, which uses a horizontal federated learning mechanism for protecting data privacy and includes a global replay buffer with synthetic spatio-temporal data generated by the previously learned global model. For each client, we fuse the current training data with synthetic spatio-temporal data using a spatio-temporal mixup mechanism to preserve historical knowledge effectively, thus avoiding catastrophic forgetting. To enable holistic representation preservation, the local models at clients each integrates a general spatio-temporal autoencoder with a spatio-temporal simple siamese network that aims to ensure prediction accuracy and avoid holistic feature loss. Extensive experiments on real data offer insight into the effectiveness of the proposed framework.

Multivariate Time Series (MTS) analysis is crucial to understanding and managing complex systems, such as traffic and energy systems, and a variety of approaches to MTS forecasting have been proposed recently. However, we often observe inconsistent or seemingly contradictory performance findings across different studies. This hinders our understanding of the merits of different approaches and slows down progress. We address the need for means of assessing MTS forecasting proposals reliably and fairly, in turn enabling better exploitation of MTS as seen in different applications. Specifically, we first propose BasicTS+, a benchmark designed to enable fair, comprehensive, and reproducible comparison of MTS forecasting solutions. BasicTS+ establishes a unified training pipeline and reasonable settings, enabling an unbiased evaluation. Second, we identify the heterogeneity across different MTS as an important consideration and enable classification of MTS based on their temporal and spatial characteristics. Disregarding this heterogeneity is a prime reason for difficulties in selecting the most promising technical directions. Third, we apply BasicTS+ along with rich datasets to assess the capabilities of more than 30 MTS forecasting solutions. This provides readers with an overall picture of the cutting-edge research on MTS forecasting.

The booming of computer graphics technology facilitates the growing use of terrain data. Notably, shortest path querying on a terrain surface is central in a range of applications and has received substantial attention from the database community. Despite this, computing the shortest paths on-the-fly on a terrain surface remains very expensive, and all existing oracle-based algorithms are only efficient when the terrain surface is fixed. They rely on large data structures that must be re-constructed from scratch when updates to the terrain surface occur, which is very time-consuming. To advance the state-of-the-art, we propose an efficiently updatable$(1+\epsilon )$-approximate shortest path oracle for a set ofPoints-Of-Interests(POIs) on an updated terrain surface, and it can be easily adapted to the case if POIs are not given as input. Our experiments show that when POIs are given (resp. not given), our oracle is up to 88 times, 12 times, and 3 times (resp. 15 times, 50 times, and 100 times) better than the best-known oracle on terrain surfaces in terms of the oracle update time, output size, and shortest path query.

Abstract We provide efficient support for applications that aim to continuously find pairs of similar sets in rapid streams, such as Twitter streams that emit tweets as sets of words. Using a sliding window model, the top-k result changes as new sets enter the window or existing ones leave the window. Specifically, when a set arrives, it may form a new top-k result pair with any set already in the window. When a set leaves the window, all its pairings in the top-k result must be replaced with other pairs. It is therefore not sufficient to maintain the k most similar pairs since less similar pairs may become top-k pairs later. We propose SWOOP, a highly scalable stream join algorithm. Novel indexing techniques and sophisticated filters efficiently prune obsolete pairs as new sets enter the window. SWOOP incrementally maintains a provably minimal stock of similar pairs to update the top-k result at any time. Empirical studies confirm that SWOOP is able to support stream rates that are orders of magnitude faster than the rates supported by existing approaches.

The widespread deployment of wireless and mobile devices results in a proliferation of spatio-temporal data that is used in applications, e.g., traffic prediction, human mobility mining, and air quality prediction, where spatio-temporal prediction is often essential to enable safety, predictability, or reliability. Many recent proposals that target deep learning for spatio-temporal prediction suffer from so-called catastrophic forgetting, where previously learned knowledge is entirely forgotten when new data arrives. Such proposals may experience deteriorating prediction performance when applied in settings where data streams into the system. To enable spatio-temporal prediction on streaming data, we propose a unified replay- based continuous learning framework. The framework includes a replay buffer of previously learned samples that are fused with training data using a spatio-temporal mixup mechanism in order to preserve historical knowledge effectively, thus avoiding catastrophic forgetting. To enable holistic representation preservation, the framework also integrates a general spatio-temporal autoencoder with a carefully designed spatio-temporal simple siamese (STSimSiam) network that aims to ensure prediction accuracy and avoid holistic feature loss by means of mutual information maximization. The framework further encompasses five spatio-temporal data augmentation methods to enhance the performance of STSimSiam. Extensive experiments on real data offer insight into the effectiveness of the proposed framework.

The unprecedented availability of new types of data coupled with the invention of new technologies combine to enable entirely new or higher-resolution services that in turn enable more rational and data-driven processes. We consider the overall process of vehicular transportation and, more specifically, the process of deciding which route to follow when having to reach a destination. Early solutions modeled a road work as a graph, used sparse in-road sensor data to assign weights to graph edges, and then applied improved versions of Dijkstra's algorithm to find routes with the lowest sums of edge weights. Since then, massive vehicle trajectory data has become available. When coupled with new technologies, this data enables entirely new and higher-resolution routing services that in turn enable better routing. For more than a decade, the authors have engaged in research aimed at exploiting trajectory data to enable better routing. The resulting technologies were developed outside a DBMS. Here, we cover aspects of this research. Further, we challenge the community to develop DBMS support for these and other aspects of routing.

Spatio-temporal forecasting of future values of spatially correlated time series is important across many cyber-physical systems (CPS). Recent studies offer evidence that the use of graph neural networks to capture latent correlations between time series holds a potential for enhanced forecasting. However, most existing methods rely on predefined or self-learning graphs, which are either static or unintentionally dynamic, and thus cannot model the time-varying correlations that exhibit trends and periodicities caused by the regularity of the underlying processes in CPS. To tackle such limitation, we propose Time-aware Graph Structure Learning (TagSL), which extracts time-aware correlations among time series by measuring the interaction of node and time representations in high-dimensional spaces. Notably, we introduce time discrepancy learning that utilizes contrastive learning with distance-based regularization terms to constrain learned spatial correlations to a trend sequence. Additionally, we propose a periodic discriminant function to enable the capture of periodic changes from the state of nodes. Next, we present a Graph Convolution-based Gated Recurrent Unit (GCGRU) that jointly captures spatial and temporal dependencies while learning time-aware and node-specific patterns. Finally, we introduce a unified framework named Time-aware Graph Convolutional Recurrent Network (TGCRN), combining TagSL, and GCGRU in an encoder-decoder architecture for multi-step spatiotemporal forecasting. We report on experiments with TGCRN and popular existing approaches on five real-world datasets, thus providing evidence that TGCRN is capable of advancing the state-of-the-art. We also cover a detailed ablation study and visualization analysis, offering detailed insight into the effectiveness of time-aware structure learning.

Increasing and massive volumes of trajectory data are being accumulated that may serve a variety of applications, such as mining popular routes or identifying ridesharing candidates. As storing and querying massive trajectory data is costly, trajectory simplification techniques have been introduced that intuitively aim to reduce the sizes of trajectories, thus reducing storage and speeding up querying, while preserving as much information as possible. Existing techniques rely mainly on hand-crafted error measures when deciding which point to drop when simplifying a trajectory. While the hope may be that such simplification affects the subsequent usability of the data only minimally, the usability of the simplified data remains largely unexplored. Instead of using error measures that indirectly may to some extent yield simplified trajectories with high usability, we adopt a direct approach to simplification and present the first study of query accuracy driven trajectory simplification, where the direct objective is to achieve a simplified trajectory database that preserves the query accuracy of the original database as much as possible. Specifically, we propose a multi-agent reinforcement learning based solution with two agents working cooperatively to collectively simplify trajectories in a database while optimizing query usability. Extensive experiments on four real-world trajectory datasets show that the solution is capable of consistently outperforming baseline solutions over various query types and dynamics.

Vehicular traffic congestion is a recurring and widespread societal phenomenon. Since drivers increasingly rely on routing services, we consider how to enhance such services to provide routes that mitigate congestion. Specifically, we propose the Congestion-mitigating Spatiotemporal Routing (CSR) problem that considers the congestion caused by vehicles following the routes provided. This problem is challenging because vehicles that follow recommended routes appear on different road segments at different times. We propose two solutions, Spatiotemporal Oblivious Routing (SOR) and Spatiotemporal Routing with History (SRH), which return routes based on the current and anticipated future traffic statuses, respectively, while offering theoretical guarantees. We also propose an update procedure for handling traffic dynamics. Extensive evaluations on real data provide insight into the properties of the solutions, indicating that SRH can reduce the number of vehicles on the most congested road segments by nearly 33 % and can process a query in less than 10 ms.

With the widespread deployment of location-aware mobile devices, a mass of trajectory data is being generated and collected. Mining co-movement patterns of people and vehicles from streaming and massive trajectory data has attracted much attention due to its wide applications in various fields. As a typical co-movement pattern, convoys describe objects moving together in consecutive timestamps. There are two challenges for efficient distributed convoy mining: object clustering and workload balancing. Clustering objects in each time snapshot is a time-consuming operation. In addition, on the basis of practical application scenarios, load balancing is an important consideration for distributed algorithms. To tackle the above challenges, we propose a novel method for distributed convoy mining via sliding window-based indexing and sub-track partitioning, abbreviated SWISP. We offer three major advancements. First, we develop a grid-based DBSCAN clustering algorithm named Grid-DBSCAN for distributed scenarios. It avoids the exhaustive calculation of pairwise distances for neighborhood search and thus improves computational efficiency in the clustering stage. Second, we propose a sliding window-based indexing scheme to filter out sub-tracks with less than$k$consecutive time snapshots, significantly reducing the number of candidate sub-tracks for convoy mining. Third, we develop a distributed convoy mining algorithm based on sub-track partitioning. It exploits both temporal and spatial information of sub-tracks for data partitioning and solves the data skewness problem caused by uneven data distributions. We conduct extensive experiments on four real-world datasets. The experimental results show that our distributed algorithm can handle large-scale trajectory data and is more efficient than state-of-the-art approaches.

GPUs are being applied widely to batch workloads that benefit from the parallel processing capabilities of GPUs. To enable the processing of concurrent batch-based transactions on GPUs, existing systems build dependency graphs during a pre-execution phase to manage read and write operations. However, as dependency-graph maintenance introduces a sub-stantial overhead, there is a need for more efficient transaction support to exploit the power of GPUs more fully for transaction processing. This paper proposes LTPG, a novel GPU-enabled database system that offers increased versatility and efficiency by eliminating the need for predefined read/write-sets. LTPG employs deterministic optimistic concurrency control to ensure correct transaction execution, thus avoiding the maintenance of dependency graphs. The proposed concurrency control simpli-fies transaction processing workflows and avoids the overhead associated with managing dependency graphs, thus resulting in improved efficiency. LTPG divides a workflow into three stages: execution, conflict detection, and write-back, leveraging the parallelism of GPUs. Moreover, several additional optimization strategies are adopted to improve system performance. Experiments with real-world workloads from two benchmarks verify LTPG can achieve effective improvement in the throughput and latency compared to the leading baselines.

Graph augmentation with contrastive learning has gained significant attention in the field of recommendation systems due to its ability to learn expressive user representations, even when labeled data is limited. However, directly applying existing GCL models to real-world recommendation environments poses challenges. There are two primary issues to address. Firstly, the lack of consideration for data noise in contrastive learning can result in noisy self-supervised signals, leading to degraded performance. Secondly, many existing GCL approaches rely on graph neural network (GNN) architectures, which can suffer from over-smoothing problems due to non-adaptive message passing. To address these challenges, we propose a principled framework called GraphAug. This framework introduces a robust data augmentor that generates denoised self-supervised signals, enhancing recommender systems. The GraphAug framework incorporates a graph information bottleneck (GIB)-regularized augmentation paradigm, which automatically distills informative self-supervision information and adaptively adjusts contrastive view generation. Through rigorous experimentation on real-world datasets, we thoroughly assessed the performance of our novel GraphAug model. The outcomes consistently unveil its superiority over existing baseline methods. The source code for our model is publicly available at: https://github.com/HKUDS/GraphAug.

Imputation of Correlated Time Series (CTS) is essential in data preprocessing for many tasks, particularly when sensor data is often incomplete. Deep learning has enabled sophisticated models that improve CTS imputation by capturing temporal and spatial patterns. However, deep models often incur considerable consumption of computational resources and thus cannot be deployed in resource-limited settings. This paper presents ReCTSi (Resource-efficient CTS imputation), a method that adopts a new architecture for decoupled pattern learning in two phases: (1) the Persistent Pattern Extraction phase utilizes a multi-view learnable codebook mechanism to identify and archive persistent patterns common across different time series, enabling rapid pattern retrieval during inference. (2) the Transient Pattern Adaptation phase introduces completeness-aware attention modules that allocate attention to the complete and hence more reliable data segments. Extensive experimental results show that ReCTSi achieves state-of-the-art imputation accuracy while consuming much fewer computational resources than the leading existing model, consuming only 0.004% of the FLOPs for inference compared to its closest competitor. The blend of high accuracy and very low resource consumption makes ReCTSi the currently best method for resource-limited scenarios. The related code is available at https://github.com/ryanlaics/RECTSI.

A trajectory is a sequence of timestamped point locations that captures the movement of an object such as a vehicle. Such trajectories encode complex spatial and temporal patterns and provide rich information about object mobility and the underlying infrastructures, typically road networks, within which the movements occur. A trajectory dataset is evolving when new trajectories are included continuously. The ability to detect anomalous trajectories in online fashion in this setting is fundamental and challenging functionality that has many applications, e.g., location-based services. State-of-the-art solutions determine anomalies based on the shapes or routes of trajectories, ignoring potential anomalies caused by different sampling rates or time offsets. We propose a multi-scale model, termed MST-OATD, for anomalous streaming trajectory detection that considers both the spatial and temporal aspects of trajectories. The model's multi-scale capabilities aim to enable extraction of trajectory features at multiple scales. In addition, to improve model evolvability and to contend with changes in trajectory patterns, the model is equipped with a learned ranking model that updates the training set as new trajectories are included. Experiments on real datasets offer evidence that the model can outperform state-of-the-art solutions and is capable of real-time anomaly detection. Further, the learned ranking model achieves promising results when updating the training set with newly arrived trajectories.

Cyber-physical system sensors emit multivariate time series (MTS) that monitor physical system processes. Such time series generally capture unknown numbers of states, each with a different duration, that correspond to specific conditions, e.g., "walking" or "running" in human-activity monitoring. Unsupervised identification of such states facilitates storage and processing in subsequent data analyses, as well as enhances result interpretability. Existing state-detection proposals face three challenges. First, they introduce substantial computational overhead, rendering them impractical in resourceconstrained or streaming settings. Second, although state-of-the-art (SOTA) proposals employ contrastive learning for representation, insufficient attention to false negatives hampers model convergence and accuracy. Third, SOTA proposals predominantly only emphasize offline non-streaming deployment, we highlight an urgent need to optimize online streaming scenarios. We propose E2Usd that enables efficient-yet-accurate unsupervised MTS state detection. E2Usd exploits a Fast Fourier Transform-based Time Series Compressor (fftCompress) and a Decomposed Dual-view Embedding Module (ddEM) that together encode input MTSs at low computational overhead. Additionally, we propose a False Negative Cancellation Contrastive Learning method (fnccLearning) to counteract the effects of false negatives and to achieve more cluster-friendly embedding spaces. To reduce computational overhead further in streaming settings, we introduce Adaptive Threshold Detection (adaTD). Comprehensive experiments with six baselines and six datasets offer evidence that E2Usd is capable of SOTA accuracy at significantly reduced computational overhead. Our code is available at https://github.com/AI4CTS/E2Usd.

Range-filtering approximate nearest neighbor (RFANN) search is attracting increasing attention in academia and industry. Given a set of data objects, each being a pair of a high-dimensional vector and a numeric value, an RFANN query with a vector and a numeric range as parameters returns the data object whose numeric value is in the query range and whose vector is nearest to the query vector. To process this query, a recent study proposes to build O(n 2 ) dedicated graph-based indexes for all possible query ranges to enable efficient processing on a database of n objects. As storing all these indexes is prohibitively expensive, the study constructs compressed indexes instead, which reduces the memory consumption considerably. However, this incurs suboptimal performance because the compression is lossy. In this study, instead of materializing a compressed index for every possible query range in preparation for querying, we materialize graph-based indexes, called elemental graphs, for a moderate number of ranges. We then provide an effective and efficient algorithm that during querying can construct an index for any query range using the elemental graphs. We prove that the time needed to construct such an index is low. We also cover an experimental study on real-world datasets that provides evidence that the materialized elemental graphs only consume moderate space and that the proposed method is capable of superior and stable query performance across different query workloads.

Time series compression encodes the information in a time-ordered sequence of data points into fewer bits, thereby reducing storage costs and possibly other costs. Compression methods are either general or XOR-based. General compression methods are time-consuming and are not suitable in streaming scenarios, while XOR-based methods are unable to consistently maintain high compression ratios. Further, existing methods compress the integer and decimal parts of floating-point values as a whole, thus disregarding the different characteristics of the two parts. We propose Camel , a new compression method for floating-point time series with the goal of advancing the compression ratios and efficiency achievable. Camel compresses the integer and decimal parts of the double-precision floating-point numbers in time series separately; and instead of performing XOR operations on values using their previous value, Camel identifies values that enable higher compression ratios. Camel also includes means of indexing compressed data, thereby making it possible to query compressed data efficiently. We report on an empirical study of Camel and 11 lossless and 6 lossy compression methods on 22 public datasets and three industrial datasets from AliCloud. The study offers evidence that Camel is capable of outperforming existing methods in terms of both compression ratio and efficiency and is capable of excellent compression performance on both time series and non-time series data.

We are witnessing an increasing availability of streaming data that may contain valuable information on the underlying processes. It is thus attractive to be able to deploy machine learning models, e.g., for classification, on edge devices near sensors such that decisions can be made instantaneously, rather than first having to transmit incoming data to servers. To enable deployment on edge devices with limited storage and computational capabilities, the full-precision parameters in standard models can be quantized to use fewer bits. The resulting quantized models are then calibrated using back-propagation with the full training data to ensure accuracy. This one-time calibration works for deployments in static environments. However, model deployment in dynamic edge environments call for continual calibration to adaptively adjust quantized models to fit new incoming data, which may have different distributions with the original training data. The first difficulty in enabling continual calibration on the edge is that the full training data may be too large and thus cannot be assumed to be always available on edge devices. The second difficulty is that the use of back-propagation on the edge for repeated calibration is too expensive. We propose QCore to enable continual calibration on the edge. First, it compresses the full training data into a small subset to enable effective calibration of quantized models with different bit-widths. We also propose means of updating the subset when new streaming data arrives to reflect changes in the environment, while not forgetting earlier training data. Second, we propose a small bit-flipping network that works with the subset to update quantized model parameters, thus enabling efficient continual calibration without back-propagation. An experimental study, conducted with real-world data in a continual learning setting, offers insight into the properties of QCore and shows that it is capable of outperforming strong baseline methods.

Trajectories that capture object movement have numerous applications, in which similarity computation between trajectories often plays a key role. Traditionally, trajectory similarity is quantified by means of non-learned measures, e.g., Hausdorff, that operate directly on the trajectories. Recent studies exploit deep learning to map trajectories tod-dimensional vectors, called embeddings. Then, some distance measure, e.g., Manhattan, is applied to the embeddings to quantify trajectory similarity. The resulting similarities are inaccurate: they only approximate the similarities obtained using the non-learned measures. As embedding distance computation is efficient, focus has been on obtaining embeddings of high accuracy. Adopting an efficiency perspective, we analyze the time complexities of both the non-learned and the learning-based approaches, finding that the time complexities of the former approaches are not necessarily higher. Through extensive experiments on open datasets, we find that only a few learning-based approaches can deliver the promised higher efficiency, when the embeddings can be pre-computed, while non-learned approaches are more efficient for one-off computations. Among the learning-based approaches, the self-attention-based ones are the fastest and the most accurate. These results have implications for the use of trajectory similarity approaches given different application requirements.

Accurate long-term forecasting from multivariate time series has important real-world applications. However, achieving this so is challenging. Thus, analyses reveal that time series that span long durations often exhibit dynamic and disrupted correlations. State-of-the-art methods employ attention mechanisms to capture dynamic correlations, but they often do not contend well with disrupted correlations, which reduces prediction accuracy. We introduce local and global information concepts and then leverage these in a Memory Guided Transformer, called the Memformer. By integrating patch-wise recurrent graph learning and global attention, the Memformer aims to capture dynamic correlations and take disrupted correlations into account. We also integrate a so-called Alternating Memory Enhancer into the Memformer to capture correlations between local and global information. We report on experiments that offer insight into the effectiveness of the Memformer at capturing dynamic correlations and its robustness to disrupted correlations. The experiments offer evidence that the new method is capable of advancing the state-of-the-art in forecasting accuracy on real-world datasets.

The availability of massive vehicle trajectory data enables the modeling of road-network constrained movement as travel-cost distributions rather than just single-valued costs, thereby capturing the inherent uncertainty of movement and enabling improved routing quality. Thus, stochastic routing has been studied extensively in the edge-centric model, where such costs are assigned to the edges in a graph representation of a road network. However, as this model still disregards important information in trajectories and fails to capture dependencies among cost distributions, a path-centric model, where costs are assigned to paths, has been proposed that captures dependencies better and provides an improved foundation for routing. Unfortunately, when applied in this model, existing routing algorithms are inefficient due to two shortcomings that we eliminate. First, when exploring candidate paths, existing algorithms only consider the costs of candidate paths from the source to intermediate vertices, while disregarding the costs of travel from the intermediate vertices to the destination, causing many noncompetitive paths to be explored. We propose two heuristics for estimating the cost from an intermediate vertex to the destination, thus improving routing efficiency. Second, the edge-centric model relies on stochastic dominance-based pruning to improve efficiency. This pruning assumes that costs are independent and is therefore inapplicable in the path-centric model that takes dependencies into account. We introduce a notion of virtual path that effectively enables stochastic dominance-based pruning in the path-based model, thus further improving efficiency. Empirical studies using two real-world trajectory sets offer insight into the properties of the proposed solution, indicating that it enables efficient stochastic routing in the path-centric model.

Due to the global trend towards urbanization, people increasingly move to and live in cities that then continue to grow. Traffic forecasting plays an important role in the intelligent transportation systems of cities as well as in spatio-temporal data mining. State-of-the-art forecasting is achieved by deep-learning approaches due to their ability to contend with complex spatio-temporal dynamics. However, existing methods assume the input is fixed-topology road networks and static traffic time series. These assumptions fail to align with urbanization, where time series are collected continuously and road networks evolve over time. In such settings, deep-learning models require frequent re-initialization and re-training, imposing high computational costs. To enable much more efficient training without jeopardizing model accuracy, we propose the Topological Evolution-aware Framework (TEAM) for traffic forecasting that incorporates convolution and attention. This combination of mechanisms enables better adaptation to newly collected time series while being able to maintain learned knowledge from old time series. TEAM features a continual learning module based on the Wasserstein metric that acts as a buffer that can identify the most stable and the most changing network nodes. Then, only data related to stable nodes is employed for re-training when consolidating a model. Further, only data of new nodes and their adjacent nodes as well as data pertaining to changing nodes are used to re-train the model. Empirical studies with two real-world traffic datasets offer evidence that TEAM is capable of much lower re-training costs than existing methods are, without jeopardizing forecasting accuracy.

A space-filling curve (SFC) maps points in a multi-dimensional space to one-dimensional points by discretizing the multi-dimensional space into cells and imposing a linear order on the cells. This way, an SFC enables computing a one-dimensional layout for multidimensional data storage and retrieval. Choosing an appropriate SFC is crucial, as different SFCs have different effects on query performance. Currently, there are two primary strategies: 1) deterministic schemes, which are computationally efficient but often yield suboptimal query performance, and 2) dynamic schemes, which consider a broad range of candidate SFCs based on cost functions but incur significant computational overhead. Despite these strategies, existing methods cannot efficiently measure the effectiveness of SFCs under heavy query workloads and numerous SFC options. To address this problem, we propose means of constant-time cost estimations that can enhance existing SFC selection algorithms, enabling them to learn more effective SFCs. Additionally, we propose an SFC learning method that leverages reinforcement learning and our cost estimations to choose an SFC pattern efficiently. Experimental studies offer evidence of the effectiveness and efficiency of the proposed means of cost estimation and SFC learning.

The expanding instrumentation of processes throughout society with sensors yields a proliferation of time series data that may in turn enable important applications, e.g., related to transportation infrastructures or power grids. Machine-learning based methods are increasingly being used to extract value from such data. We provide means of reducing the resulting considerable computational and data storage costs. We achieve this by providing means of condensing large time series datasets such that models trained on the condensed data achieve performance comparable to those trained on the original, large data. Specifically, we propose a time series dataset condensation framework, TimeDC, that employs two-fold modal matching, encompassing frequency matching and training trajectory matching. Thus, TimeDC performs time series feature extraction and decomposition-driven frequency matching to preserve complex temporal dependencies in the reduced time series. Further, TimeDC employs curriculum training trajectory matching to ensure effective and generalized time series dataset condensation. To avoid memory overflow and to reduce the cost of dataset condensation, the framework includes an expert buffer storing pre-computed expert trajectories. Extensive experiments on real data offer insight into the effectiveness and efficiency of the proposed solutions.

With the continued migration of storage to cloud database systems, the impact of slow queries in such systems on services and user experience is increasing. Root-cause diagnosis plays an indispensable role in facilitating slow-query detection and revision. This paper proposes a method capable of both identifying possible root cause types for slow queries and ranking these according to their potential for accelerating slow queries. This enables prioritizing root causes with the highest impact, in turn improving slow-query revision effectiveness. To enable more accurate and detailed diagnoses, we propose the multimodal Ranking for the Root Causes of slow queries (RCRank) framework, which formulates root cause analysis as a multimodal machine learning problem and leverages multimodal information from query statements, execution plans, execution logs, and key performance indicators. To obtain expressive embeddings from its heterogeneous multimodal input, RCRank integrates self-supervised pre-training that enhances cross-modal alignment and task relevance. Next, the framework integrates root-cause-adaptive cross Transformers that enable adaptive fusion of multimodal features with varying characteristics. Finally, the framework offers a unified model that features an impact-aware training objective for identifying and ranking root causes. We report on experiments on real and synthetic datasets, finding that RCRank is capable of consistently outperforming the state-of-the-art methods at root cause identification and ranking according to a range of metrics.

Time series are generated in diverse domains such as economic, traffic, health, and energy, where forecasting of future values has numerous important applications. Not surprisingly, many forecasting methods are being proposed. To ensure progress, it is essential to be able to study and compare such methods empirically in a comprehensive and reliable manner. To achieve this, we propose TFB, an automated benchmark for Time Series Forecasting (TSF) methods. TFB advances the state-of-the-art by addressing shortcomings related to datasets, comparison methods, and evaluation pipelines: 1) insufficient coverage of data domains, 2) stereotype bias against traditional methods, and 3) inconsistent and inflexible pipelines. To achieve better domain coverage, we include datasets from 10 different domains : traffic, electricity, energy, the environment, nature, economic, stock markets, banking, health, and the web. We also provide a time series characterization to ensure that the selected datasets are comprehensive. To remove biases against some methods, we include a diverse range of methods, including statistical learning, machine learning, and deep learning methods, and we also support a variety of evaluation strategies and metrics to ensure a more comprehensive evaluations of different methods. To support the integration of different methods into the benchmark and enable fair comparisons, TFB features a flexible and scalable pipeline that eliminates biases. Next, we employ TFB to perform a thorough evaluation of 21 Univariate Time Series Forecasting (UTSF) methods on 8,068 univariate time series and 14 Multivariate Time Series Forecasting (MTSF) methods on 25 datasets. The results offer a deeper understanding of the forecasting methods, allowing us to better select the ones that are most suitable for particular datasets and settings. Overall, TFB and this evaluation provide researchers with improved means of designing new TSF methods.

As large volumes of trajectory data accumulate, simplifying trajectories to reduce storage and querying costs is increasingly studied. Existing proposals face three main problems. First, they require numerous iterations to decide which GPS points to delete. Second, they focus only on the relationships between neighboring points (local information) while neglecting the overall structure (global information), reducing the global similarity between the simplified and original trajectories and making it difficult to maintain consistency in query results, especially for similarity-based queries. Finally, they fail to differentiate the importance of points with similar features, leading to suboptimal selection of points to retain the original trajectory information. We propose MLSimp, a novel Mutual Learning query-driven trajectory simplification framework that integrates two distinct models: GNN-TS, based on graph neural networks, and Diff-TS, based on diffusion models. GNN-TS evaluates the importance of a point according to its globality, capturing its correlation with the entire trajectory, and its uniqueness, capturing its differences from neighboring points. It also incorporates attention mechanisms in the GNN layers, enabling simultaneous data integration from all points within the same trajectory and refining representations, thus avoiding iterative processes. Diff-TS generates amplified signals to enable the retention of the most important points at low compression rates. Experiments involving eight baselines on three databases show that MLSimp reduces the simplification time by 42%--70% and improves query accuracy over simplified trajectories by up to 34.6%.

Dynamic Graph Neural Networks (DGNNs) have demonstrated exceptional performance at dynamic-graph analysis tasks. However, the costs exceed those incurred by other learning tasks, to the point where deployment on large-scale dynamic graphs is infeasible. Existing distributed frameworks that facilitate DGNN training are in their early stages and experience challenges such as communication bottlenecks, imbalanced workloads, and GPU memory overflow. We introduce DynaHB, a distributed framework for DGNN training using so-called Hybrid Batches. DynaHB reduces communication by means of vertex caching, and it ensures even data and workload distribution by means of load-aware vertex partitioning. DyanHB also features a novel hybrid-batch training mode that combines vertex-batch and snapshot-batch techniques, thereby reducing training time and GPU memory usage. Next, to further enhance the hybrid batch based approach, DynaHB integrates a reinforcement learning-based batch adjuster and a pipelined batch generator with a batch reservoir to reduce the cost of generating hybrid batches. Extensive experiments show that DynaHB is capable of up to a 93× and an average of 8.06× speedups over the state-of-the-art training framework.

Societal and industrial infrastructures and systems increasingly leverage sensors that emit correlated time series. Forecasting of future values of such time series based on recorded historical values has important benefits. Automatically designed models achieve higher accuracy than manually designed models. Given a forecasting task, which includes a dataset and a forecasting horizon, automated design methods automatically search for an optimal forecasting model for the task in a manually designed search space, and then train the identified model using the dataset to enable the forecasting. Existing automated methods face three challenges. First, the search space is constructed by human experts, rending the methods only semi-automated and yielding search spaces prone to subjective biases. Second, it is time consuming to search for an optimal model. Third, training the identified model for a new task is also costly. These challenges limit the practicability of automated methods in real-world settings. To contend with the challenges, we propose a fully automated and highly efficient correlated time series forecasting framework where the search and training can be done in minutes. The framework includes a data-driven, iterative strategy to automatically prune a large search space to obtain a high-quality search space for a new forecasting task. It includes a zero-shot search strategy to efficiently identify the optimal model in the customized search space. And it includes a fast parameter adaptation strategy to accelerate the training of the identified model. Experiments on seven benchmark datasets offer evidence that the framework is capable of state-of-the-art accuracy and is much more efficient than existing methods.

Trajectory representation learning (TRL) maps trajectories to vectors that can then be used for various downstream tasks, including trajectory similarity computation, trajectory classification, and travel-time estimation. However, existing TRL methods often produce vectors that, when used in downstream tasks, yield insufficiently accurate results. A key reason is that they fail to utilize the comprehensive information encompassed by trajectories. We propose a self-supervised TRL framework, called RED, which effectively exploits multiple types of trajectory information. Overall, RED adopts the Transformer as the backbone model and masks the constituting paths in trajectories to train a masked autoencoder (MAE). In particular, RED considers the moving patterns of trajectories by employing a R oad-aware masking strategy that retains key paths of trajectories during masking, thereby preserving crucial information of the trajectories. RED also adopts a spatial-temporal-user joint E mbedding scheme to encode comprehensive information when preparing the trajectories as model inputs. To conduct training, RED adopts D ual-objective task learning : the Transformer encoder predicts the next segment in a trajectory, and the Transformer decoder reconstructs the entire trajectory. RED also considers the spatial-temporal correlations of trajectories by modifying the attention mechanism of the Transformer. We compare RED with 9 state-of-the-art TRL methods for 4 downstream tasks on 3 real-world datasets, finding that RED can usually improve the accuracy of the best-performing baseline by over 5%.

With the widespread use of GPS-enabled devices and services, trajectory data fuels services in a variety of fields, such as transportation and smart cities. However, trajectory data often contains errors stemming from inaccurate GPS measurements, low sampling rates, and transmission interruptions, yielding low-quality trajectory data with negative effects on downstream services. Therefore, a crucial yet tedious endeavor is to assess the quality of trajectory data, serving as a guide for subsequent data cleaning and analyses. Despite some studies addressing general-purpose data quality assessment, no studies exist that are tailored specifically for trajectory data. To more effectively diagnose the quality of trajectory data, we propose T-Assess, an automated trajectory data quality assessment system. T-Assess is built on three fundamental principles: i) extensive coverage, ii) versatility, and iii) efficiency. To achieve comprehensive coverage, we propose assessment criteria spanning validity, completeness, consistency, and fairness. To provide high versatility, T-Assess supports both offline and online evaluations for full-batch trajectory datasets as well as real-time trajectory streams. In addition, we incorporate an evaluation optimization strategy to achieve assessment efficiency. Extensive experiments on four real-life benchmark datasets offer insight into the effectiveness of T-Assess at quantifying trajectory data quality beyond the capabilities of state-of-the-art data quality systems.

Correlated time series (CTS) forecasting is essential in many practical applications, such as traffic management and server load control. Various deep learning based solutions have been proposed to improve forecasting accuracy. However, while models have become increasingly computationally intensive, they struggle to improve accuracy. This study aims instead to enable more lightweight, accurate models suitable for resource-constrained devices. To achieve this goal, we characterize popular CTS forecasting models, yielding two observations for developing lightweight CTS forecasting. On this basis, we propose theLightCTSframework that adopts plain stacking of temporal and spatial operators instead of alternate stacking which is much more computationally expensive. Moreover,LightCTSfeatures light temporal and spatial operators, L-TCN and GL-Former, offering improved computational efficiency without compromising their feature extraction capabilities.LightCTSalso encompasses a last-shot compression scheme to reduce redundant temporal features and speed up subsequent computations. Next, we equipLightCTSwith two knowledge distillation modules,TafdandCaad, that result inLightCTS$^\star$retaining the original benefits ofLightCTS, while also being able to adapt to varying levels of ultra-constrained resources. Experimental studies offer detailed insight into these proposals and provide evidence that bothLightCTSandLightCTS$^\star$are capable of nearly state-of-the-art accuracy at substantially reduced computational costs.

Spatio-temporal trajectories provide valuable information about movement and travel behavior, enabling various downstream tasks that in turn power real-world applications. Learning trajectory embeddings can improve task performance but may incur high computational costs and face limited training data availability. Pre-training learns generic embeddings by means of specially constructed pretext tasks that enable learning from unlabeled data. Existing pre-training methods face (i) difficulties in learning general embeddings due to biases towards certain downstream tasks incurred by the pretext tasks, (ii) limitations in capturing both travel semantics and spatio-temporal correlations, and (iii) the complexity of long, irregularly sampled trajectories. To tackle these challenges, we propose Maximum Multi-view Trajectory Entropy Coding (MMTEC) for learning general and comprehensive trajectory embeddings. We introduce a pretext task that reduces biases in pre-trained trajectory embeddings, yielding embeddings that are useful for a wide variety of downstream tasks. We also propose an attention-based discrete encoder and a NeuralCDE-based continuous encoder that extract and represent travel behavior and continuous spatio-temporal correlations from trajectories in embeddings, respectively. Extensive experiments on two real-world datasets and three downstream tasks offer insight into the design properties of our proposal and indicate that it is capable of outperforming existing trajectory embedding methods.

Spatial Crowdsourcing (SC) is finding widespread application in today's online world. As we have transitioned from desktop crowdsourcing applications (e.g., Wikipedia) to SC applications (e.g., Uber), there is a sense that SC systems must not only provide effective task assignment but also need to ensure privacy. To achieve these often-conflicting objectives, we propose a framework, Task Assignment with Federated Preference Learning, that performs task assignment based on worker preferences while keeping the data decentralized and private in each platform center (e.g., each delivery center of an SC company). The framework includes a federated preference learning phase and a task assignment phase. Specifically, in the first phase, we build a local preference model for each platform center based on historical data. We provide means of horizontal federated learning that makes it possible to collaboratively train these local preference models under the orchestration of a central server. Specifically, we provide a practical method that accelerates federated preference learning based on stochastic controlled averaging and achieves low communication costs while considering data heterogeneity among clients. The task assignment phase aims to achieve effective and efficient task assignment by considering workers’ preferences. Extensive evaluations on real data offer insight into the effectiveness and efficiency of the paper's proposals.

Hypergraphs can model higher-order relationships among data objects that are found in applications such as social networks and bioinformatics. However, recent studies on hypergraph learning that extend graph convolutional networks to hypergraphs cannot learn effectively from features of unlabeled data. To such learning, we propose a contrastive hypergraph neural network, CHGNN, that exploits self-supervised contrastive learning techniques to learn from labeled and unlabeled data. First, CHGNN includes an adaptive hypergraph view generator that adopts an auto-augmentation strategy and learns a perturbed probability distribution of minimal sufficient views. Second, CHGNN encompasses an improved hypergraph encoder that considers hyperedge homogeneity to fuse information effectively. Third, CHGNN is equipped with a joint loss function that combines a similarity loss for the view generator, a node classification loss, and a hyperedge homogeneity loss to inject supervision signals. It also includes basic and cross-validation contrastive losses, associated with an enhanced contrastive loss training process. Experimental results on nine real datasets offer insight into the effectiveness of CHGNN, showing that it outperforms 19 competitors in terms of classification accuracy consistently.

Attributed graphs are graphs, where the vertices have attributes. Such graphs encompass, e.g., social network graph, citation graphs, and knowledge graphs, which have numerous real-world applications. Keyword-based search is a prominent and user-friendly way of querying attributed graphs. One widely used approach to keyword search adopts tree-based query semantics that relies on scoring functions that aggregate distances from a root to keyword-matched vertices. However, it is non-trivial to design scoring functions that capture different users’ keyword preferences. This study defines and solves the skyline KTree retrieval problem that combines keyword querying with skyline functionality on attributed graphs. The result of a skyline KTree query is independent of scoring functions. Hence, no matter which keywords are preferred, users can always find their favorite KTrees in a result. To enable efficient skyline KTree retrieval, we propose algorithm$\mathsf {FilterRefine}$that first identifies candidate results and then uses them for search space pruning. Computing distances between keywords and vertices is expensive and dominates the computational cost of$\mathsf {FilterRefine}$. Inspired by subspace skyline query techniques, we convert the skyline KTree retrieval problem into a multi-dimensional subspace skyline problem and propose algorithm$\mathsf {MultiDiSkylineOpt}$. This algorithm is able to reuse skylines in subspaces and uses bounds on all dimensions to accelerate distance computation. Experimental results on real datasets show that a baseline algorithm cannot report results within a 500 second cut-off time, while the proposed algorithms are able to compute results in reasonable time. In particular,$\mathsf {MultiDiSkylineOpt}$is able to efficiently retrieve skyline KTrees on large graphs with millions of nodes and hundreds of millions of edges.

Anomaly detection is one of the most significant tasks in multivariate time series analysis, while it remains challenging to model complex patterns for improving detection accuracy and to interpret the root causes of anomalies. However, existing studies either consider only the temporal dependencies, or simply reconstruct the original input for detection, both neglecting the hidden relationships among multivariate. We propose an adversarial graph neural network based anomaly detection model, called SGAT-AE, which consists of aSelf-learningGraphATtention network (SGAT), anAuto-Encoder (AE), and an adversarial training component. Specifically, SGAT is a prediction model that discovers the graph dependency relationships among multivariate and acts as a sample generator to confuse AE, while AE reconstructs the samples and acts as a discriminator that distinguishes a real sample from a generated one. A novel adversarial training between SGAT and AE is applied to amplify the errors of anomalies such that the prediction performance of SGAT is improved and the overfitting of AE is avoided. In addition, we aggregate the prediction error, the reconstruction error, and the adversarial error for anomaly detection, and develop a graph based anomaly interpretation method that locates the root causes from both local and global perspectives. Extensive experiments with five real-world data offer evidence that the proposed solution SGAT-AE is capable of achieving better performance when compared with the state-of-the-art proposals.

Abstract With the widespread use of networked and geo-positioned mobile devices, e.g., smartphones, Spatial Crowdsourcing (SC), which refers to the assignment of location-based tasks to moving workers, is drawing increasing attention. One of the critical issues in SC is task assignment that allocates tasks to appropriate workers. We propose and study a novel SC problem, namely Coalition-based Task Assignment (CTA), where the spatial tasks (e.g., home improvement and furniture installation) may require more than one worker (forming a coalition) to cooperate to maximize the overall rewards of workers. We design a greedy and an equilibrium-based CTA approach. The greedy approach forms a set of worker coalitions greedily for performing tasks and uses an acceptance probability to identify high-value task assignments. In the equilibrium-based approach, workers form coalitions in sequence and update their strategies (i.e., selecting a best-response task), to maximize their own utility (i.e., the reward of the coalition they belong to) until a Nash equilibrium is reached. Since the equilibrium obtained is not unique and optimal in terms of total rewards, we further propose a simulated annealing scheme to find a better Nash equilibrium. To achieve fair task assignments, we optimize the framework to distribute rewards fairly among workers in a coalition based on their marginal contributions and give workers who arrive first at the SC platform highest priority. Extensive experiments demonstrate the efficiency and effectiveness of the proposed methods on real and synthetic data.

Attributed graph clustering aims to partition the nodes in a graph into groups such that the nodes in the same group are close in terms of graph proximity and also have similar attribute values. Recently, deep learning methods have achieved state-of-the-art clustering performance. However, the effectiveness of existing methods remains unclear due to two reasons. First, the datasets used for evaluation do not support fully the goal of attributed graph clustering. The category labels of nodes are only relevant to node attributes, and nodes with the same category label are often distant in the graph. Second, existing methods for the attributed graph clustering are complex and consist of several components. There is lack of comparisons of methods composed of different components from existing methods. This study proposes six benchmark datasets that support better the goal of attributed graph clustering and reports the performance of existing representative methods. Given that existing methods leave room for improvement on the proposed benchmark datasets, we systematically analyze five aspects of existing methods: encoded information, training networks, fusion mechanisms, loss functions, and clustering result generation. Based on these aspects, we decompose existing methods into modules and evaluate the performance of reconfigured methods based on these modules. According to the experimental results on the proposed benchmark datasets, we identify two promising configurations: (i) taking the attribute matrix as input to a graph convolutional network and (ii) layer-wise linear fusing deep neural network and graph attention network. And we also find that complex loss function fails to improve the clustering performance.

Indoor location-based services rely on the availability of sufficiently accurate positioning in indoor spaces. A popular approach to positioning relies on so-called radio maps that contain pairs of a vector of Wi-Fi signal strength indicator values (RSSIs), called a fingerprint, and a location label, called a reference point (RP), in which the fingerprint was observed. The positioning accuracy depends on the quality of the radio maps and their fingerprints. Radio maps are often sparse, with many pairs containing vectors missing many RSSIs as well as RPs. Aiming to improve positioning accuracy, we present a complete set of techniques to impute such missing values in radio maps. We differentiate two types of missing RSSIs: missing not at random (MNAR) and missing at random (MAR). Specifically, we design a framework encompassing a missing RSSI differentiator followed by a data imputer for missing values. The differentiator identifies MARs and MNARs via clustering-based fingerprint analysis. Missing RSSIs and RPs are then imputed jointly by means of a novel encoder-decoder architecture that leverages temporal dependencies in data collection as well as correlations among fingerprints and RPs. A time-lag mechanism is used to consider the aging of data, and a sparsity-friendly attention mechanism is used to focus attention score calculation on observed data. Extensive experiments with real data from two buildings show that our proposal outperforms the alternatives with significant advantages in terms of imputation accuracy and indoor positioning accuracy.

Learned indices can leverage the high prediction accuracy and efficiency of modern deep learning techniques. They are capable of delivering better query performance than traditional indices over one-dimensional data. Recent studies demonstrate that we can also achieve query-efficient learned in-dices for spatial data by partitioning and subsequently transforming spatial data to one-dimensional values, after which existing techniques can be applied. While enabling efficient querying, building and rebuilding learned spatial indices efficiently remains largely unaddressed. As the model training needed to learn a spatial index is costly, efficient building and rebuilding of learned spatial indices on large data sets is challenging if performed by means of model training and retraining.To advance the practicality of learned spatial indices, we propose a system named ELSI that enables the efficient building and rebuilding of a class of learned spatial indices that follow two simple design principles. The core idea is to reduce the model (re-)building times by engineering reduced training sets that preserve key data distribution patterns. ELSI encompasses a suite of methods for constructing small and distribution-preserving training sets from input data sets. Further, given an input data set, ELSI can adaptively select a method that produces a learned index with high query efficiency. Experiments on real data sets of 100+ million points show that ELSI can reduce the build times of four different learned spatial indices consistently (by up to two orders of magnitude) without jeopardizing query efficiency.

Computing the shortest path distance between two vertices in a road network is a building block in numerous applications. To do so efficiently, the state-of-the-art proposals adopt a tree decomposition process with heuristic strategies to build 2-hop label indexes. However, these indexes suffer from large space overheads caused by either tree imbalance or a large tree height. Independently of this, reinforcement learning has recently show impressive performance at sequential decision making in spatial data management tasks. We observe that tree decomposition is naturally a sequential decision making problem that decides which vertex to process at each step. In this paper, we propose a reinforcement learning based tree decomposition (RLTD) approach that reduces the space overhead significantly. We model tree decomposition as a Markov Decision Process, exploiting features of both the network topological structure and the tree structure. We further optimize the tree decomposition process by taking the network density into account, which yields a great generalization of the model on large road networks. Extensive experiments with real-world data offer insights into the performance of the proposals, showing that they are able to reduce the space overhead by about 51% and achieve on average about 14% speedup for queries with almost the same preprocessing time when compared with the state-of-the-art proposals.

Approximate nearest neighbor (ANN) search in high-dimensional space plays an essential role in a variety of real-world applications. A well-known solution to ANN search, inverted file product quantization (IVFPQ) adopts inverted files to avoid exhaustive examination and compresses vectors using product quantization to reduce the space overhead. However, existing implementations use the same fixed probing cardinality (i.e., the number of cells to probe) setting for all queries, which leads to too many or too few cell examinations, thus increasing the average query latency or reducing the recall. To achieve a better trade-off between latency and accuracy, we enable probing cardinality estimation for high-dimensional ANN search by using deep learning techniques. We develop HBK-means, a hierarchical balanced clustering algorithm that reduces the data distribution imbalance of cells to enable a better estimation. Next, we develop PCE-Net, an encoder-decoder based neural network for estimating query-dependent minimum probing cardinality. In addition, we introduce two query optimization strategies: lower bound sorting based pruning (LBS-Pruning) and early termination (ET), to further reduce query latency. Extensive experiments with real-world data offer evidence that the proposed solution is capable of achieving better performance than IVFPQ and its variants.

Movement paths are used widely in intelligent transportation and smart city applications. To serve such applications, path representation learning aims to provide compact representations of paths that enable efficient and accurate operations when used for different downstream tasks such as path ranking and travel cost estimation. In many cases, it is attractive that the path representation learning is lightweight and scalable; in resource-limited environments and under green computing limitations, it is essential. Yet, existing path representation learning studies focus on accuracy and pay at most secondary attention to resource consumption and scalability. We propose a lightweight and scalable path representation learning framework, termed LightPath, that aims to reduce resource consumption and achieve scalability without affecting accuracy, thus enabling broader applicability. More specifically, we first propose a sparse auto-encoder that ensures that the framework achieves good scalability with respect to path length. Next, we propose a relational reasoning framework to enable faster training of more robust sparse path encoders. We also propose global-local knowledge distillation to further reduce the size and improve the performance of sparse path encoders. Finally, we report extensive experiments on two real-world datasets to offer insight into the efficiency, scalability, and effectiveness of the proposed framework.

Test2SQL, a natural language interface to database querying, has seen considerable improvement, in part due to advances in deep learning. However, despite recent improvement, existing Text2SQL proposals allow only input in the form of complete questions. This leaves behind users who struggle to formulate complete questions, e.g., because they lack database expertise or are unfamiliar with the underlying database schema. To address this shortcoming, we study the novel problem of Text2SQL Auto-Completion (TSAC) that extends Text2SQL to also take partial or incomplete questions as input. Specifically, the TSAC problem is to predict the complete, executable SQL query. To solve the problem, we propose a novel Relation-aware Historical Bridging Network (RHB-Net) that consists of a relation-aware union encoder and an extraction-generation sensitive decoder. RHB-Net models relations between questions and database schemas and predicts the ambiguous intents expressed in partial queries. We also propose two optimization strategies: historical query bridging that fuses historical database queries, and a dynamic context construction that prevents repeated generation of the same SQL elements. Extensive experiments with real-world data offer evidence that RHB-Net is capable of outperforming baseline algorithms.

Correlated time series (CTS) forecasting plays an essential role in many practical applications, such as traffic management and server load control. Many deep learning models have been proposed to improve the accuracy of CTS forecasting. However, while models have become increasingly complex and computationally intensive, they struggle to improve accuracy. Pursuing a different direction, this study aims instead to enable much more efficient, lightweight models that preserve accuracy while being able to be deployed on resource-constrained devices. To achieve this goal, we characterize popular CTS forecasting models and yield two observations that indicate directions for lightweight CTS forecasting. On this basis, we propose the LightCTS framework that adopts plain stacking of temporal and spatial operators instead of alternate stacking that is much more computationally expensive. Moreover, LightCTS features light temporal and spatial operator modules, called L-TCN and GL-Former, that offer improved computational efficiency without compromising their feature extraction capabilities. LightCTS also encompasses a last-shot compression scheme to reduce redundant temporal features and speed up subsequent computations. Experiments with single-step and multi-step forecasting benchmark datasets show that LightCTS is capable of nearly state-of-the-art accuracy at much reduced computational and storage overheads.

Due to the sweeping digitalization of processes, increasingly vast amounts of time series data are being produced. Accurate classification of such time series facilitates decision making in multiple domains. State-of-the-art classification accuracy is often achieved by ensemble learning where results are synthesized from multiple base models. This characteristic implies that ensemble learning needs substantial computing resources, preventing their use in resource-limited environments, such as in edge devices. To extend the applicability of ensemble learning, we propose the LightTS framework that compresses large ensembles into lightweight models while ensuring competitive accuracy. First, we propose adaptive ensemble distillation that assigns adaptive weights to different base models such that their varying classification capabilities contribute purposefully to the training of the lightweight model. Second, we propose means of identifying Pareto optimal settings w.r.t. model accuracy and model size, thus enabling users with a space budget to select the most accurate lightweight model. We report on experiments using 128 real-world time series sets and different types of base models that justify key decisions in the design of LightTS and provide evidence that LightTS is able to outperform competitors.

Trajectory similarity queries, including similarity search and similarity join, offer a foundation for many geo-spatial applications. With the rapid increase of streaming trajectory data volumes, e.g., data from mobile phones, vessel monitoring, or traffic systems, many location-based services benefit from online similarity analytics over trajectory data streams, where moving objects continually emit real-time position data. However, most existing studies focus on offline settings, and thus several major challenges remain unanswered in an online setting. To this end, we describe Ghost, a distributed stream processing framework that enables generic, efficient, and scalable online trajectory similarity search and join. We propose a novel incremental online similarity computation (IOSC) mechanism to accelerate pair-wise streaming trajectory distance calculation, which supports a broad range of trajectory distance metrics. Compared with previous studies, IOSC reduces the complexity from quadratic to linear in terms of trajectory length. Building on this foundation, we propose histogram-based algorithms that exploit histogram indexes and a series of pruning bounds to enable streaming trajectory similarity search and join. Finally, we extend our methods to the distributed platform Flink for scalability, where a CostPartitioner is developed to ensure parallel processing and workload balancing. An experimental study using two real-life and one synthetic datasets shows that Ghost (i) acquires 6-20× efficiency/throughput gains and one order of magnitude memory overhead savings over state-of-the-art baselines, (ii) achieves 3--8× workload balancing gains on Flink, and (iii) exhibits low parameter sensitivity and high robustness.

Given an origin (O), a destination (D), and a departure time (T), an Origin-Destination (OD) travel time oracle~(ODT-Oracle) returns an estimate of the time it takes to travel from O to D when departing at T. ODT-Oracles serve important purposes in map-based services. To enable the construction of such oracles, we provide a travel-time estimation (TTE) solution that leverages historical trajectories to estimate time-varying travel times for OD pairs. The problem is complicated by the fact that multiple historical trajectories with different travel times may connect an OD pair, while trajectories may vary from one another. To solve the problem, it is crucial to remove outlier trajectories when doing travel time estimation for future queries. We propose a novel, two-stage framework called Diffusion-based Origin-destination Travel Time Estimation (DOT), that solves the problem. First, DOT employs a conditioned Pixelated Trajectories (PiT) denoiser that enables building a diffusion-based PiT inference process by learning correlations between OD pairs and historical trajectories. Specifically, given an OD pair and a departure time, we aim to infer a PiT. Next, DOT encompasses a Masked Vision Transformer~(MViT) that effectively and efficiently estimates a travel time based on the inferred PiT. We report on extensive experiments on two real-world datasets that offer evidence that DOT is capable of outperforming baseline methods in terms of accuracy, scalability, and explainability.

Distributed computing is promising to enable large-scale graph neural network (GNN) model training. However, care is needed to avoid excessive computational and communication overheads. Sampling is promising in terms of enabling scalability, and sampling techniques have been proposed to reduce training costs. However, online sampling introduces large overheads, and while offline sampling that is done only once can eliminate such overheads, it instead introduces information loss and accuracy degradation. Thus, existing sampling techniques are unable to improve simultaneously both efficiency and accuracy, particularly at low sampling rates. We develop a distributed system, ADGNN, for full-batch based GNN training that adopts a hybrid sampling architecture to enable a trade-off between efficiency and accuracy. Specifically, ADGNN employs sampling result reuse techniques to reduce the cost associated with sampling and thus improve training efficiency. To alleviate accuracy degradation, we introduce a new metric,Aggregation Difference (AD), that quantifies the gap between sampled and full neighbor set aggregation. We present so-called AD-Sampling that aims to minimize the Aggregation Difference with an adaptive sampling frequency tuner. Finally, ADGNN employs anAD -importance-based sampling technique for remote neighbors to further reduce communication costs. Experiments on five real datasets show that ADGNN is able to outperform the state-of-the-art by up to nearly 9 times in terms of efficiency, while achieving comparable accuracy to the non-sampling methods.

Sensors in cyber-physical systems often capture interconnected processes and thus emit correlated time series (CTS), the forecasting of which enables important applications. The key to successful CTS forecasting is to uncover the temporal dynamics of time series and the spatial correlations among time series. Deep learning-based solutions exhibit impressive performance at discerning these aspects. In particular, automated CTS forecasting, where the design of an optimal deep learning architecture is automated, enables forecasting accuracy that surpasses what has been achieved by manual approaches. However, automated CTS solutions remain in their infancy and are only able to find optimal architectures for predefined hyperparameters and scale poorly to large-scale CTS. To overcome these limitations, we propose AutoCTS+, a joint, scalable framework, to automatically devise effective CTS forecasting models. Specifically, we encode each candidate architecture and accompanying hyperparameters into a joint graph representation. We introduce an efficient Architecture-Hyperparameter Comparator (AHC) to rank all architecture-hyperparameter pairs, and we then further evaluate the top-ranked pairs to select an architecture-hyperparameter pair as the final model. Extensive experiments on six benchmark datasets demonstrate that AutoCTS+ not only eliminates manual efforts but also is capable of better performance than manually designed and existing automatically designed CTS models. In addition, it shows excellent scalability to large CTS.

Robust multivariate time series forecasting is crucial in many cyberphysical and Internet of Things applications. Existing state-of-the-art robust forecasting models decompose time series into independent functions covering trends and periodicities. However, these independent functions fail to capture correlations among multiple time series, thereby reducing prediction accuracy. Moreover, existing robust forecasting models treat certain abrupt but normal changes, e.g., caused by holidays, as outliers because they occur infrequently and have data distributions that resemble those of outliers. This exacerbates model bias and reduces prediction accuracy. This paper aims to capture correlations across multiple time series and abrupt but normal changes, thereby improving prediction accuracy. We employ weak labels to partition the dataset into source and target domains. Then, we propose the Domain Adversarial Robust Forecaster (DARF). This forecasting model is based on adversarial domain adaptation and includes two novel modules: Correlated Robust Forecaster (CORF) and Domain Critic. Specifically, CORF constitutes an encoder-decoder framework proficient at robust multivariate time series forecasting, and Domain Critic works to reduce data bias. Extensive experiments and discussions show that DARF is capable of state-of-the-art forecasting accuracy.

Sensor data streams occur widely in various real-time applications in the context of the Internet of Things (IoT). However, sensor data streams feature missing values due to factors such as sensor failures, communication errors, or depleted batteries. Missing values can compromise the quality of real-time analytics tasks and downstream applications. Existing imputation methods either make strong assumptions about streams or have low efficiency. In this study, we aim to accurately and efficiently impute missing values in data streams that satisfy only general characteristics in order to benefit real-time applications more widely. First, we propose a message propagation imputation network (MPIN) that is able to recover the missing values of data instances in a time window. We give a theoretical analysis of why MPIN is effective. Second, we present a continuous imputation framework that consists of data update and model update mechanisms to enable MPIN to perform continuous imputation both effectively and efficiently. Extensive experiments on multiple real datasets show that MPIN can outperform the existing data imputers by wide margins and that the continuous imputation framework is efficient and accurate.

Maximum inner product search (MIPS) in high-dimensional spaces has wide applications but is computationally expensive due to the curse of dimensionality. Existing studies employ asymmetric transformations that reduce the MIPS problem to a nearest neighbor search (NNS) problem, which can be solved using locality-sensitive hashing (LSH). However, these studies usually maintain multiple hash tables and locally examine them one by one, which may cause additional costs on probing unnecessary points. In addition, LSH is applied without taking into account the properties of the inner product. In this paper, we develop a fast search framework FARGO for MIPS on large-scale, high-dimensional data. We propose a global multi-probing (GMP) strategy that exploits the properties of the inner product to globally examine high quality candidates. In addition, we develop two optimization techniques. First, different with existing transformations that introduce either distortion errors or data distribution imbalances, we design a novel transformation, called random XBOX transformation, that avoids the negative effects of data distribution imbalances. Second, we propose a global adaptive early termination condition that finds results quickly and offers theoretical guarantees. We conduct extensive experiments with real-world data that offer evidence that FARGO is capable of outperforming existing proposals in terms of both accuracy and efficiency.

Growing demands for the efficient processing of extreme-scale time series workloads call for more capable time series database management systems (TSDBMS). Specifically, to maintain consistency and durability of transaction processing, systems employ write-ahead logging (WAL) whereby transactions are committed only after the related log entries are flushed to disk. However, when faced with massive I/O, this becomes a throughput bottleneck. Recent advances in byte-addressable Non-Volatile Memory (NVM) provide opportunities to improve logging performance by persisting logs to NVM instead. Existing studies typically track complex transaction dependencies and use barrier instructions of NVM to ensure log ordering. In contrast, few studies consider the heavy-tailed characteristics of time series workloads, where most transactions are independent of each other. We propose DecLog, a decentralized NVM-based logging system that enables concurrent logging of TSDBMS transactions. Specifically, we propose data-driven log sequence numbering and relaxed ordering strategies to track transaction dependencies and resolve serialization issues. We also propose a parallel logging method to persist logs to NVM after being compressed and aligned. An experimental study on the YCSB-TS benchmark offers insight into the performance properties of DecLog, showing that it improves throughput by up to 4.6× while offering lower recovery time in comparison to the open source TSDBMS Beringei.

The ability to decentralize knowledge graphs (KG) is important to exploit the full potential of the Semantic Web and realize the Web 3.0 vision. However, decentralization also renders KGs more prone to attacks with adverse effects on data integrity and query verifiability. While existing studies focus on ensuring data integrity, how to ensure query verifiability - thus guarding against incorrect, incomplete, or outdated query results - remains unsolved. We propose VeriDKG, the first SPARQL query engine for decentralized knowledge graphs (DKG) that offers both data integrity and query verifiability guarantees. The core of VeriDKG is the RGB-Trie, a new blockchain-maintained authenticated data structure (ADS) facilitating correctness proofs for SPARQL query results. VeriDKG enables verifiability of subqueries by gathering global index information on subgraphs using the RGB-Trie, which is implemented as a new variant of the Merkle prefix tree with an RGB color model. To enable verifiability of the final query result, the RGB-Trie is integrated with a cryptographic accumulator to support verifiable aggregation operations. A rigorous analysis of query verifiability in VeriDKG is presented, along with evidence from an extensive experimental study demonstrating its state-of-the-art query performance on the largeRDFbench benchmark.

The proliferation of event-based social networking (EBSN) motivates studies on topics such as event, venue, and friend recommendation as well as event creation and organization. In this setting, the notion of event-partner recommendation has attracted attention. When recommending an event to a user, this functionality allows the recommendation of partners with whom to attend the event. However, in existing proposals, recommendations are pushed to users at the system's initiative. In contrast, EBSNs provide users with keyword-based search functionality. This way, users may retrieve information in pull mode. We propose a new way of accessing information in EBSNs that combines pull and push, thus allowing users to not only conduct ad-hoc searches for events, but also to receive partner recommendations for retrieved events. Specifically, we define and study top-k k event-partner (k kEP) pair retrieval querying that integrates keyword-based search for events with event-partner recommendation. This type of query retrieves event-partner pairs, taking into account the relevance of events to user-supplied keywords and so-called together preferences that indicate the extent of a user's preference to attend an event with a given partner. To compute k kEP queries efficiently, we propose a rank-join based framework with three optimizations. Results of empirical studies with implementations of the proposed techniques demonstrate that the proposed techniques are capable of excellent performance.

With the diffusion of online mobile devices with geo-location capabilities, the infrastructure necessary for real-world deployment of Spatial Crowdsourcing (SC), where so-called mobile workers are assigned location-sensitive tasks, is in place. Some SC tasks cannot be completed by a single worker due to their complexity, but rather must be assigned to and completed by a group of users. Achieving such group assignments that satisfy all group members evenly is an open challenge. To address this challenge, we propose a novel preference-aware group task assignment framework encompassing two components: Mutual Information-based Preference Modeling (MIPM) and Preference-aware Group Task Assignment (PGTA). The MIPM component learns the preferences of groups contrastively by maximizing the mutual information between workers and worker groups based on worker-task and group-task interaction data and by using an attention mechanism to weight group members adaptively. In addition, curriculum negative sampling is adopted to generate a small number of negative workers for each worker group, following the principles of curriculum learning. Next, the PGTA component offers an optimal task assignment algorithm that employs tree decomposition to assign tasks to appropriate worker groups, with the aim of maximizing the number of task assignments while prioritizing more interested groups when assigning tasks. The task assignment framework also features preference-constrained pruning of unpromising worker groups to speed up the assignment process. Finally, we report extensive experiments that offer evidence of the effectiveness and practicality of the paper's proposal.

In Spatial crowdsourcing, mobile users perform spatio-temporal tasks that involve travel to specified locations. Spatial crowdsourcing (SC) is enabled by SC platforms that support mobile worker recruitment and retention, as well as task assignment, which is essential to maximize profits that are accrued from serving task requests. Specifically, how to best achieve task assignment in a cost-effective manner while contending with spatio-temporal constraints is a key challenge in SC. To address this challenge, we formalize and study a novel Profit-driven Task Assignment problem. We first establish a task reward pricing model that takes into account the temporal constraints (i.e., expected completion time and deadline) of tasks. Then we adopt an optimal algorithm based on tree decomposition to achieve an optimal task assignment and propose greedy algorithms based on Random Tuning Optimization to improve the computational efficiency. To balance effectiveness and efficiency, we also provide a heuristic task assignment algorithm based on Ant Colony Optimization that assigns tasks by simulating behavior of ant colonies foraging for food. Finally, we conduct extensive experiments using real and synthetic data, offering detailed insight into effectiveness and efficiency of the proposed methods.

We consider a setting with an evolving set of requests for transportation from an origin to a destination before a deadline and a set of agents capable of servicing the requests. In this setting, an assignment authority is to assign agents to requests such that the average idle time of the agents is minimized. An example is the scheduling of taxis (agents) to meet incoming passenger requests for trips while ensuring that the taxis are empty as little as possible. In this paper, we study the problem of spatial-temporal demand forecasting and competitive supply (SOUP). We address the problem in two steps. First, we build a granular model that provides spatial-temporal predictions of requests. Specifically, we propose a Spatial-Temporal Graph Convolutional Sequential Learning (ST-GCSL) model that predicts the requests across locations and time slots. Second, we provide means of routing agents to request origins while avoiding competition among the agents. In particular, we develop a demand-aware route planning (DROP) algorithm that considers both the spatial-temporal predictions and the supply-demand state. We report on extensive experiments with real-world data that offer insight into the performance of the solution and show that it is capable of outperforming the state-of-the-art proposals.

With the widespread diffusion of smartphones, Spatial Crowdsourcing (SC), which aims to assign spatial tasks to mobile workers, has drawn increasing attention in both academia and industry. One of the major issues is how to best assign tasks to workers. Given a worker and a task, the worker will choose to accept the task based on her affinity towards the task, and the worker can propagate the information of the task to attract more workers to perform it. These factors can be measured as worker-task influence. Since workers' affinities towards tasks are different and task issuers may ask workers who performed tasks to propagate the information of tasks to attract more workers to perform them, it is important to analyze worker-task influence when making assignments. We propose and solve a novel influence-aware task assignment problem in SC, where tasks are assigned to workers in a manner that achieves high worker-task influence. In particular, we aim to maximize the number of assigned tasks and worker-task influence. To solve the problem, we first determine workers' affinities towards tasks by identifying workers' historical task-performing patterns. Next, a Historical Acceptance approach is developed to measure workers' willingness of performing a task, i.e., the probability of workers visiting the location of the task when they are informed. Next, we propose a Random reverse reachable-based Propagation Optimization algorithm that exploits reverse reachable sets to calculate the probability of workers being informed about tasks in a social network. Based on worker-task influence derived from the above three factors, we propose three influence-aware task assignment algorithms that aim to maximize the number of assigned tasks and worker-task influence. Extensive experiments on two real-world datasets offer detailed insight into the effectiveness of our solutions.

We propose variational quasi-recurrent autoencoders (VQRAEs) to enable robust and efficient anomaly detection in time series in unsupervised settings. The proposed VQRAEs employs a judiciously designed objective function based on robust divergences, including a, ß, and, -divergence, making it possible to separate anomalies from normal data without the reliance on anomaly labels, thus achieving robustness and fully unsupervised training. To better capture temporal dependencies in time series data, VQRAEs are built upon quasi-recurrent neural networks, which employ convolution and gating mechanisms to avoid the inefficient recursive computations used by classic recurrent neural networks. Further, VQRAEs can be extended to bi-directional Bi VQRAEs that utilize bi-directional information to further improve the accuracy. The above design choices make VQRAEs not only robust and thus accurate, but also efficient at detecting anomalies in streaming settings. Experiments on five real-world time series offer insight into the design properties of VQRAEs and demonstrate that VQRAEs are capable of outperforming state-of-the-art methods.

Time series data occurs widely, and outlier detection is a fundamental problem in data mining, which has numerous applications. Existing autoencoder-based approaches deliver state-of-the-art performance on challenging real-world data but are vulnerable to outliers and exhibit low explainability. To address these two limitations, we propose robust and explainable unsupervised auto encoder frameworks that decompose an input time series into a clean time series and an outlier time series using autoencoders. Improved explainability is achieved because clean time series are better explained with easy-to-understand patterns such as trends and periodicities. We provide insight into this by means of a post-hoc explainability analysis and empirical studies. In addition, since outliers are separated from clean time series iteratively, our approach offers improved robustness to outliers, which in turn improves accuracy. We evaluate our approach on five real-world datasets and report improvements over the state-of-the-art approaches in terms of robustness and explainability.

The widespread deployment of smartphones, net-worked in-vehicle devices with geo-positioning capabilities, and vessel tracking technologies renders it feasible to collect the evolving geo-locations of populations of land- and sea-based moving objects. The continuous clustering of such data can enable a variety of real-time services, such as road traffic management and vessel collision risk assessment. However, little attention has so far been given to the quality of moving-object clusters-for example, it is beneficial to smooth short-term fluctuations in clusters to achieve robustness to exceptional data and to improve existing applications. We propose the notion of evolutionary clustering of moving objects, abbreviated ECM, that enhances the quality of moving object clustering by means of temporal smoothing that prevents abrupt changes in clusters across successive timestamps. Employing the notions of snapshot and historical costs, we formalize ECM and formulate ECM as an optimization problem. We prove that ECM can be performed approximately in linear time, thus eliminating iterative processes employed in previous studies. Further, we propose a minimal-group structure and a seed-point shifting strategy to facilitate temporal smoothing. Finally, we present all algorithms underlying ECM along with a set of optimization techniques. Extensive experiments with three real-life datasets offer insights into ECM and show that it outperforms state-of-the-art solutions in terms of both clustering quality and clustering efficiency.

In step with the digitalization of transportation, we are witnessing a growing range of path-based smart-city applications, e.g., travel-time estimation and travel path ranking. A temporal path (TP) that includes temporal information, e.g., departure time, into the path is of fundamental to enable such applications. In this setting, it is essential to learn generic temporal path representations (TPRs) that consider spatial and temporal correlations simultaneously and that can be used in different applications, i.e., downstream tasks. Existing methods fail to achieve the goal since (i) supervised methods require large amounts of task-specific labels when training and thus fail to generalize the obtained TPRs to other tasks; (ii) though unsupervised methods can learn generic representations, they disregard the temporal aspect, leading to sub-optimal results. To contend with the limitations of existing solutions, we propose a Weakly-Supervised Contrastive learning model. We first propose a temporal path encoder that encodes both the spatial and temporal information of a temporal path into a TPR. To train the encoder, we introduce weak labels that are easy and inexpensive to obtain, and are relevant to different tasks, e.g., temporal labels indicating peak vs. off-peak hour from departure times. Based on the weak labels, we construct meaningful positive and negative temporal path samples by considering both spatial and temporal information, which facilities training the encoder using contrastive learning by pulling closer the positive samples' representations while pushing away the negative samples' representations. To better guide the contrastive learning, we propose a learning strategy based on Curriculum Learning such that the learning performs from easy to hard training instances. Experimental studies involving three downstream tasks, i.e., travel time estimation, path ranking, and path recommendation, on three road networks offer strong evidence that the proposal is superior to state-of-the-art unsupervised and supervised methods and that it can be used as a pre-training approach to enhance supervised TPR learning.

Computing shortest-path distances in road networks is core functionality in a range of applications. To enable the efficient computation of such distance queries, existing proposals frequently apply 2-hop labeling that constructs a label for each vertex and enables the computation of a query by performing only a linear scan of labels. However, few proposals take into account the spatio-temporal characteristics of query workloads. We observe that real-world workloads exhibit (1) spatial skew, meaning that only a small subset of vertices are queried frequently, and (2) temporal locality, meaning that adjacent time intervals have similar query distributions. We propose a Workload-aware Core-Forest label index (WCF) to exploit spatial skew in workloads. In addition, we develop a Reinforcement Learning based Time Interval Partitioning (RL-TIP) algorithm that exploits temporal locality to partition workloads to achieve further performance improvements. Extensive experiments with real-world data offer insights into the performance of the proposals, showing that they achieve 62% speedup on average for query processing with less preprocessing time and space overhead when compared with the state-of-the-art proposals.

Deep learning based trajectory similarity computation holds the potential for improved efficiency and adaptability over traditional similarity computation. However, existing learning-based trajectory similarity learning solutions prioritize spatial similarity over temporal similarity, making them suboptimal for time-aware analyses. To this end, we propose ST2Vec, a representation learning based solution that considers fine-grained spatial and temporal relations between trajectories to enable spatio-temporal similarity computation in road networks. Specifically, ST2Vec encompasses two steps: (i) spatial and temporal modeling that encode spatial and temporal information of trajectories, where a generic temporal modeling module is proposed for the first time; and (ii) spatio-temporal co-attention fusion, where two fusion strategies are designed to enable the generation of unified spatio-temporal embeddings of trajectories. Further, under the guidance of triplet loss, ST2Vec employs curriculum learning in model optimization to improve convergence and effectiveness. An experimental study offers evidence that ST2Vec outperforms state-of-the-art competitors substantially in terms of effectiveness and efficiency, while showing low parameter sensitivity and good model robustness. Moreover, similarity involved case studies including top-k querying and DBSCAN clustering offer further insight into the capabilities of ST2Vec.

Within the rapidly expanding Internet of Things (IoT), growing amounts of spatially referenced data are being generated. Due to the dynamic, decentralized, and heterogeneous nature of the IoT, spatial IoT data (SID) quality has attracted considerable attention in academia and industry. How to invent and use technologies for managing spatial data quality and exploiting low-quality spatial data are key challenges in the IoT. In this tutorial, we highlight the SID consumption requirements in applications and offer an overview of spatial data quality in the IoT setting. In addition, we review pertinent technologies for quality management and low-quality data exploitation, and we identify trends and future directions for quality-aware SID management and utilization. The tutorial aims to not only help researchers and practitioners to better comprehend SID quality challenges and solutions, but also offer insights that may enable innovative research and applications.

The ongoing, sweeping digitalization of societal processes generates massive volumes of data that capture the underlying processes at an unprecedented level of detail, in turn enabling us to better understand and improve those processes. Put differently, if harnessed properly, data holds the potential to enable value creation throughout society.

Crowdsourcing aims to enable the assignment of available resources to the completion of tasks at scale. The continued digitization of societal processes translates into increased opportunities for crowdsourcing. For example, crowdsourcing enables the assignment of computational resources of humans, called workers, to tasks that are notoriously hard for computers. In settings faced with malicious actors, detection of such actors holds the potential to increase the robustness of crowdsourcing platform. We propose a framework called Outlier Detection for Streaming Task Assignment that aims to improve robustness by detecting malicious actors. In particular, we model the arrival of workers and the submission of tasks as evolving time series and provide means of detecting malicious actors by means of outlier detection. We propose a novel socially aware Generative Adversarial Network (GAN) based architecture that is capable of contending with the complex distributions found in time series. The architecture includes two GANs that are designed to adversarially train an autoencoder to learn the patterns of distributions in worker and task time series, thus enabling outlier detection based on reconstruction errors. A GAN structure encompasses a game between a generator and a discriminator, where it is desirable that the two can learn to coordinate towards socially optimal outcomes, while avoiding being exploited by selfish opponents. To this end, we propose a novel training approach that incorporates social awareness into the loss functions of the two GANs. Additionally, to improve task assignment efficiency, we propose an efficient greedy algorithm based on degree reduction that transforms task assignment into a bipartite graph matching. Extensive experiments offer insight into the effectiveness and efficiency of the proposed framework.

Underlying many types of data analytics, a spatiotemporal quantile monitoring (SQM) query continuously returns the quantiles of a dataset observed in a spatiotemporal range. In this paper, we study SQM in an Internet of Things (IoT) based edge computing environment, where concurrent SQM queries share the same infrastructure asynchronously. To minimize query latency while providing result accuracy guarantees, we design a processing framework that virtualizes edge-resident data sketches for quantile computing. In the framework, a coordinator edge node manages edge sketches and synchronizes edge sketch processing and query executions. The co-ordinator also controls the processed data fractions of edge sketches, which helps to achieve the optimal latency with error-bounded results for each single query. To support concurrent queries, we employ a grid to decompose queries into subqueries and process them efficiently using shared edge sketches. We also devise a relaxation algorithm to converge to optimal latencies for those subqueries whose result errors are still bounded. We evaluate our proposals using two high-speed streaming datasets in a simulated IoT setting with edge nodes. The results show that our proposals achieve efficient, scalable, and error-bounded SQM.

We all depend on mobility, and vehicular transportation affects the daily lives of most of us. Thus, the ability to forecast the state of traffic in a road network is an important functionality and a challenging task. Traffic data is often obtained from sensors deployed in a road network. Recent proposals on spatial-temporal graph neural networks have achieved great progress at modeling complex spatial-temporal correlations in traffic data, by modeling traffic data as a diffusion process. However, intuitively, traffic data encompasses two different kinds of hidden time series signals, namely the diffusion signals and inherent signals. Unfortunately, nearly all previous works coarsely consider traffic signals entirely as the outcome of the diffusion, while neglecting the inherent signals, which impacts model performance negatively. To improve modeling performance, we propose a novel Decoupled Spatial-Temporal Framework (DSTF) that separates the diffusion and inherent traffic information in a data-driven manner, which encompasses a unique estimation gate and a residual decomposition mechanism. The separated signals can be handled subsequently by the diffusion and inherent modules separately. Further, we propose an instantiation of DSTF, Decoupled Dynamic Spatial-Temporal Graph Neural Network (D 2 STGNN), that captures spatial-temporal correlations and also features a dynamic graph learning module that targets the learning of the dynamic characteristics of traffic networks. Extensive experiments with four real-world traffic datasets demonstrate that the framework is capable of advancing the state-of-the-art.

Many datasets including social media data and bibliographic data can be modeled as graphs. Clustering such graphs is able to provide useful insights into the structure of the data. To improve the quality of clustering, node attributes can be taken into account, resulting in attributed graphs. Existing attributed graph clustering methods generally consider attribute similarity and structural similarity separately. In this paper, we represent attributed graphs as star-schema heterogeneous graphs, where attributes are modeled as different types of graph nodes. This enables the use of personalized pagerank (PPR) as a unified distance measure that captures both structural and attribute similarities. We employ DBSCAN for clustering, and we update edge weights iteratively to balance the importance of different attributes. The rapidly growing volume of data nowadays challenges traditional clustering algorithms, and thus, a distributed method is required. Hence, we adopt a popular distributed graph computing system Blogel, based on which, we develop four exact and approximate approaches that enable efficient PPR score computation when edge weights are updated. To improve the effectiveness of the clustering, we propose a simple yet effective edge weight update strategy based on entropy. In addition, we present a game theory based method that enables trading efficiency for result quality. Extensive experiments on real-life datasets offer insights into the effectiveness and efficiency of our proposals.

So-called spatial web queries retrieve web content representing points of interest, such that the points of interest have descriptions that are relevant to query keywords and are located close to a query location. Two broad categories of such queries exist. The first encompasses queries that retrieve single spatial web objects that each satisfy the query arguments. Most proposals belong to this category. The second category, to which this paper's proposal belongs, encompasses queries that support exploratory user behavior and retrieve sets of objects that represent regions of space that may be of interest to the user. Specifically, the paper proposes a new type of query, the top-$k$spatial textual cluster retrieval ($k$-STC) query that returns the top-$k$clusters that (i) are located close to a query location, (ii) contain objects that are relevant with regard to given query keywords, and (iii) have an object density that exceeds a given threshold. To compute this query, we propose a DBSCAN-based approach and an OPTICS-based approach that rely on on-line density-based clustering and that exploit early stop conditions. Empirical studies on real data sets offer evidence that the paper's proposals can find good quality clusters and are capable of excellent performance.

Abstract Joins are essential and potentially expensive operations in database management systems. When data is associated with time periods, joins commonly include predicates that require pairs of argument tuples to overlap in order to qualify for the result. Our goal is to enable built-in systems support for such joins. In particular, we present an approach where overlap joins are formulated as unions of range joins, which are more general purpose joins compared to overlap joins, i.e., are useful in their own right, and are supported well by B+-trees. The approach is sufficiently flexible that it also supports joins with additional equality predicates, as well as open, closed, and half-open time periods over discrete and continuous domains, thus offering both generality and simplicity, which is important in a system setting. We provide both a stand-alone solution that performs on par with the state-of-the-art and a DBMS embedded solution that is able to exploit standard indexing and clearly outperforms existing DBMS solutions that depend on specialized indexing techniques. We offer both analytical and empirical evaluations of the proposals. The empirical study includes comparisons with pertinent existing proposals and offers detailed insight into the performance characteristics of the proposals.

202402 bcch

Many navigation applications take natural language speech as input, which avoids typing in words with their hands and decreases the occurrence of traffic accidents. We propose the SpearkNav navigation system that enables users to describe intended routes via speech and supports clue-based route retrieval. SpeakNav includes a route description language understanding model for determining POIs and distances along expected routes, and it includes an efficient algorithm to compute desired routes. In addition, SpeakNav supports basic POI and location search and location-based route navigation. We demonstrate how SpeakNav accurately recognizes users' intentions and recommends appropriate routes in real application scenarios.

Online car-hailing services have gained substantial popularity. An effective taxi fleet management strategy should not only increase taxi utilization by reducing taxi idle time, but should also improve passenger satisfaction by minimizing passenger waiting time. We demonstrate a fleet management system called SOUP that aims at minimizing taxi idle time and that monitors the fleet movement status. SOUP includes a passenger request prediction model called ST-GCSL that predicts the number of requests in the near future, and it includes a demand-aware route planning algorithm called DROP that provides idle taxis with search routes to serve potential requests. In addition, SOUP supports visualizing and analyzing historical passenger requests, simulating fleet movement, and computing evaluation metrics. We demonstrate how SOUP accurately predicts passenger demand and significantly reduces taxi idle time.

The widespread diffusion of smartphones offers a capable foundation for the deployment of Spatial Crowdsourcing (SC), where mobile users, called workers, perform location- dependent tasks assigned to them. A key issue in SC is how best to assign tasks, e.g., the delivery of food and packages, to appropriate workers. Specifically, we study the problem of Fairness-aware Task Assignment (FTA) in SC, where tasks are to be assigned in a manner that achieves some notion of fairness across workers. In particular, we aim to minimize the payoff difference among workers while maximizing the average worker payoff. To solve the problem, we first generate so-called Valid Delivery Point Sets (VDPSs) for each worker according to an approach that exploits dynamic programming and distance- constrained pruning. Next, we show that FTA is NP-hard and proceed to propose two heuristic algorithms, a Fairness-aware Game-Theoretic (FGT) algorithm and an Improved Evolutionary Game-Theoretic (IEGT) algorithm. More specifically, we formulate FTA as a multi-player game. In this setting, the FGT approach represents a best-response method with sequential and asynchronous updates of workers' strategies, given by the VDPSs, that achieves a satisfying task assignment when a pure Nash equilibrium is reached. Next, the IEGT approach considers a setting with a large population of workers that repeatedly engage in strategic interactions. The IEGT approach exploits replicator dynamics that cause the whole population to evolve and choose better resources, i.e., VDPSs. Using the property of evolutionary equilibrium, a satisfying task assignment is obtained that corresponds to a stable state with similar payoffs among workers and good average worker payoff. Extensive experiments offer insight into the effectiveness and efficiency of the proposed solutions.

Trajectory similarity computation is a fundamental component in a variety of real-world applications, such as ridesharing, road planning, and transportation optimization. Recent advances in mobile devices have enabled an unprecedented increase in the amount of available trajectory data such that efficient query processing can no longer be supported by a single machine. As a result, means of performing distributed in-memory trajectory similarity search are called for. However, existing distributed proposals either suffer from computing resource waste or are unable to support the range of similarity measures that are being used. We propose a distributed in-memory management framework called REPOSE for processing top-k trajectory similarity queries on Spark. We develop a reference point trie (RP-Trie) index to organize trajectory data for local search. In addition, we design a novel heterogeneous global partitioning strategy to eliminate load imbalance in distributed settings. We report on extensive experiments with real-world data that offer insight into the performance of the solution, and show that the solution is capable of outperforming the state-of-the-art proposals.

With the sweeping digitalization of societal, medical, industrial, and scientific processes, sensing technologies are being deployed that produce increasing volumes of time series data, thus fueling a plethora of new or improved applications. In this setting, outlier detection is frequently important, and while solutions based on neural networks exist, they leave room for improvement in terms of both accuracy and efficiency. With the objective of achieving such improvements, we propose a diversity-driven, convolutional ensemble. To improve accuracy, the ensemble employs multiple basic outlier detection models built on convolutional sequence-to-sequence autoencoders that can capture temporal dependencies in time series. Further, a novel diversity-driven training method maintains diversity among the basic models, with the aim of improving the ensemble's accuracy. To improve efficiency, the approach enables a high degree of parallelism during training. In addition, it is able to transfer some model parameters from one basic model to another, which reduces training time. We report on extensive experiments using real-world multivariate time series that offer insight into the design choices underlying the new approach and offer evidence that it is capable of improved accuracy and efficiency.

The deployment of vehicle location services generates increasingly massive vehicle trajectory data, which incurs high storage and transmission costs. A range of studies target offline compression to reduce the storage cost. However, to enable online services such as real-time traffic monitoring, it is attractive to also reduce transmission costs by being able to compress streaming trajectories in real-time. Hence, we propose a framework called TRACE that enables compression, transmission, and querying of network-constrained streaming trajectories in a fully online fashion. We propose a compact two-stage representation of streaming trajectories: a speed-based representation removes redundant information, and a multiple-references based referential representation exploits subtrajectory similarities. In addition, the online referential representation is extended with reference selection, deletion and rewriting functions that further improve the compression performance. An efficient data transmission scheme is provided for achieving low transmission overhead. Finally, indexing and filtering techniques support efficient real-time range queries over compressed trajectories. Extensive experiments with real-life and synthetic datasets evaluate the different parts of TRACE, offering evidence that it is able to outperform the existing representative methods in terms of both compression ratio and transmission cost.

Correlated time series (CTS) forecasting plays an essential role in many cyber-physical systems, where multiple sensors emit time series that capture interconnected processes. Solutions based on deep learning that deliver state-of-the-art CTS forecasting performance employ a variety of spatio-temporal (ST) blocks that are able to model temporal dependencies and spatial correlations among time series. However, two challenges remain. First, ST-blocks are designed manually, which is time consuming and costly. Second, existing forecasting models simply stack the same ST-blocks multiple times, which limits the model potential. To address these challenges, we propose AutoCTS that is able to automatically identify highly competitive ST-blocks as well as forecasting models with heterogeneous ST-blocks connected using diverse topologies, as opposed to the same ST-blocks connected using simple stacking. Specifically, we design both a micro and a macro search space to model possible architectures of ST-blocks and the connections among heterogeneous ST-blocks, and we provide a search strategy that is able to jointly explore the search spaces to identify optimal forecasting models. Extensive experiments on eight commonly used CTS forecasting benchmark datasets justify our design choices and demonstrate that AutoCTS is capable of automatically discovering forecasting models that outperform state-of-the-art human-designed models.

Many navigation applications take natural language speech as input, which avoids users typing in words and thus improves traffic safety. However, navigation applications often fail to understand a user's free-form description of a route. In addition, they only support input of a specific source or destination, which does not enable users to specify additional route requirements. We propose a SpeakNav framework that enables users to describe intended routes via speech and then recommends appropriate routes. Specifically, we propose a novel Route Template based Bidirectional Encoder Representation from Transformers (RT-BERT) model that supports the understanding of natural language route descriptions. The model enables extraction of information of intended POI keywords and related distances. Then we formalize a template-driven path query that uses the extracted information. To enable efficient query processing, we develop a hybrid label index for computing network distances between POIs, and we propose a branch-and-bound algorithm along with a pivot reverse B-tree (PB-tree) index. Experiments with real and synthetic data indicate that RT-BERT offers high accuracy and that the proposed algorithm is capable of outperforming baseline algorithms.

To infer structures in diffusion networks, existing approaches mostly need to know not only the final infection statuses of network nodes, but also the exact times when infections occur. In contrast, in many real-world settings, such as disease propagation, monitoring exact infection times is often infeasible due to a high cost. We investigate the problem of how to learn diffusion network structures based on only the final infection statuses of nodes. Instead of utilizing sequences of timestamps to determine potential parent-child influence relationships between nodes, we propose to find influence relationships with high statistical significance. To this end, we design a probabilistic generative model of the final infection statuses to quantitatively measure the likelihood of potential structures of the objective diffusion network, taking into account network complexity. Based on this model, we can infer an appropriate number of most probable parent nodes for each node in the network. Furthermore, to reduce redundant inference computations, we are able to preclude insignificant candidate parent nodes from being considered during inferencing, if their infections have little correlation with the infections of the corresponding child nodes. Extensive experiments on both synthetic and real-world networks offer evidence that the proposed approach is effective and efficient.

Abstract Computing path queries such as the shortest path in public transport networks is challenging because the path costs between nodes change over time. A reachability query from a node at a given start time on such a network retrieves all points of interest (POIs) that are reachable within a given cost budget. Reachability queries are essential building blocks in many applications, for example, group recommendations, ranking spatial queries, or geomarketing. We propose an efficient solution for reachability queries in public transport networks. Currently, there are two options to solve reachability queries. (1) Execute a modified version of Dijkstra’s algorithm that supports time-dependent edge traversal costs; this solution is slow since it must expand edge by edge and does not use an index. (2) Issue a separate path query for each single POI, i.e., a single reachability query requires answering many path queries. None of these solutions scales to large networks with many POIs. We propose a novel and lightweight reachability index. The key idea is to partition the network into cells. Then, in contrast to other approaches, we expand the network cell by cell. Empirical evaluations on synthetic and real-world networks confirm the efficiency and the effectiveness of our index-based reachability query solution.

Matching similar pairs of trajectories, called trajectory similarity join, is a fundamental functionality in spatial data management. We consider the problem of semantic trajectory similarity join (STS-Join). Each semantic trajectory is a sequence of Points-of-interest (POIs) with both location and text information. Thus, given two sets of semantic trajectories and a threshold θ, the STS-Join returns all pairs of semantic trajectories from the two sets with spatio-textual similarity no less than θ. This join targets applications such as term-based trajectory near-duplicate detection, geo-text data cleaning, personalized ridesharing recommendation, keyword-aware route planning, and travel itinerary recommendation.With these applications in mind, we provide a purposeful definition of spatio-textual similarity. To enable efficient STS-Join processing on large sets of semantic trajectories, we develop trajectory pair filtering techniques and consider the parallel processing capabilities of modern processors. Specifically, we present a two-phase parallel search algorithm. We first group semantic trajectories based on their text information. The algorithm's per-group searches are independent of each other and thus can be performed in parallel. For each group, the trajectories are further partitioned based on the spatial domain. We generate spatial and textual summaries for each trajectory batch, based on which we develop batch filtering and trajectory-batch filtering techniques to prune unqualified trajectory pairs in a batch mode. Additionally, we propose an efficient divide-and-conquer algorithm to derive bounds of spatial similarity and textual similarity between two semantic trajectories, which enable us prune dissimilar trajectory pairs without the need of computing the exact value of spatio-textual similarity. Experimental study with large semantic trajectory data confirms that our algorithm of processing semantic trajectory join is capable of outperforming our well-designed baseline by a factor of 8-12.

Origin-destination (OD) matrices are used widely in transportation and logistics to record the travel cost (e.g., travel speed or greenhouse gas emission) between pairs of OD regions during different intervals within a day. We model a travel cost as a distribution because when traveling between a pair of OD regions, different vehicles may travel at different speeds even during the same interval, e.g., due to different driving styles or different waiting times at intersections. This yields stochastic OD matrices. We consider an increasingly pertinent setting where a set of vehicle trips is used for instantiating OD matrices. Since the trips may not cover all OD pairs for each interval, the resulting OD matrices are likely to be sparse. We then address the problem of forecasting complete, near future OD matrices from sparse, historical OD matrices. To solve this problem, we propose a generic learning framework that (i) employs matrix factorization and graph convolutional neural networks to contend with the data sparseness while capturing spatial correlations and that (ii) captures spatio-temporal dynamics via recurrent neural networks extended with graph convolutions. Empirical studies using two taxi trajectory data sets offer detailed insight into the properties of the framework and indicate that it is effective.

Networked data, notably social network data, often comes with a rich set of annotations, or attributes, such as documents (e.g., tweets) and locations (e.g., check-ins). Community search in such attributed networks has been studied intensively due to its many applications in friends recommendation, event organization, advertising, etc. We study the problem of attribute-constrained co-located community (ACOC) search, which returns a community that satisfies three properties: i) structural cohesiveness: the members in the community are densely connected; ii) spatial co-location: the members are close to each other; and iii) attribute constraint: a set of attributes are covered by the attributes associated with the members. The ACOC problem is shown to be NP-hard. We develop four efficient approximation algorithms with guaranteed error bounds in addition to an exact solution that works on relatively small graphs. Extensive experiments conducted with both real and synthetic data offer insight into the efficiency and effectiveness of the proposed methods, showing that they outperform three adapted state-of-the-art algorithms by an order of magnitude. We also find that the approximation algorithms are much faster than the exact solution and yet offer high accuracy.

Increasingly available trajectory data enables detailed capture of traffic conditions. We consider an uncertain road network graph, where each graph edge is associated with a travel time distribution, and we study probabilistic budget routing that aims to find the path with the highest probability of arriving within a given time budget. In this setting, a fundamental operation is to compute the travel cost distribution of a path from the cost distributions of the edges in the path. Solutions that rely on convolution generally assume independence among the edges' distributions, which often does not hold and thus incurs poor accuracy. We propose a hybrid approach that combines convolution and machine learning-based estimation to take into account dependencies among distributions in order to improve accuracy. Next, we propose an efficient routing algorithm that is able to utilize the hybrid approach and that features effective pruning techniques to enable faster routing. Empirical studies on a substantial real-world trajectory set offer insight into the properties of the proposed solution, indicating that it is promising.

The set similarity join (SSJ) is core functionality in a range of applications, including data cleaning, near-duplicate object detection, and data integration. Threshold-based SSJ queries return all pairs of sets with similarity no smaller than a given threshold. As results, and their utility, are very sensitive to the choice of threshold value, it is a problem that it is difficult to choose such an appropriate value. Doing so requires prior knowledge of the data, which users often do not have. To avoid this problem, we propose a solution to the top-k overlap set similarity join (TkOSSJ) that returns k pairs of sets with the highest overlap similarities. The state-of-the-art solution disregards the effect of the so-called step size, which is the number of elements accessed in each iteration of the algorithm. This affects its performance negatively. To address this issue, we first propose an algorithm that uses a fixed step size, thus taking advantage of the benefits of a large step size, and then we present an adaptive step size algorithm that is capable of automatically adjusting the step size, thus reducing redundant computations. An extensive empirical study offers insight into the new algorithms and indicates that they are capable of outperforming the state-of-the-art method on real, large-scale data sets.

In Pickup-and-Delivery problems (PDP), mobile workers are employed to pick up and deliver items with the goal of reducing travel and fuel consumption. Unlike most existing efforts that focus on finding a schedule that enables the delivery of as many items as possible at the lowest cost, we consider trichromatic (worker-item-task) utility that encompasses worker reliability, item quality, and task profitability. Moreover, we allow customers to specify keywords for desired items when they submit tasks, which may result in multiple pickup options, thus further increasing the difficulty of the problem. Specifically, we formulate the problem of Online Trichromatic Pickup and Delivery Scheduling (OTPD) that aims to find optimal delivery schedules with highest overall utility. In order to quickly respond to submitted tasks, we propose a greedy solution that finds the schedule with the highest utility-cost ratio. Next, we introduce a skyline kinetic tree-based solution that materializes intermediate results to improve the result quality. Finally, we propose a density-based grouping solution that partitions streaming tasks and efficiently assigns them to the workers with high overall utility. Extensive experiments with real and synthetic data offer evidence that the proposed solutions excel over baselines with respect to both effectiveness and efficiency.

The aim of real-time co-movement pattern mining for streaming trajectories is to discover co-moving objects that satisfy specific spatio-temporal constraints in real time. This functionality serves a range of real-world applications, such as traffic monitoring and management. However, little work targets the visualization and interaction with such co-movement detection on streaming trajectories. To this end, we develop CoMing, a real-time co-movement pattern mining system, to handle streaming trajectories. CoMing leverages ICPE, a real-time distributed co-movement pattern detection framework, and thus, it has its capacity of good performance. This demonstration offers hands-on experience with CoMing's visual and user-friendly interface. Moreover, several applications in the traffic domain, including object monitoring and traffic statistics visualization, are also provided to users.

Massive volumes of uncertain trajectory data are being generated by GPS devices. Due to the limitations of GPS data, these trajectories are generally uncertain. This state of affairs renders it is attractive to be able to compress uncertain trajectories and to be able to query the trajectories efficiently without the need for (full) decompression. Unlike existing studies that target accurate trajectories, we propose a framework that accommodates uncertain trajectories in road networks. To address the large cardinality of instances of a single uncertain trajectory, we exploit the similarity between uncertain trajectory instances and provide a referential representation. First, we propose a reference selection algorithm based on the notion of Fine-grained Jaccard Distance to efficiently select trajectory instances as references. Then we provide referential representations of the different types of information contained in trajectories to achieve high compression ratios. In particular, a new compression scheme for temporal information is presented to take into account variations in sample intervals. Finally, we propose an index and develop filtering techniques to support efficient queries over compressed uncertain trajectories. Extensive experiments with real-life datasets offer insight into the properties of the framework and suggest that it is capable of outperforming the existing state-of-the-art method in terms of both compression ratio and efficiency.

Increasingly massive volumes of vehicle trajectory data hold the potential to enable higher-resolution traffic services than hitherto possible. We use trajectory data to create a high-resolution, uncertain road-network graph, where edges are associated with travel-time distributions. In this setting, we study probabilistic budget routing that aims to find the path with the highest probability of arriving at a destination within a given time budget. A key challenge is to compute accurately and efficiently the travel-time distribution of a path from the travel-time distributions of the edges in the path. Existing solutions that rely on convolution assume independence among the distributions to be convolved, but as distributions are often dependent, the result distributions exhibit poor accuracy. We propose a hybrid approach that combines convolution with estimation based on machine learning to account for dependencies among distributions in order to improve accuracy. Since the hybrid approach cannot rely on the independence assumption that enables effective pruning during routing, naive use of the hybrid approach is costly. To address the resulting efficiency challenge, we propose an anytime routing algorithm that is able to return a "good enough" path at any time and that eventually computes a high-quality path. Empirical studies involving a substantial real-world trajectory set offer insight into the design properties of the proposed solution, indicating that it is practical in real-world settings.

Nearest neighbor (NN) search in high-dimensional spaces is inherently computationally expensive due to the curse of dimensionality. As a well-known solution to approximate NN search, locality-sensitive hashing (LSH) is able to answer c-approximate NN ( c -ANN) queries in sublinear time with constant probability. Existing LSH methods focus mainly on building hash bucket based indexing such that the candidate points can be retrieved quickly. However, existing coarse-grained structures fail to offer accurate distance estimation for candidate points, which translates into additional computational overhead when having to examine unnecessary points. This in turn reduces the performance of query processing. In contrast, we propose a fast and accurate LSH framework, called PM-LSH, that aims to compute the c -ANN query on large- scale, high-dimensional datasets. First, we adopt a simple yet effective PM-tree to index the data points. Second, we develop a tunable confidence interval to achieve accurate distance estimation and guarantee high result quality. Third, we propose an efficient algorithm on top of the PM-tree to improve the performance of computing c -ANN queries. Extensive experiments with real-world data offer evidence that PM-LSH is capable of outperforming existing proposals with respect to both efficiency and accuracy.

With the proliferation of geo-textual objects on the web, extensive efforts have been devoted to improving the efficiency of top-k spatial keyword queries in different settings. However, comparatively much less work has been reported on enhancing the quality and usability of such queries. In this context, we propose means of enhancing the usability of a top-k group spatial keyword query, where a group of users aim to find k objects that contain given query keywords and are nearest to the users. Specifically, when users receive the result of such a query, they may find that one or more objects that they expect to be in the result are in fact missing, and they may wonder why. To address this situation, we develop a so-called why-notquery that is able to minimally modifythe original query into a query that returns the expected, but missing, objects, in addition to other objects. Specifically, we formalize the why-not query in relation to the top-k group spatial keyword query, called the Why-not Group Spatial Keyword Query (WGSK) that is able to provide a group of users with a more satisfactory query result. We propose a three-phase framework for efficiently computing the WGSK. The first phase substantially reduces the search space for the subsequent phases by retrieving a set of objects that may affect the ranking of the user-expected objects. The second phase provides an incremental sampling algorithm that generates candidate weightings of more promising queries. The third phase determines the penalty of each refined query and returns the querywith minimal penalty, i.e., the minimally modified query. Extensive experiments with real and synthetic data offer evidence that the proposed solution excels over baselines with respect to both effectiveness and efficiency.

No abstract available.

A key service in vehicular transportation is routing according to estimated travel times. With the availability of massive volumes of vehicle trajectory data, it has become increasingly feasible to estimate travel times, which are typically modeled as probability distributions in the form of histograms. An earlier study shows that use of a carefully selected, context-dependent subset of available trajectories when estimating a travel-time histogram along a user-specified path can significantly improve the accuracy of the estimates. This selection of trajectories cannot occur in a pre-processing step, but must occur online—it must be integrated into the routing itself. It is then a key challenge to be able to select very efficiently the "right" subset of trajectories that offer the best accuracy when the cost of a route is to be assessed. To address this challenge, we propose a solution that applies novel indexing to all available trajectories and that then is capable of selecting the most relevant trajectories and of computing a travel-time distribution based on these trajectories. Specifically, the solution utilizes an in-memory trajectory index and a greedy algorithm to identify and retrieve the relevant trajectories. The paper reports on an extensive empirical study with a large real-world GPS data set that offers insight into the accuracy and efficiency of the proposed solution. The study shows that the proposed online selection of trajectories can be performed efficiently and is able to provide highly accurate travel-time distributions.

The application of machine learning techniques in the setting of road networks holds the potential to facilitate many important transportation applications. Graph Convolutional Networks (GCNs) are neural networks that are capable of leveraging the structure of a network. However, many implicit assumptions of GCNs do not apply to road networks.

The increasingly massive volumes of vehicle trajectory data that are becoming available hold the potential to enable more accurate vehicle travel-time estimation than hitherto possible. To enable such uses, we present a multi-threaded, in-memory trajectory store that supports efficient and accurate travel-time estimation for road-network paths based on network-constrained trajectories. The trajectory store employs advanced indexing to support so-called strict-path queries that retrieve all trajectories that traverse a given path to provide accurate travel-time estimations. As a key novel feature, the store is designed and implemented to exploit modern non-uniform memory access (NUMA) systems. We provide a detailed experimental study of the performance of the trajectory store using a synthetic trajectory data set based on real traffic data. The study shows that query latency can be halved compared to our baseline system.

Many datasets including social media data and bibliographic data can be modeled as graphs. Clustering such graphs is able to provide useful insights into the structure of the data. To improve the quality of clustering, node attributes can be taken into account, resulting in attributed graphs. Existing attributed graph clustering methods generally consider attribute similarity and structural similarity separately. In this paper, we represent attributed graphs as star-schema heterogeneous graphs, where attributes are modeled as different types of graph nodes. This enables the use of personalized pagerank (PPR) as a unified distance measure that captures both structural and attribute similarity. We employ DBSCAN for clustering, and we update edge weights iteratively to balance the importance of different attributes. To improve the efficiency of the clustering, we develop two incremental approaches that aim to enable efficient PPR score computation when edge weights are updated. To boost the effectiveness of the clustering, we propose a simple yet effective edge weight update strategy based on entropy. In addition, we present a game theory based method that enables trading efficiency for result quality. Extensive experiments on real-life datasets offer insight into the effectiveness and efficiency of our proposals, compared with existing methods.

Innovations in transportation, such as mobility-on-demand services and autonomous driving, call for high-resolution routing that relies on an accurate representation of travel time throughout the underlying road network. Specifically, the travel time of a road-network edge is modeled as a time-varying distribution that captures the variability of traffic over time and the fact that different drivers may traverse the same edge at the same time at different speeds. Such stochastic weights may be extracted from data sources such as GPS and loop detector data. However, even very large data sources are incapable of covering all edges of a road network at all times. Yet, high-resolution routing needs stochastic weights for all edges. We solve the problem of filling in the missing weights. To achieve that, we provide techniques capable of estimating stochastic edge weights for all edges from traffic data that covers only a fraction of all edges. We propose a generic learning framework called Graph Convolutional Weight Completion (GCWC) that exploits the topology of a road network graph and the correlations of weights among adjacent edges to estimate stochastic weights for all edges. Next, we incorporate contextual information into GCWC to further improve accuracy. Empirical studies using loop detector data from a highway toll gate network and GPS data from a large city offer insight into the design properties of GCWC and its effectiveness.

Consider the task of selecting a set of facilities, e.g., hotspots, shops, or utility stations, each with a capacity to serve a certain number of customers. Given a set of customer locations, we have to minimize a cumulative distance between each customer and the facility earmarked to serve this customer within its capacity. This problem is known as the Capacitated k-Median (CKM) problem. In a data-intensive variant, distances are calculated over a network, while a data set associates each candidate facility location with a different capacity. In other words, going beyond positioning facilities in a metric space, the problem is to select a small subset out of a large data set of candidate network-based facilities with capacity constraints. We call this variant the Multicapacity Facility Selection (MCFS) problem. Linear Programming solutions are unable to contend with the network sizes and supplies of candidate facilities encountered in real-world applications; yet the problem may need to be solved scalably and repeatedly, as in applications requiring the dynamic reallocation of customers to facilities. We present the first, to our knowledge, solution to the MCFS problem that achieves both scalability and high quality, the Wide Matching Algorithm (WMA). WMA iteratively assigns customers to candidate facilities and leverages a data-driven heuristic for the SETCOVER problem inherent to the MCFS problem. An extensive experimental study with real-world and synthetic networks demonstrates that WMA scales gracefully to million-node networks and large facility and customer data sets; further, WMA provides a solution quality superior to scalable baselines (also proposed in the paper) and competitive vis-á-vis the optimal solution, returned by an off-the-shelf solver that runs only on small facility databases.

With the proliferation of geo-textual objects on the web, extensive efforts have been devoted to improving the efficiency of top-k spatial keyword queries in different settings. However, comparatively much less work has been reported on enhancing the quality and usability of such queries. In this context, we propose means of enhancing the usability of a top-k group spatial keyword query, where a group of users aim to find k objects that contain given query keywords and are nearest to the users. Specifically, when users receive the result of such a query, they may find that one or more objects that they expect to be in the result are in fact missing, and they may wonder why. To address this situation, we develop a so-called why-not query that is able to minimally modify the original query into a query that returns the expected, but missing, objects, in addition to other objects. Specifically, we formalize the why-not query in relation to the top-k group spatial keyword query, called the Why-not Group Spatial Keyword Query (WGSK) that is able to provide a group of users with a more satisfactory query result. We propose a three-phase framework for efficiently computing he WGSK. Extensive experiments with real and synthetic data offer evidence that the proposed solution excels over baselines with respect to both effectiveness and efficiency.

With the increasing availability of moving-object tracking data, use of this data for route search and recommendation is increasingly important. To this end, we propose a novel parallel split-and-combine approach to enable route search by locations (RSL-Psc). Given a set of routes, a set of places to visit O, and a threshold θ, we retrieve the route composed of sub-routes that (i) has similarity to O no less than θ and (ii) contains the minimum number of sub-route combinations. The resulting functionality targets a broad range of applications, including route planning and recommendation, ridesharing, and location-based services in general. To enable efficient and effective RSL-Psc computation on massive route data, we develop novel search space pruning techniques and enable use of the parallel processing capabilities of modern processors. Specifically, we develop two parallel algorithms, Fully-Split Parallel Search (FSPS) and Group-Split Parallel Search (GSPS). We divide the route split-and-combine task into ∑k=0 M S(|O|,k+1) sub-tasks, where M is the maximum number of combinations and S(⋅) is the Stirling number of the second kind. In each sub-task, we use network expansion and exploit spatial similarity bounds for pruning. The algorithms split candidate routes into sub-routes and combine them to construct new routes. The sub-tasks are independent and are performed in parallel. Extensive experiments with real data offer insight into the performance of the algorithms, indicating that our RSL-Psc problem can generate high-quality results and that the two algorithms are capable of achieving high efficiency and scalability.

Large vehicle trajectory data sets can give detailed insight into traffic and congestion that is useful for routing as well as transportation planning. Making information from such data sets available to more users can enable applications that reduce travel time and fuel consumption. However, extracting such information efficiently requires deep knowledge of the underlying schema and indexing methods. To enable more users to extract information from trajectory data, we have developed an API that removes the need to be familiar with the schema. Furthermore, when giving access to trajectory data, privacy concerns often call for the application of anonymization methods before analysis results are made available. In our demonstration, owners of trajectory data are able to experiment with different levels of anonymization to see how this affects the quality of different types of trajectory analysis services implemented on top of a large trajectory data set.

With the widespread deployment of mobile devices with positioning capabilities, increasingly massive volumes of trajectory data are being collected that capture the movements of people and vehicles. This data enables co-movement pattern detection, which is important in applications such as trajectory compression and future-movement prediction. Existing co-movement pattern detection studies generally consider historical data and thus propose offline algorithms. However, applications such as future movement prediction need real-time processing over streaming trajectories. Thus, we investigate real-time distributed co-movement pattern detection over streaming trajectories. Existing off-line methods assume that all data is available when the processing starts. Nevertheless, in a streaming setting, unbounded data arrives in real time, making pattern detection challenging. To this end, we propose a framework based on Apache Flink, which is designed for efficient distributed streaming data processing. The framework encompasses two phases: clustering and pattern enumeration. To accelerate the clustering, we use a range join based on two-layer indexing, and provide techniques that eliminate unnecessary verifications. To perform pattern enumeration efficiently, we present two methods FBA and VBA that utilize id-based partitioning. When coupled with bit compression and candidate-based enumeration techniques, we reduce the enumeration cost from exponential to linear. Extensive experiments offer insight into the efficiency of the proposed framework and its constituent techniques compared with existing methods.

With the proliferation of mobile devices and location-based services, increasingly massive volumes of geo-tagged data are becoming available. This data typically also contains non-location information. We study how to use such information to characterize a region and then how to find a region of the same size and with the most similar characteristics. This functionality enables a user to identify regions that share characteristics with a user-supplied region that the user is familiar with and likes. More specifically, we formalize and study a new problem called the attribute-aware similar region search ( ASRS ) problem. We first define so-called composite aggregators that are able to express aspects of interest in terms of the information associated with a user-supplied region. When applied to a region, an aggregator captures the region's relevant characteristics. Next, given a query region and a composite aggregator, we propose a novel algorithm called DS-Search to find the most similar region of the same size. Unlike any previous work on region search, DS-Search repeatedly discretizes and splits regions until an split region either satisfies a drop condition or it is guaranteed to not contribute to the result. In addition, we extend DS-Search to solve the ASRS problem approximately. Finally, we report on extensive empirical studies that offer insight into the efficiency and effectiveness of the paper's proposals.

Data cleaning is a prerequisite to subsequent data analysis, and is know to often be time-consuming and labor-intensive. We present IHCS, a hybrid data cleaning system that integrates error detection and repair to contend effectively with multiple error types. In a preprocessing step that precedes the data cleaning, IHCS formats an input dataset to be cleaned, and transforms applicable data quality rules into a unified format. Then, an MLN index structure is formed according to the unified rules, enabling IHCS to handle multiple error types simultaneously. During the cleaning, IHCS first tackles abnormalities through an abnormal group process, and then, it generates multiple data versions based on the MLN index. Finally, IHCS eliminates conflicting values across the multiple versions, and derives the final unified clean data. A visual interface enables cleaning process monitoring and cleaning result analysis.

The matching between trajectories and locations, called Trajectory-to-Location join (TL-Join), is fundamental functionality in spatiotemporal data management. Given a set of trajectories, a set of locations, and a threshold 8, the TL-Join finds all (trajectory, location) pairs from the two sets with spatiotemporal correlation above 8. This join targets diverse applications, including location recommendation, event tracking, and trajectory activity analyses. We address three challenges in relation to the TL-Join: how to define the spatiotemporal correlation between trajectories and locations, how to prune the search space effectively when computing the join, and how to perform the computation in parallel. Specifically, we define new metrics to measure the spatiotemporal correlation between trajectories and locations. We develop a novel parallel collaborative (PCol) search method based on a divide-and-conquer strategy. For each location o, we retrieve the trajectories with high spatiotemporal correlation to o, and then we merge the results. An upper bound on the spatiotemporal correlation and a heuristic scheduling strategy are developed to prune the search space. The trajectory searches from different locations are independent and are performed in parallel, and the result merging cost is independent of the degree of parallelism. Studies of the performance of the developed algorithms using large spatiotemporal data sets are reported.

Road networks are a type of spatial network, where edges may be associated with qualitative information such as road type and speed limit. Unfortunately, such information is often incomplete; for instance, OpenStreetMap only has speed limits for 13% of all Danish road segments. This is problematic for analysis tasks that rely on such information for machine learning. To enable machine learning in such circumstances, one may consider the application of network embedding methods to extract structural information from the network. However, these methods have so far mostly been used in the context of social networks, which differ significantly from road networks in terms of, e.g., node degree and level of homophily (which are key to the performance of many network embedding methods).We analyze the use of network embedding methods, specifi-cally node2vec, for learning road segment embeddings in road networks. Due to the often limited availability of information on other relevant road characteristics, the analysis focuses on leveraging the spatial network structure. Our results suggest that network embedding methods can indeed be used for deriving relevant network features (that may, e.g, be used for predicting speed limits), but that the qualities of the embeddings differ from embeddings for social networks.

We consider a scenario that occurs often in the auto insurance industry. We are given a large collection of trajectories that stem from many different drivers. Only a small number of the trajectories are labeled with driver identifiers, and only some drivers are used in labels. The problem is to label correctly the unlabeled trajectories with driver identifiers. This is important in auto insurance to detect possible fraud and to identify the driver in, e.g., pay-as-you-drive settings when a vehicle has been involved in an incident. To solve the problem, we first propose a Trajectory-to-Image( T2I) encoding scheme that captures both geographic features and driving behavior features of trajectories in 3D images. Next, we propose a multi-task, deep learning model called T2INet for estimating the total number of drivers in the unlabeled trajectories, and then we partition the unlabeled trajectories into groups so that the trajectories in a group belong to the same driver. Experimental results on a large trajectory data set offer insight into the design properties of T2INet and demonstrate that T2INet is capable of outperforming baselines and the state-of-the-art method.

Massive amount of data that contain spatial, textual, and temporal information are being generated at a high scale. These spatio-temporal documents cover a wide range of topics in local area. Users are interested in receiving local popular terms from spatio-temporal documents published with a specified region. We consider the Top-k Spatial-Temporal Term (ST2) Subscription. Given an ST2 subscription, we continuously maintain up-to-date top-k most popular terms over a stream of spatio-temporal documents. The ST2 subscription takes into account both frequency and recency of a term generated from spatio-temporal document streams in evaluating its popularity. We propose an efficient solution to process a large number of ST2 subscriptions over a stream of spatio-temporal documents. The performance of processing ST2 subscriptions is studied in extensive experiments based on two real spatio-temporal datasets.

Ridesharing refers to a transportation scenario where travellers with similar itineraries and time schedules share a vehicle for a trip and split the travel cost, which may include fuel, tolls, and parking fees. Ridesharing is popular among travellers because it can reduce their travel costs, and it also holds the potential to reduce travel time, congestion, air pollution, and overall fuel consumption. However, existing ridesharing systems often offer each traveller only one choice that aims to minimize system-wide vehicle travel distance or time. We propose a solution that offers more options. Specifically, we do this by considering both pick-up time and price, so that travellers are able to choose the vehicle that matches their preferences best. In order to identify quickly vehicles that satisfy incoming ridesharing requests, we propose two efficient matching algorithms that follow the single-side and dual-side search paradigms, respectively. To further accelerate the matching, indexes on the road network and vehicles are developed, based on which several pruning heuristics are designed. Extensive experiments on a large Shanghai taxi dataset offer insights into the performance of our proposed techniques and compare with a baseline that extends the state-of-the art method.

Motivated by the increasing availability of vehicle trajectory data, we propose learn-to-route, a comprehensive trajectory-based routing solution. Specifically, we first construct a graph-like structure from trajectories as the routing infrastructure. Second, we enable trajectory-based routing given an arbitrary (source, destination) pair. In the first step, given a road network and a collection of trajectories, we propose a trajectory-based clustering method that identifies regions in a road network. If a pair of regions are connected by trajectories, we maintain the paths used by these trajectories and learn a routing preference for travel between the regions. As trajectories are skewed and sparse, %and although the introduction of regions serves to consolidate the sparse data, many region pairs are not connected by trajectories. We thus transfer routing preferences from region pairs with sufficient trajectories to such region pairs and then use the transferred preferences to identify paths between the regions. In the second step, we exploit the above graph-like structure to achieve a comprehensive trajectory-based routing solution. Empirical studies with two substantial trajectory data sets offer insight into the proposed solution, indicating that it is practical. A comparison with a leading routing service offers evidence that the paper's proposal is able to enhance routing quality.

Trajectory similarity computation is fundamental functionality with many applications such as animal migration pattern studies and vehicle trajectory mining to identify popular routes and similar drivers. While a trajectory is a continuous curve in some spatial domain, e.g., 2D Euclidean space, trajectories are often represented by point sequences. Existing approaches that compute similarity based on point matching suffer from the problem that they treat two different point sequences differently even when the sequences represent the same trajectory. This is particularly a problem when the point sequences are non-uniform, have low sampling rates, and have noisy points. We propose the first deep learning approach to learning representations of trajectories that is robust to low data quality, thus supporting accurate and efficient trajectory similarity computation and search. Experiments show that our method is capable of higher accuracy and is at least one order of magnitude faster than the state-of-the-art methods for k-nearest trajectory search.

Owing to the widespread use of location-aware devices and the increased popularity of micro-blogging applications, we are witnessing a rapid proliferation of geo-textual data. In this demonstration, we present iZone, an efficient system for determining influence zones over geo-textual data. Specifically, iZone allows users to browse geo-textual objects, evaluate the influence zones of specified geo-textual objects, and obtain explanations of the evaluation results. The iZone system adopts a browser-server model. The server side integrates two types of spatial keyword search, namely top-k spatial keyword query and reverse top-k keyword-based location query, to support the functionality of the system. A variety of spatial indexes are employed to enhance the efficiency of the system. The browser side provides a map-based GUI interface, which enables convenient and user-friendly interaction with the system. Using a real hotel dataset from Hong Kong, iZone offers hands-on experience with influence zone evaluation in real-life applications.

We propose and investigate a novel query type named trajectory search by regions of interest (TSR query). Given an argument set of trajectories, a TSR query takes a set of regions of interest as a parameter and returns the trajectory in the argument set with the highest spatial-density correlation to the query regions. This type of query is useful in applications such as trip planning and recommendation. To process the TSR query, a set of new metrics are defined to model spatial-density correlations. An efficient trajectory search algorithm is developed that exploits upper and lower bounds to prune the search space and that adopts a query-source selection strategy, as well as integrates a heuristic search strategy based on priority ranking to schedule multiple query sources. The performance of TSR query processing is studied in extensive experiments based on real and synthetic spatial data.

Massive trajectory data is being collected and used widely in many applications such as transportation, location-based services, and urban computing. As a result, abundant methods and systems have been proposed for managing and processing trajectory data. However, it remains difficult for users to interact well with data management and processing, due to the lack of efficient data processing methods and effective visualization techniques for big trajectory data. In this demonstration, we present a new framework, VIPTRA, to process big trajectory data visually and interactively. VIPTRA builds upon UlTraMan, a distributed in-memory system for big trajectory data, and thus, it takes advantage of its capability of high performance. The demonstration shows the efficiency of data processing and user-friendly visualization and interaction techniques provided in VIPTRA, via several scenarios of visual analysis and trajectory editing tasks.

Due to the continued digitization of industrial and societal processes, including the deployment of networked sensors, we are witnessing a rapid proliferation of time-ordered observations, known as time series. For example, the behavior of drivers can be captured by GPS or accelerometer as a time series of speeds, directions, and accelerations. We propose a framework for outlier detection in time series that, for example, can be used for identifying dangerous driving behavior and hazardous road locations. Specifically, we first propose a method that generates statistical features to enrich the feature space of raw time series. Next, we utilize an autoencoder to reconstruct the enriched time series. The autoencoder performs dimensionality reduction to capture, using a small feature space, the most representative features of the enriched time series. As a result, the reconstructed time series only capture representative features, whereas outliers often have non-representative features. Therefore, deviations of the enriched time series from the reconstructed time series can be taken as indicators of outliers. We propose and study autoencoders based on convolutional neural networks and long-short term memory neural networks. In addition, we show that embedding of contextual information into the framework has the potential to further improve the accuracy of identifying outliers. We report on empirical studies with multiple time series data sets, which offers insight into the design properties of the proposed framework, indicating that it is effective at detecting outliers.

Travel-time estimation for paths in a road network often relies on pre-computed histograms that are usually available on a road segment level. Then the pre-computed histograms of the segments of a path are convolved to obtain a histogram that estimates the travel time. With the growing sizes of trajectory datasets, it becomes possible to compute histograms for increasingly longer sub-paths. Since pre-computation is infeasible for all sub-paths in a road network, we propose computing histograms on-the-fly, i.e., during routing. Such an on-the-fly method must filter the underlying trajectory dataset by spatio-temporal predicates to obtain the relevant trajectories and offers the opportunity to apply additional filtering predicates to the trajectories with little overhead. We report on a study showing that considerable improvements in accuracy of the histograms obtained for paths can be obtained by choosing filtering predicates that not only adapt to the intended start of a trip, but also to the driver and the weather. We also make the cases for a sub-path partitioning based on segment categories since there are significant differences between road types when applying our on-the-fly method.

Ridesharing is popular among travellers because it can reduce their travel costs, and it also holds the potential to reduce travel time, congestion, air pollution, and overall fuel consumption. Existing ridesharing systems (e.g., lyft, uberPOOL) often offer each traveler only one choice that aims to minimize system-wide vehicle travel distance or time. In this demonstration, we present a price-and-time-aware ridesharing system, termed as PTRider, which provides more options. It considers both pick-up time and price, so that travellers are able to choose the vehicle matching their preferences best. To answer the ridesharing request in real time, PTRider builds indexes on the road network and vehicles separately, and utilizes corresponding efficient matching methods. A real-life dataset that contains 432,327 trips extracted from 17,000 Shanghai taxis for one day (May 29, 2009) is used to demonstrate that PTRider can return various options for every ridesharing request in real time.

Massive trajectory data is being generated by GPS-equipped devices, such as cars and mobile phones, which is used increasingly in transportation, location-based services, and urban computing. As a result, a variety of methods have been proposed for trajectory data management and analytics. However, traditional systems and methods are usually designed for very specific data management or analytics needs, which forces users to stitch together heterogeneous systems to analyze trajectory data in an inefficient manner. Targeting the overall data pipeline of big trajectory data management and analytics, we present a unified platform, termed as UlTraMan. In order to achieve scalability, efficiency, persistence , and flexibility , (i) we extend Apache Spark with respect to both data storage and computing by seamlessly integrating a key-value store, and (ii) we enhance the MapReduce paradigm to allow flexible optimizations based on random data access. We study the resulting system's flexibility using case studies on data retrieval, aggregation analyses, and pattern mining. Extensive experiments on real and synthetic trajectory data are reported to offer insight into the scalability and performance of UlTraMan.

With the continued proliferation of location-based services, a growing number of web-accessible data objects are geo-tagged and have text descriptions. An important query over such web objects is thedirection-aware spatial keyword querythat aims to retrieve the top-$k$objects that best match query parameters in terms of spatial distance and textual similarity in a given query direction. In some cases, it can be difficult for users to specify appropriate query parameters. After getting a query result, users may find some desired objects are unexpectedly missing and may therefore question the entire result. Enabling why-not questions in this setting may aid users to retrieve better results, thus improving the overall utility of the query functionality. This paper studies the direction-aware why-not spatial keyword top-$k$query problem. We propose efficient query refinement techniques to revive missing objects by minimally modifying users’ direction-aware queries. We prove that the best refined query directions lie in a finite solution space for a special case and reduce the search for the optimal refinement to a linear programming problem for the general case. Extensive experimental studies demonstrate that the proposed techniques outperform a baseline method by two orders of magnitude and are robust in a broad range of settings.

No abstract available.

Increasingly large volumes of vehicle trajectory data are becoming available. This data holds the potential to offer detailed insight into important aspects of vehicular transportation and road networks. This insight can in turn be utilized to enable a range of important services. Specifically, we demonstrate a system that is capable of leveraging very large collections of GPS trajectories for enabling interactive analyses of traffic in road intersections, which are often bottlenecks in road networks. These analyses are able to provide detailed insight into the time-varying functioning of intersections, and they offer a solid, data-driven foundation for improving the capacity of intersections and the overall road network. The system enables more cost-effective analyses than what is possible with traditional techniques. Demonstration participants will gain first-hand experience with interactive analyses on top of a database of some 40 billion GPS records capturing more than a billion km of driving.

With the continued proliferation of location-based services, a growing number of web-accessible data objects are geotagged and have text descriptions. An important query over such web objects is the direction-aware spatial keyword query that aims to retrieve the top-k objects that best match query parameters in terms of spatial distance and textual similarity in a given query direction. In some cases, it can be difficult for users to specify appropriate query parameters. After getting a query result, users may find some desired objects are unexpectedly missing and may therefore question the entire result. Enabling why-not questions in this setting may aid users to retrieve better results, thus improving the overall utility of the query functionality. This paper studies the direction-aware why-not spatial keyword top-k query problem. We propose efficient query refinement techniques to revive missing objects by minimally modifying users' directionaware queries. Experimental studies demonstrate the efficiency and effectiveness of the proposed techniques.

We propose and investigate a novel query, the Collective Travel Planning (CTP) query, that finds the lowest-cost route connecting multiple query sources and a destination via at most k meeting points. This type of query is useful in organizing large events, and it can bring significant benefits to society and the environment: it can help optimize the allocation of transportation resources, reduce resource consumption, and enable smarter and greener transportation; and it can help reduce greenhouse-gas emissions and traffic congestion.

The proliferation of geo-textual data gives prominence to spatial keyword search. The basic top-k spatial keyword query, returns k geo-textual objects that rank the highest according to their textual relevance and spatial proximity to query keywords and a query location. We define, study, and provide means of computing the reverse top-k keyword-based location query. This new type of query takes a set of keywords, a query object q, and a number k as arguments, and it returns a spatial region such that any top-k spatial keyword query with the query keywords and a location in this region would contain object q in its result. This query targets applications in market analysis, geographical planning, and location optimization, and it may support applications related to safe zones and influence zones that are used widely in location-based services. We show that computing an exact query result requires evaluating and merging a set of weighted Voronoi cells, which is expensive. We therefore devise effective algorithms that approximate result regions with quality guarantees. We develop novel pruning techniques on top of an index, and we offer a series of optimization techniques that aim to further accelerate query processing. Empirical studies suggest that the proposed query processing is efficient and scalable.

Identifying prospective customers is an important aspect of marketing research. In this paper, we provide support for a new type of query, the Reverse Top-k Geo-Social Keyword (RkGSK) query. This query takes into account spatial, textual, and social information, and finds prospective customers for geotagged objects. As an example, a restaurant manager might apply the query to find prospective customers. To address this, we propose a hybrid index, the GIM-tree, which indexes locations, keywords, and social information of geo-tagged users and objects, and then, using the GIM-tree, we present efficient RkGSK query processing algorithms that exploit several pruning strategies. The effectiveness of RkGSK retrieval is characterized via a case study, and extensive experiments using real datasets offer insight into the efficiency of the proposed index and algorithms.

Spatial keyword querying has attracted considerable research efforts in the past few years. A prototypical query takes a location and keywords as arguments and returns the k objects that score the highest according to a ranking function. While different scoring functions have been used, how to compare different ranking functions for spatial keyword querying still remains an open question with little investigation. We propose a crowdsourcing-based approach to evaluate and compare ranking functions for spatial keyword search. Given two ranking functions f1 and f2, we use a matrix to model all possible binary questions regarding the different results produced by f1 and f2. We propose a multi-step process to reduce the number of binary questions, identifying the most important questions to ask. Further, we design a crowdsourcing model that obtains the answers to those important binary questions from crowd workers. We also devise a global evaluation process that is able to quantitatively compare f1 and f2 based on a multitude of answers received. According to the results of empirical studies using real data, the proposed approach is efficient and able to draw reliable conclusions in comparing ranking functions for spatial keyword search.

Today's one-size-fits-all approach to travel-time computation in spatial networks proceeds in two steps. In a preparatory off-line step, a set of distributions, e.g., one per hour of the day, is computed for each network segment. Then, when a path and a departure time are provided, a distribution for the path is computed on-line from pertinent pre-computed distributions. Motivated by the availability of massive trajectory data from vehicles, we propose a completely on-line approach, where distributions are computed from trajectories on-the-fly, i.e., when a query arrives. This new approach makes it possible to use arbitrary sets of underlying trajectories for a query. Specifically, we study the potential for accuracy improvements over the one-size-fits-all approach that can be obtained using the on-the-fly approach and report findings from an empirical study that suggest that the on-the-fly approach is able to improve accuracy significantly and has the potential to replace the current one-size-fits-all approach.

The general notion of a metric space encompasses a diverse range of data types and accompanying similarity measures. Hence, metric search plays an important role in a wide range of settings, including multimedia retrieval, data mining, and data integration. With the aim of accelerating metric search, a collection of pivot-based indexing techniques for metric data has been proposed, which reduces the number of potentially expensive similarity comparisons by exploiting the triangle inequality for pruning and validation. However, no comprehensive empirical study of those techniques exists. Existing studies each offers only a narrower coverage, and they use different pivot selection strategies that affect performance substantially and thus render cross-study comparisons difficult or impossible. We offer a survey of existing pivot-based indexing techniques, and report a comprehensive empirical comparison of their construction costs, update efficiency, storage sizes, and similarity search performance. As part of the study, we provide modifications for two existing indexing techniques to make them more competitive. The findings and insights obtained from the study reveal different strengths and weaknesses of different indexing techniques, and offer guidance on selecting an appropriate indexing technique for a given setting.

Along with the proliferation of online digital map and location-based service, very large POI (point of interest) databases have been constructed where a record corresponds to a POI with information including name, category, address, geographical location and other features. A basic spatial query in POI database is POI retrieval. In many scenarios, a user cannot provide enough information to pinpoint the POI except some clue. For example, a user wants to identify a caf é in a city visited many years ago. SHe cannot remember the name and address but she still recalls that "the caf é is about 200 meters away from a restaurant; and turning left at the restaurant there is a bakery 500 meters away, etc.". Intuitively, the clue, even partial and approximate, describes the spatio-textual context around the targeted POI. Motivated by this observation, this work investigates clue-based spatio-textual query which allows user providing clue, i.e., some nearby POIs and the spatial relationships between them, in POI retrieval. The objective is to retrieve k POIs from a POI database with the highest spatio-textual context similarities against the clue. This work has deliberately designed data-quality-tolerant spatio-textual context similarity metric to cope with various data quality problems in both the clue and the POI database. Through crossing valuation, the query accuracy is further enhanced by ensemble method. Also, this work has developed an index called roll-out-star R-tree (RSR-tree) to dramatically improve the query processing efficiency. The extensive tests on data sets from the real world have verified the superiority of our methods in all aspects.

The matching of similar pairs of objects, called similarity join, is fundamental functionality in data management. We consider the case of trajectory similarity join (TS-Join), where the objects are trajectories of vehicles moving in road networks. Thus, given two sets of trajectories and a threshold θ , the TS-Join returns all pairs of trajectories from the two sets with similarity above θ . This join targets applications such as trajectory near-duplicate detection, data cleaning, ridesharing recommendation, and traffic congestion prediction. With these applications in mind, we provide a purposeful definition of similarity. To enable efficient TS-Join processing on large sets of trajectories, we develop search space pruning techniques and take into account the parallel processing capabilities of modern processors. Specifically, we present a two-phase divide-and-conquer algorithm. For each trajectory, the algorithm first finds similar trajectories. Then it merges the results to achieve a final result. The algorithm exploits an upper bound on the spatiotemporal similarity and a heuristic scheduling strategy for search space pruning. The algorithm's per-trajectory searches are independent of each other and can be performed in parallel, and the merging has constant cost. An empirical study with real data offers insight in the performance of the algorithm and demonstrates that is capable of outperforming a well-designed baseline algorithm by an order of magnitude.

Spatial queries including similarity search and similarity joins are useful in many areas, such as multimedia retrieval, data integration, and so on. However, they are not supported well by commercial DBMSs. This may be due to the complex data types involved and the needs for flexible similarity criteria seen in real applications. In this paper, we propose a versatile and efficient disk-based index for metric data, the Space-fillingcurve and Pivot-based B+-tree (SPB-tree). This index leverages the B+-tree, and uses space-filling curve to cluster data into compact regions, thus achieving storage efficiency. It utilizes a small set of so-called pivots to reduce significantly the number of distance computations when using the index. Further, it makes use of a separate random access file to support abroad range of data. By design, it is easyto integrate the SPB-tree into an existing DBMS. We present efficient algorithms for processing similarity search and similarity joins, as well as corresponding cost models based on SPB-trees. Extensive experiments using both real and synthetic data show that, compared with state-of-the-art competitors, the SPB-tree has much lower construction cost, smallerstorage size, and supports more efficient similarity search and similarity joins with high accuracy cost models.

With the increasing availability of moving-object tracking data, trajectory search is increasingly important. We propose and investigate a novel query type named trajectory search by regions of interest (TSR query). Given an argument set of trajectories, a TSR query takes a set of regions of interest as a parameter and returns the trajectory in the argument set with the highest spatial-density correlation to the query regions. This type of query is useful in many popular applications such as trip planning and recommendation, and location based services in general. TSR query processing faces three challenges: how to model the spatial-density correlation between query regions and data trajectories, how to effectively prune the search space, and how to effectively schedule multiple so-called query sources. To tackle these challenges, a series of new metrics are defined to model spatial-density correlations. An efficient trajectory search algorithm is developed that exploits upper and lower bounds to prune the search space and that adopts a query-source selection strategy, as well as integrates a heuristic search strategy based on priority ranking to schedule multiple query sources. The performance of TSR query processing is studied in extensive experiments based on real and synthetic spatial data.

No abstract available.

Spatial keyword queries retrieve spatial textual objects that are near a query location and are relevant to query keywords. The paper defines the top-k spatial textual clusters (k-STC) query that returns the top-k clusters that are located close to a given query location, contain relevant objects with regard to given query keywords, and have an object density that exceeds a given threshold. This query aims to support users who wish to explore nearby regions with many relevant objects. To compute this query, the paper proposes a basic and an advanced algorithm that rely on on-line density-based clustering. An empirical study offers insight into the performance properties of the proposed algorithms.

Indoor tracking data is being amassed due to the deployment of indoor positioning technologies. Analysing such data discloses useful insights that are otherwise hard to obtain. For example, by studying tracking data from an airport, we can identify the shops and restaurants that are most popular among passengers. In this paper, we study two query types for finding frequently visited Points of Interest (POIs) from symbolic indoor tracking data. The snapshot query finds those POIs that were most frequently visited at a given time point, whereas the interval query finds such POIs for a given time interval. A typical example of symbolic tracking is RFID-based tracking, where an object with an RFID tag is detected by an RFID reader when the object is in the reader’s detection range. A symbolic indoor tracking system deploys a limited number of proximity detection devices, like RFID readers, at preselected locations, covering only part of the host indoor space. Consequently, symbolic tracking data is inherently uncertain and only enables the discrete capture of the trajectories of indoor moving objects in terms of coarse regions. We provide uncertainty analyses of the data in relation to the two kinds of queries. The outcomes of the analyses enable us to design processing algorithms for both query types. An experimental evaluation with both real and synthetic data suggests that the framework and algorithms enable efficient and scalable query processing.

With the rapidly growing availability of vehicle trajectory data, travel costs such as travel time and fuel consumption can be captured accurately as distributions (e.g., travel time distributions) instead of deterministic values (e.g., average travel times). We study a new path finding problem in uncertain road networks, where paths have travel cost distributions. Given a source and a destination, we find optimal, non-dominated paths connecting the source and the destination, where the optimality is defined in terms of the stochastic dominance among cost distributions of paths. We first design an A based framework that utilizes the uncertain graph to obtain the most accurate cost distributions while finding the candidate paths. Next, we propose a three-stage dominance examination method that employs extreme values in each candidate path's cost distribution for early detection of dominated paths, thus reducing the need for expensive distributions convolutions. We conduct extensive experiments using real world road network and trajectory data. The results show that our algorithm outperforms baseline algorithms by up to two orders of magnitude in terms of query response time while achieving the most accurate results.

With the decreasing cost and growing size of main memory, it is increasingly relevant to utilize main-memory indexing for efficient query processing. We propose SPNET, which we believe is the first in-memory index for network-constrained trajectory data. To exploit the main-memory setting SPNET exploits efficient shortest-path compression of trajectories to achieve a compact index structure. SPNET is capable of exploiting the parallel computing capabilities of modern machines and supports both intra- and inter-query parallelism. The former improves response time, and the latter improves throughput. By design, SPNET supports a wider range of query types than any single existing index. An experimental study in a real-world setting with 1.94 billion GPS records and nearly 4 million trajectories in a road network with 1.8 million edges indicates that SPNET typically offers performance improvements over the best existing indexes of 1.5 to 2 orders of magnitude.

Metric probabilistic range queries (MPRQ) have received substantial attention due to their utility in multimedia and text retrieval, decision making, etc. Existing MPRQ studies generally aim to improve query efficiency and resource usage. In contrast, we define and offer solutions to why-not questions on MPRQ. Given an original metric probabilistic range query and a why-not set W of uncertain objects that are absent from the query result, a why-not question on MPRQ explains why the uncertain objects in W do not appear in the query result, and provides refinements of the original query and/or W with the minimal penalty, so that the uncertain objects in W appear in the result of the refined query. Specifically, we propose a framework that consists of three efficient solutions, one that modifies the original query, one that modifies the why-not set, and one that modifies both the original query and the why-not set. Extensive experiments using both real and synthetic data sets offer insights into the properties of the proposed algorithms, and show that they are effective and efficient.

Web objects, often associated with descriptive text documents, are increasingly being geo-tagged. A spatial keyword top-k query retrieves the best k such objects according to a scoring function that considers both spatial distance and textual similarity. However, it is in some cases difficult for users to identify the exact keywords that describe their query intent. After a user issues an initial query and gets back the result, the user may find that some expected objects are missing and may wonder why. Answering the resulting why-not questions can aid users in retrieving better results. However, no existing techniques are able to answer why-not questions by adapting the query keywords. We propose techniques capable of adapting an initial set of query keywords so that expected, but missing, objects enter the result along with other relevant objects. We develop a basic algorithm with a set of optimizations that sequentially examines a sequence of candidate keyword sets. In addition, we present an index-based bound-and-prune algorithm that is able to determine the best sample out of a set of candidates in just one pass of index traversal, thus speeding up the query processing. We also extend the proposed algorithms to handle multiple missing objects. Extensive experimental results offer insight into the efficiency of the proposed techniques in terms of running time and I/O cost.

We study safe zones for service subscriptions in a volunteered geographic service setting, covering the concepts, properties, and algorithms needed for the use of weighted imprecise Voronoi cells as safe zones. Empirical performance studies on both synthetic and real datasets offer insights into the efficiency and scalability of our proposal. For a comprehensive coverage, see the full version of the paper.

Congestion is a major problem in many cities. In order to monitor and manage traffic, a number of different sensor types are used to collect traffic data. This includes GPS devices in the vehicles themselves as well as fixed Bluetooth sensors along the roads. Each sensor type has advantages and disadvantages. Where GPS has a wide coverage of the road network, Bluetooth sensors gather data from a much higher number of vehicles. In this paper we present Fog BAT, a system that combines GPS data with Bluetooth data. The goal of the system is to retain the advantages of both. We show how the data types are aligned to ensure that data from each sensor type is related to the exact same part of the road network and cover the same time period. Using very large real-world data sets, we use the system to compare travel speeds based on each data type, and how the use of both data types simultaneously can improve the accuracy of computed travel speeds and congestion levels.

We demonstrate Crowd Rank Eval, a novel framework for the evaluation of ranking functions for top-k spatial keyword queries. The framework enables researchers to study hypotheses regarding ranking functions. Crowd Rank Eval uses crowd sourcing for synthesizing results to top-k queries and is able to visualize the results and to compare them to the results obtained from ranking functions, thus offering insight into the ranking functions.

Over the past decade, we have moved from a predominantly desktop based web to a predominantly mobile web, where users most often access the web from mobile devices such as smartphones. In addition, we are witnessing a proliferation of geo-located, textual web content. Motivated in part by these developments, the research community has been hard at work enabling the efficient computation of a variety of query functionality on geo-textual data, yielding a sizable body of literature on the querying of geo-textual data. With a focus on different types of keyword-based queries on geo-textual data, the tutorial also explores topics such as continuous queries on streaming geo-textual data, queries that retrieve attractive regions of geo-textual objects, and queries that extract properties, e.g., topics and top-$k$ frequent words, of the objects in regions. The tutorial is designed to offer an overview of the problems addressed in this body of literature and offers an overview of pertinent concepts and techniques. In addition, the tutorial suggests open problems and new research direction.

With the proliferation of the mobile use of the web, spatial keyword query (SKQ) services are gaining in importance. However, state-of-the-art SKQ systems do not provide systematic functionality that allows users to ask why some known object is unexpectedly missing from a query result and do not provide an explanation for such missing objects. In this demonstration, we present a system called YASK, a whY-not question Answering engine for Spatial Keyword query services, that is capable of answering why-not questions posed in response to answers to spatial keyword top- k queries. Two explanation and query refinement models, namely preference adjustment and keyword adaption , are implemented in YASK. The system provides users not only with the reasons why desired objects are missing from query results, but provides also relevant refined queries that revive the expected but missing objects. This demonstration gives attendees hands-on experience with YASK through a map-based GUI interface in which attendees can issue spatial keyword queries, pose why-not questions, and visualize the results.

With the growing volumes of vehicle trajectory data, it becomes increasingly possible to capture time-varying and uncertain travel costs in a road network, including travel time and fuel consumption. The current paradigm represents a road network as a weighted graph; it blasts trajectories into small fragments that fit the under-lying edges to assign weights to edges; and it then applies a routing algorithm to the resulting graph. We propose a new paradigm, the hybrid graph , that targets more accurate and more efficient path cost distribution estimation. The new paradigm avoids blasting trajectories into small fragments and instead assigns weights to paths rather than simply to the edges. We show how to compute path weights using trajectory data while taking into account the travel cost dependencies among the edges in the paths. Given a departure time and a query path, we show how to select an optimal set of weights with associated paths that cover the query path and such that the weights enable the most accurate joint cost distribution estimation for the query path. The cost distribution of the query path is then computed accurately using the joint distribution. Finally, we show how the resulting method for computing cost distributions of paths can be integrated into existing routing algorithms. Empirical studies with substantial trajectory data from two different cities offer insight into the design properties of the proposed method and confirm that the method is effective in real-world settings.

Information diffusion in social networks is often characterized by huge participating communities and viral cascades of high dynamicity. To observe, summarize, and understand the evolution of dynamic diffusion processes in an informative and insightful way is a challenge of high practical value. However, few existing studies aim to summarize networks for interesting dynamic patterns. Dynamic networks raise new challenges not found in static settings, including time sensitivity, online interestingness evaluation, and summary traceability, which render existing techniques inadequate. We propose dynamic network summarization to summarize dynamic networks with millions of nodes by only capturing the few most interesting nodes or edges overtime. Based on the concepts of diffusion radius and scope, we define interestingness measures for dynamic networks, and we propose OSNet, an online summarization framework for dynamic networks. Efficient algorithms are included in OSNet. We report on extensive experiments with both synthetic and real-life data. The study offers insight into the effectiveness, efficiency, and design properties of OSNet.

Travel planning and recommendation are important aspects of transportation. We propose and investigate a novel Collective Travel Planning (CTP) query that finds the lowest-cost route connecting multiple sources and a destination, via at most$k$meeting points. When multiple travelers target the same destination (e.g., a stadium or a theater), they may want to assemble at meeting points and then go together to the destination by public transport to reduce their global travel cost (e.g., energy, money, or greenhouse-gas emissions). This type of functionality holds the potential to bring significant benefits to society and the environment, such as reducing energy consumption and greenhouse-gas emissions, enabling smarter and greener transportation, and reducing traffic congestions. The CTP query is Max SNP-hard. To compute the query efficiently, we develop two algorithms, including an exact algorithm and an approximation algorithm. The exact algorithm is capable finding the optimal result for small values of$k$(e.g.,$k = 2$) in interactive time, while the approximation algorithm, which has a$5$-approximation ratio, is suitable for other situations. The performance of the CTP query is studied experimentally with real and synthetic spatial data.

We provide techniques that enable a scalable so-called Volunteered Geographic Services system. This system targets the increasing populations of online mobile users, e.g., smartphone users, enabling such users to provide location-based services to each other, thus enabling citizen reporter or citizen as a sensor scenarios. More specifically, the system allows users to register as service volunteers, or micro-service providers, by accepting service descriptions and periodically updated locations from such volunteers; and the system allows users to subscribe to notifications of available, nearby relevant services by accepting subscriptions, formalized as continuous queries, that take service preferences and user locations as arguments and return relevant services. Services are ranked according to their relevance and distance to a query, and the highest ranked services are returned. The key challenge addressed is that of scalably providing up-to-date results to queries when the query locations change continuously. This is achieved by the proposal of a new so-called safe-zone model. With safe zones, query results are accompanied by safe zones with the property that a query result remains the same for all locations in its safe zone. Then, query users need only notify the system when they exit their current safe zone. Existing safe-zone models fall short in the papers setting. The new model is enabled by (i) weighted and (ii) set weighted imprecise Voronoi cells. The paper covers underlying concepts, properties, and algorithms, and it covers applications in VGS tracking and presents findings of empirical performance studies.

Many databases contain temporal, or time-referenced, data and use intervals to capture the temporal aspect. While SQL-based database management systems (DBMSs) are capable of supporting the management of interval data, the support they offer can be improved considerably. A range of proposed temporal data models and query languages offer ample evidence to this effect. Natural queries that are very difficult to formulate in SQL are easy to formulate in these temporal query languages. The increased focus on analytics over historical data where queries are generally more complex exacerbates the difficulties and thus the potential benefits of a temporal query language. Commercial DBMSs have recently started to offer limited temporal functionality in a step-by-step manner, focusing on the representation of intervals and neglecting the implementation of the query evaluation engine. This article demonstrates how it is possible to extend the relational database engine to achieve a full-fledged, industrial-strength implementation of sequenced temporal queries, which intuitively are queries that are evaluated at each time point. Our approach reduces temporal queries to nontemporal queries over data with adjusted intervals, and it leaves the processing of nontemporal queries unaffected. Specifically, the approach hinges on three concepts: interval adjustment , timestamp propagation , and attribute scaling . Interval adjustment is enabled by introducing two new relational operators, a temporal normalizer and a temporal aligner, and the latter two concepts are enabled by the replication of timestamp attributes and the use of so-called scaling functions. By providing a set of reduction rules, we can transform any temporal query, expressed in terms of temporal relational operators, to a query expressed in terms of relational operators and the two new operators. We prove that the size of a transformed query is linear in the number of temporal operators in the original query. An integration of the new operators and the transformation rules, along with query optimization rules, into the kernel of PostgreSQL is reported. Empirical studies with the resulting temporal DBMS are covered that offer insights into pertinent design properties of the article's proposal. The new system is available as open-source software.

No abstract available.

editorial Free Access Share on Editorial: The Dark Citations of TODS Papers and What to Do about It—or: Cite the Journal Paper Editor: Christian S. Jensen View Profile Authors Info & Claims ACM Transactions on Database SystemsVolume 41Issue 2June 2016 Article No.: 8epp 1–3https://doi.org/10.1145/2946798Published:30 June 2016Publication History 0citation264DownloadsMetricsTotal Citations0Total Downloads264Last 12 Months18Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

We define and study Euclidean and spatial network variants of a new path finding problem: given a set of safe zones, find paths that minimize the distance traveled outside the safe zones. In this problem, the entire space with the exception of the safe zones is unsafe, but passable, and it differs from problems that involve unsafe regions to be strictly avoided. As a result, existing algorithms are not effective solutions to the new problem. To solve the Euclidean variant, we devise a transformation of the continuous data space with safe zones into a discrete graph upon which shortest path algorithms apply. A naïve transformation yields a very large graph that is expensive to search. In contrast, our transformation exploits properties of hyperbolas in the Euclidean space to safely eliminate graph edges, thus improving performance without affecting the shortest path results. To solve the spatial network variant, we propose a different graph-to-graph transformation that identifies critical points that serve the same purpose as do the hyperbolas, thus avoiding the creation of extraneous edges. This transformation can be extended to support a weighted version of the problem, where travel in safe zones has non-zero cost. We conduct extensive experiments using both real and synthetic data. The results show that our approaches outperform baseline approaches by more than an order of magnitude in graph construction time, storage space and query response time.

The goal in similarity search is to find objects similar to a specified query object given a certain similarity criterion. Although useful in many areas, such as multimedia retrieval, pattern recognition, and computational biology, to name but a few, similarity search is not yet supported well by commercial DBMS. This may be due to the complex data types involved and the needs for flexible similarity criteria seen in real applications. We propose an efficient disk-based metric access method, the Space-filling curve and Pivot-based B+-tree (SPB-tree), to support a wide range of data types and similarity metrics. The SPB-tree uses a small set of so-called pivots to reduce significantly the number of distance computations, uses a space-filling curve to cluster the data into compact regions, thus improving storage efficiency, and utilizes a B+-tree with minimum bounding box information as the underlying index. The SPB-tree also employs a separate random access file to efficiently manage a large and complex data. By design, it is easy to integrate the SPB-tree into an existing DBMS. We present efficient similarity search algorithms and corresponding cost models based on the SPB-tree. Extensive experiments using real and synthetic data show that the SPB-tree has much lower construction cost, smaller storage size, and can support more efficient similarity queries with high accuracy cost models than is the case for competing techniques. Moreover, the SPB-tree scales sublinearly with growing dataset size.

Large volumes of geo-tagged text objects are available on the web. Spatial keyword top-k queries retrieve k such objects with the best score according to a ranking function that takes into account a query location and query keywords. In this setting, users may wonder why some known object is unexpectedly missing from a result; and understanding why may aid users in retrieving better results. While spatial keyword querying has been studied intensively, no proposals exist for how to offer users explanations of why such expected objects are missing from results. We provide techniques that allow the revision of spatial keyword queries such that their results include one or more desired, but missing objects. In doing so, we adopt a query refinement approach to provide a basic algorithm that reduces the problem to a two-dimensional geometrical problem. To improve performance, we propose an index-based ranking estimation algorithm that prunes candidate results early. Extensive experimental results offer insight into design properties of the proposed techniques and suggest that they are efficient in terms of both running time and I/O cost.

Reduction in greenhouse gas emissions from transportation attracts increasing interest from governments, fleet managers, and individual drivers. Eco-routing, which enables drivers to use eco-friendly routes, is a simple and effective approach to reducing emissions from transportation. We present EcoSky, a system that annotates edges of a road network with time dependent and uncertain eco-weights using GPS data and that supports different types of eco-routing. Basic eco-routing returns the most eco-friendly routes; skyline eco-routing takes into account not only fuel consumption but also travel time and distance when computing eco-routes; and personalized eco-routing considers each driver's past behavior and accordingly suggests different routes to different drivers.

Rapidly increasing volumes of GPS data collected from vehicles provide new and increasingly comprehensive insight into the routes that drivers prefer. While routing services generally compute shortest or fastest routes, recent studies suggest that local drivers often prefer routes that are neither shortest nor fastest, indicating that drivers value route properties that are diverse and hard to quantify or even identify. We propose a routing service that uses an existing routing service while exploiting the availability of historical route usage data from local drivers. Given a source and destination, the service recommends a corresponding route that is most preferred by local drivers. It uses a route preference function that takes into account the number of distinct drivers and the number of trips associated with a route, as well as temporal aspects of the trips. The paper provides empirical studies with real route usage data and an existing online routing service.

Summary form only given. A prototypical geo-textual web query takes a user location and user-supplied keywords as arguments and returns geotextual objects that are spatially and textually relevant to these arguments. Due perhaps to the rich semantics of geographical space and its importance to our daily lives, many different kinds of useful geo-textual web query functionality may be envisioned.

Range queries in metric spaces have applications in many areas such as multimedia retrieval, computational biology, and location-based services, where metric uncertain data exists in different forms, resulting from equipment limitations, high-throughput sequencing technologies, privacy preservation, or others. In this paper, we represent metric uncertain data by using an object-level model and a bi-level model, respectively. Two novel indexes, the uncertain pivot B+-tree (UPB-tree) and the uncertain pivot B+-forest (UPB-forest), are proposed accordingly in order to support probabilistic range queries w.r.t. a wide range of uncertain data types and similarity metrics. Both index structures use a small set of effective pivots chosen based on a newly defined criterion, and employ the B+-tree(s) as the underlying index. By design, they are easy to be integrated into any existing DBMS. In addition, we present efficient metric probabilistic range query algorithms, which utilize the validation and pruning techniques based on our derived probability lower and upper bounds. Extensive experiments with both real and synthetic data sets demonstrate that, compared against existing state-of-the-art indexes for metric uncertain data, the UPB-tree and UPB-forest incur much lower construction costs, consume smaller storage spaces, and can support more efficient metric probabilistic range queries.

Massive amounts of geo-tagged and textually annotated images are provided by online photo services such as Flickr and Zommr. However, most existing image retrieval engines only consider text annotations. We present I2RS, a system that allows users to view geo-textual images on Google Maps, find hot topics within a specific geographic region and time period, retrieve images similar to a query image, and receive recommended images that they might be interested in. I2RS is a distributed geo-textual image retrieval and recommendation system that employs SPB-trees to index geo-textual images, and that utilizes metric similarity queries, including top- m spatio-temporal range and k nearest neighbor queries, to support geo-textual image retrieval and recommendation. The system adopts the browser-server model, whereas the server is deployed in a distributed environment that enables efficiency and scalability to huge amounts of data and requests. A rich set of 100 million geo-textual images crawled from Flickr is used to demonstrate that, I2RS can return high-quality answers in an interactive way and support efficient updates for high image arrival rates.

The increasing availability of massive and accurate laser data enables the processing of spatial queries on terrains. As shortest-path computation, an integral element of query processing, is inherently expensive on terrains, a key approach to enabling efficient query processing is to reduce the need for exact shortest-path computation in query processing. We develop new lower and upper bounds on terrain shortest distances that are provably tighter than any existing bounds. Unlike existing bounds, the new bounds do not rely on the quality of the triangulation. We show how use of the new bounds speeds up query processing by reducing the need for exact distance computations. Speedups of of nearly an order of magnitude are demonstrated empirically for well-known spatial queries.

The papers in this special section were presented a the 29th International Conference on Data Engineering was held in Brisbane, QLD, Australia, on April 8-11, 2013.

The discovery of regions of interest in large cities is an important challenge. We propose and investigate a novel query called the path nearby cluster (PNC) query that finds regions of potential interest (e.g., sightseeing places and commercial districts) with respect to a user-specified travel route. Given a set of spatial objects$O$(e.g., POIs, geo-tagged photos, or geo-tagged tweets) and a query route$q$, if a cluster$c$has high spatial-object density and is spatially close to$q$, it is returned by the query (a cluster is a circular region defined by a center and a radius). This query aims to bring important benefits to users in popular applications such as trip planning and location recommendation. Efficient computation of the PNC query faces two challenges: how to prune the search space during query processing, and how to identify clusters with high density effectively. To address these challenges, a novel collective search algorithm is developed. Conceptually, the search process is conducted in the spatial and density domains concurrently. In the spatial domain, network expansion is adopted, and a set of vertices are selected from the query route as expansion centers. In the density domain, clusters are sorted according to their density distributions and they are scanned from the maximum to the minimum. A pair of upper and lower bounds are defined to prune the search space in the two domains globally. The performance of the PNC query is studied in extensive experiments based on real and synthetic spatial data.

A moving top-$k$spatial keyword (M$k$SK) query, which takes into account a continuously moving query location, enables a mobile client to be continuously aware of the top-$k$spatial web objects that best match a query with respect to location and text relevance. The increasing mobile use of the web and the proliferation of geo-positioning render it of interest to consider a scenario where spatial keyword search is outsourced to a separate service provider capable at handling the voluminous spatial web objects available from various sources. A key challenge is that the service provider may return inaccurate or incorrect query results (intentionally or not), e.g., due to cost considerations or invasion of hackers. Therefore, it is attractive to be able to authenticate the query results at the client side. Existing authentication techniques are either inefficient or inapplicable for the kind of query we consider. We propose new authentication data structures, the MIR-tree and MIR$^*$-tree, that enable the authentication of MkSK queries at low computation and communication costs. We design a verification object for authenticating MkSK queries, and we provide algorithms for constructing verification objects and using these for verifying query results. A thorough experimental study on real data shows that the proposed techniques are capable of outperforming two baseline algorithms by orders of magnitude.

With the proliferation of geo-positioning and geo-tagging techniques, spatio-textual objects that possess both a geographical location and a textual description are gaining in prevalence, and spatial keyword queries that exploit both location and textual description are gaining in prominence. However, the queries studied so far generally focus on finding individual objects that each satisfy a query rather than finding groups of objects where the objects in a group together satisfy a query. We define the problem of retrieving a group of spatio-textual objects such that the group's keywords cover the query's keywords and such that the objects are nearest to the query location and have the smallest inter-object distances. Specifically, we study three instantiations of this problem, all of which are NP-hard. We devise exact solutions as well as approximate solutions with provable approximation bounds to the problems. In addition, we solve the problems of retrieving top- k groups of three instantiations, and study a weighted version of the problem that incorporates object weights. We present empirical studies that offer insight into the efficiency of the solutions, as well as the accuracy of the approximate solutions.

No abstract available.

No abstract available.

With the proliferation of Internet-connected, location-aware mobile devices, such as smartphones, we are also witnessing a proliferation and increased use of map-based services that serve information about relevant Points of Interest (PoIs) to their users.

Online driving direction services offer fundamental functionality to mobile users, and such services see substantial and increasing loads as mobile access continues to proliferate. Cache servers can be deployed in order to reduce the resulting network traffic. We define so-called concise shortest paths that are equivalent to driving instructions. A concise shortest path occupies much less space than a shortest path; yet it provides sufficient navigation information to mobile users. Then we propose techniques that enable the caching of concise shortest paths in order to improve the cache hit ratio.

With the rapidly increasing deployment of Internet-connected, location-aware mobile devices, very large and increasing amounts of geo-tagged and timestamped user-generated content, such as microblog posts, are being generated. We present indexing, update, and query processing techniques that are capable of providing the top-k terms seen in posts in a user-specified spatio-temporal range. The techniques enable interactive response times in the millisecond range in a realistic setting where the arrival rate of posts exceeds today's average tweet arrival rate by a factor of 4-10. The techniques adaptively maintain the most frequent items at various spatial and temporal granularities. They extend existing frequent item counting techniques to maintain exact counts rather than approximations. An extensive empirical study with a large collection of geo-tagged tweets shows that the proposed techniques enable online aggregation and query processing at scale in realistic settings.

Different uses of a road network call for the consideration of different travel costs: in route planning, travel time and distance are typically considered, and green house gas (GHG) emissions are increasingly being considered. Further, travel costs such as travel time and GHG emissions are time-dependent and uncertain. To support such uses, we propose techniques that enable the construction of a multi-cost, time-dependent, uncertain graph (MTUG) model of a road network based on GPS data from vehicles that traversed the road network. Based on the MTUG, we define stochastic skyline routes that consider multiple costs and time-dependent uncertainty, and we propose efficient algorithms to retrieve stochastic skyline routes for a given source-destination pair and a start time. Empirical studies with three road networks in Denmark and a substantial GPS data set offer insight into the design properties of the MTUG and the efficiency of the stochastic skyline routing algorithms.

In step with the web being used widely by mobile users, user location is becoming an essential signal in services, including local intent search. Given a large set of spatial web objects consisting of a geographical location and a textual description (e.g., Online business directory entries of restaurants, bars, and shops), how can we find sets of objects that are both spatially and textually relevant to a query? Most of existing studies solve the problem by requiring that all query keywords are covered by the returned objects and then rank the sets by spatial proximity. The needs for identifying sets with more textually relevant objects render these studies inapplicable. We propose locality Search, a query that returns top-k sets of spatial web objects and integrates spatial distance and textual relevance in one ranking function. We show that computing the query is NP-hard, and we present two efficient exact algorithms and one generic approximate algorithm based on greedy strategies for computing the query. We report on findings from an empirical study with three real-life datasets. The study offers insight into the efficiency and effectiveness of the proposed algorithms.

Points of interest (PoI) data serves an important role as a foundation for a wide variety of location-based services. Such data is typically obtained from an authoritative source or from users through crowd sourcing. It can be costly to maintain an up-to-date authoritative source, and data obtained from users can vary greatly in coverage and quality. We are also witnessing a proliferation of both GPS-enabled mobile devices and geotagged content generated by users of such devices. This state of affairs motivates the paper's proposal of techniques for the automatic discovery of PoI data from geo-tagged microblog posts. Specifically, the paper proposes a new clustering technique that takes into account both the spatial and textual attributes of microblog posts to obtain clusters that represent PoIs. The technique expands clusters based on a proposed quality function that enables clusters of arbitrary shape and density. An empirical study with a large database of real geo-tagged microblog posts offers insight into the properties of the proposed techniques and suggests that they are effective at discovering real-world points of interest.

Increasing volumes of geo-referenced data are becoming available. This data includes so-called points of interest that describe businesses, tourist attractions, etc. by means of a geo-location and properties such as a textual description or ratings. We propose and study the efficient implementation of a new kind of query on points of interest that takes into account both the locations and properties of the points of interest. The query takes a result cardinality, a spatial range, and property-related preferences as parameters, and it returns a compact set of points of interest with the given cardinality and in the given range that satisfies the preferences. Specifically, the points of interest in the result set cover so-called allying preferences and are located far from points of interest that possess so-called alienating preferences. A unified result rating function integrates the two kinds of preferences with spatial distance to achieve this functionality. We provide efficient exact algorithms for this kind of query. To enable queries on large datasets, we also provide an approximate algorithm that utilizes a nearest-neighbor property to achieve scalable performance. We develop and apply lower and upper bounds that enable search-space pruning and thus improve performance. Finally, we provide a generalization of the above query and also extend the algorithms to support the generalization. We report on an experimental evaluation of the proposed algorithms using real point of interest data from Google Places for Business that offers insight into the performance of the proposed solutions.

We consider an application scenario where points of interest (PoIs) each have a web presence and where a web user wants to identify a region that contains relevant PoIs that are relevant to a set of keywords, e.g., in preparation for deciding where to go to conveniently explore the PoIs. Motivated by this, we propose thelength-constrained maximum-sum region(LCMSR) query that returns a spatial-network region that is located within a general region of interest, that does not exceed a given size constraint, and that best matches query keywords. Such a query maximizes the total weight of the PoIs in it w.r.t. the query keywords. We show that it is NP-hard to answer this query. We develop an approximation algorithm with a (5 + ε) approximation ratio utilizing a technique that scales node weights into integers. We also propose a more efficient heuristic algorithm and a greedy algorithm. Empirical studies on real data offer detailed insight into the accuracy of the proposed algorithms and show that the proposed algorithms are capable of computing results efficiently and effectively.

A recent PVLDB paper reports on experimental analyses of ten spatial join techniques in main memory. We build on this comprehensive study to raise awareness of the fact that empirical running time performance findings in main-memory settings are results of not only the algorithms and data structures employed, but also their implementation, which complicates the interpretation of the results. In particular, we re-implement the worst performing technique without changing the underlying high-level algorithm, and we then offer evidence that the resulting re-implementation is capable of outperforming all the other techniques. This study demonstrates that in main memory, where no time-consuming I/O can mask variations in implementation, implementation details are very important; and it offers a concrete illustration of how it is difficult to make conclusions from empirical running time performance findings in main-memory settings about data structures and algorithms studied.

We are witnessing increasing interests in the effective use of road networks. For example, to enable effective vehicle routing, weighted-graph models of transportation networks are used, where the weight of an edge captures some cost associated with traversing the edge, e.g., greenhouse gas (GHG) emissions or travel time. It is a precondition to using a graph model for routing that all edges have weights. Weights that capture travel times and GHG emissions can be extracted from GPS trajectory data collected from the network. However, GPS trajectory data typically lack the coverage needed to assign weights to all edges. This paper formulates and addresses the problem of annotating all edges in a road network with travel cost based weights from a set of trips in the network that cover only a small fraction of the edges, each with an associated ground-truth travel cost. A general framework is proposed to solve the problem. Specifically, the problem is modeled as a regression problem and solved by minimizing a judiciously designed objective function that takes into account the topology of the road network. In particular, the use of weighted PageRank values of edges is explored for assigning appropriate weights to all edges, and the property of directional adjacency of edges is also taken into account to assign weights. Empirical studies with weights capturing travel time and GHG emissions on two road networks (Skagen, Denmark, and North Jutland, Denmark) offer insight into the design properties of the proposed techniques and offer evidence that the techniques are effective.

No abstract available.

No abstract available.

A wide variety of desktop and mobile Web applications involve geo-tagged content, e.g., photos and (micro-) blog postings. Such content, often called User Generated Geo-Content (UGGC), plays an increasingly important role in many applications. However, a great demand also exists for "core" UGGC where the geo-spatial aspect is not just a tag on other content, but is the primary content, e.g., a city street map with up-to-date road construction data. Along these lines, the iPark system aims to turn volumes of GPS data obtained from vehicles into information about the locations of parking spaces, thus enabling effective parking search applications. In particular, we demonstrate how iPark helps ordinary users annotate an existing digital map with two types of parking, on-street parking and parking zones, based on vehicular tracking data.

Social media has changed the way we communicate. Social media data capture our social interactions and utterances in machine readable format. Searching and analysing massive and frequently updated social media data brings significant and diverse rewards across many different application domains, from politics and business to social science and epidemiology.

Finding a location for a new facility s.t. the facility attracts the maximal number of customers is a challenging problem. Existing studies either model customers as static sites and thus do not consider customer movement, or they focus on theoretical aspects and do not provide solutions that are shown empirically to be scalable. Given a road network, a set of existing facilities, and a collection of customer route traversals, an optimal segment query returns the optimal road network segment(s) for a new facility. We propose a practical framework for computing this query, where each route traversal is assigned a score that is distributed among the road segments covered by the route according to a score distribution model. We propose two algorithms that adopt different approaches to computing the query. Empirical studies with real data sets demonstrate that the algorithms are capable of offering high performance in realistic settings.

Reduction in greenhouse gas emissions from transportation is essential in combating global warming and climate change. Eco-routing enables drivers to use the most eco-friendly routes and is effective in reducing vehicle emissions. The EcoTour system assigns eco-weights to a road network based on GPS and fuel consumption data collected from vehicles to enable ecorouting. Given an arbitrary source-destination pair in Denmark, EcoTour returns the shortest route, the fastest route, and the eco-route, along with statistics for the three routes. EcoTour also serves as a testbed for exploring advanced solutions to a range of challenges related to eco-routing.

Reliable indoor positioning is an important foundation for emerging indoor location based services. Most existing indoor positioning proposals rely on a single wireless technology, e.g., Wi-Fi, Bluetooth, or RFID. A hybrid positioning system combines such technologies and achieves better positioning accuracy by exploiting the different capabilities of the different technologies. In a hybrid system based on Wi-Fi and Bluetooth, the former works as the main infrastructure to enable fingerprint based positioning, while the latter (via hotspot devices) partitions the indoor space as well as a large Wi-Fi radio map. As a result, the Wi-Fi based online position estimation is improved in a divide-and-conquer manner. We study three aspects of such a hybrid indoor positioning system. First, to avoid large positioning errors caused by similar reference positions that are hard to distinguish, we design a deployment algorithm that identifies and separates such positions into different smaller radio maps by deploying Bluetooth hotspots at particular positions. Second, we design methods that improve the partition switching that occurs when a user leaves the detection range of a Bluetooth hotspot. Third, we propose three architectural options for placement of the computation workload. We evaluate all proposals using both simulation and walkthrough experiments in two indoor environments of different sizes. The results show that our proposals are effective and efficient in achieving very good indoor positioning performance.

Mobile location-based services is a very successful class of services that are being used frequently by users with GPS-enabled mobile devices such as smartphones. This paper presents a study of how to exploit GPS trajectory data, which is available in increasing volumes, for the assessment of the quality of one kind of location-based service, namely routing services. Specifically, the paper presents a framework that enables the comparison of the routes provided by routing services with the actual driving behaviors of local drivers. Comparisons include route length, travel time, and also route popularity, which are enabled by common driving behaviors found in available trajectory data. The ability to evaluate the quality of routing services enables service providers to improve the quality of their services and enables users to identify the services that best serve their needs. The paper covers experiments with real vehicle trajectory data and an existing online navigation service. It is found that the availability of information about previous trips enables better prediction of route travel time and makes it possible to provide the users with more popular routes than does a conventional navigation service.

In step with the rapid proliferation of mobile devices with Internet access, the Web is increasingly being access by mobile-device users on the move. Further, it is increasingly possible to accurately geo-position mobile devices, and increasing volumes of geo-positioned content, e.g., Web pages, business directory entries, and microblog posts, are becoming available on the Web. In short, an increasingly mobile and spatial Web is fast emerging. This development enables Web queries with local intent, i.e., keyword-based queries issued by users who are looking for Web content near them. In addition, it implies an increasing demand for query functionality that supports local intent.

The use of accurate 3D spatial network models can enable substantial improvements in vehicle routing. Notably, such models enable eco-routing, which reduces the environmental impact of transportation. We propose a novel filtering and lifting framework that augments a standard 2D spatial network model with elevation information extracted from massive aerial laser scan data and thus yields an accurate 3D model. We present a filtering technique that is capable of pruning irrelevant laser scan points in a single pass, but assumes that the 2D network fits in internal memory and that the points are appropriately sorted. We also provide an external-memory filtering technique that makes no such assumptions. During lifting, a triangulated irregular network (TIN) surface is constructed from the remaining points. The 2D network is projected onto the TIN, and a 3D network is constructed by means of interpolation. We report on a large-scale empirical study that offers insight into the accuracy, efficiency, and scalability properties of the framework.

With the proliferation of mobile computing, positioning systems are becoming available that enable indoor location-based services. As a result, indoor tracking data is also becoming available. This paper puts focus on one use of such data, namely the identification of typical movement patterns among indoor moving objects. Specifically, the paper presents a method for the identification of movement patterns. Leveraging concepts from sequential pattern mining, the method takes into account the specifics of spatial movement and, in particular, the specifics of tracking data that captures indoor movement. For example, the paper's proposal supports spatial aggregation and utilizes the topology of indoor spaces to achieve better performance. The paper reports on empirical studies with real and synthetic data that offer insights into the functional and computational aspects of its proposal.

The monitoring of a system can yield a set of measurements that can be modeled as a collection of time series. These time series are often sparse, due to missing measurements, and spatiotemporally correlated, meaning that spatially close time series exhibit temporal correlation. The analysis of such time series offers insight into the underlying system and enables prediction of system behavior. While the techniques presented in the paper apply more generally, we consider the case of transportation systems and aim to predict travel cost from GPS tracking data from probe vehicles. Specifically, each road segment has an associated travel-cost time series, which is derived from GPS data. We use spatio-temporal hidden Markov models (STHMM) to model correlations among different traffic time series. We provide algorithms that are able to learn the parameters of an STHMM while contending with the sparsity, spatio-temporal correlation, and heterogeneity of the time series. Using the resulting STHMM, near future travel costs in the transportation network, e.g., travel time or greenhouse gas emissions, can be inferred, enabling a variety of routing services, e.g., eco-routing. Empirical studies with a substantial GPS data set offer insight into the design properties of the proposed framework and algorithms, demonstrating the effectiveness and efficiency of travel cost inferencing.

The notion of point-of-interest (PoI) has existed since paper road maps began to include markings of useful places such as gas stations, hotels, and tourist attractions. With the introduction of geopositioned mobile devices such as smartphones and mapping services such as Google Maps, the retrieval of PoIs relevant to a user's intent has became a problem of automated spatio-textual information retrieval. Over the last several years, substantial research has gone into the invention of functionality and efficient implementations for retrieving nearby PoIs. However, with a couple of exceptions existing proposals retrieve results at single-PoI granularity. We assume that a mobile device user issues queries consisting of keywords and an automatically supplied geo-position, and we target the common case where the user wishes to find nearby groups of PoIs that are relevant to the keywords. Such groups are relevant to users who wish to conveniently explore several options before making a decision such as to purchase a specific product. Specifically, we demonstrate a practical proposal for finding top- k PoI groups in response to a query. We show how problem parameter settings can be mapped to options that are meaningful to users. Further, although this kind of functionality is prone to combinatorial explosion, we will demonstrate that the functionality can be supported efficiently in practical settings.

Geo-textual indices play an important role in spatial keyword querying. The existing geo-textual indices have not been compared systematically under the same experimental framework. This makes it difficult to determine which indexing technique best supports specific functionality. We provide an all-around survey of 12 state-of-the-art geo-textual indices. We propose a benchmark that enables the comparison of the spatial keyword query performance. We also report on the findings obtained when applying the benchmark to the indices, thus uncovering new insights that may guide index selection as well as further research.

With the increasing availability of terrain data, e.g., from aerial laser scans, the management of such data is attracting increasing attention in both industry and academia. In particular, spatial queries, e.g., k -nearest neighbor and reverse nearest neighbor queries, in Euclidean and spatial network spaces are being extended to terrains. Such queries all rely on an important operation, that of finding shortest surface distances. However, shortest surface distance computation is very time consuming. We propose techniques that enable efficient computation of lower and upper bounds of the shortest surface distance, which enable faster query processing by eliminating expensive distance computations. Empirical studies show that our bounds are much tighter than the best-known bounds in many cases and that they enable speedups of up to 43 times for some well-known spatial queries.

GPS-enabled devices are pervasive nowadays. Finding movement patterns in trajectory data stream is gaining in importance. We propose a group discovery framework that aims to efficiently support the online discovery of moving objects that travel together. The framework adopts a sampling-independent approach that makes no assumptions about when positions are sampled, gives no special importance to sampling points, and naturally supports the use of approximate trajectories. The framework's algorithms exploit state-of-the-art, density-based clustering (DBScan) to identify groups. The groups are scored based on their cardinality and duration, and the top-k groups are returned. To avoid returning similar subgroups in a result, notions of domination and similarity are introduced that enable the pruning of low-interest groups. Empirical studies on real and synthetic data sets offer insight into the effectiveness and efficiency of the proposed framework.

Web users and content are increasingly being geo-positioned. This development gives prominence to spatial keyword queries, which involve both the locations and textual descriptions of content. We study the efficient processing of continuously moving top-kspatial keyword (MkSK) queries over spatial text data. State-of-the-art solutions for moving queries employsafe zonesthat guarantee the validity of reported results as long as the user remains within the safe zone associated with a result. However, existing safe-zone methods focus solely on spatial locations and ignore text relevancy. We propose two algorithms for computing safe zones that guarantee correct results at any time and that aim to optimize the server-side computation as well as the communication between the server and the client. We exploit tight and conservative approximations of safe zones and aggressive computational space pruning. We present techniques that aim to compute the next safe zone efficiently, and we present two types of conservative safe zones that aim to reduce the communication cost. Empirical studies with real data suggest that the proposals are efficient. To understand the effectiveness of the proposed safe zones, we study analytically the expected area of a safe zone, which indicates on average for how long a safe zone remains valid, and we study the expected number of influence objects needed to define a safe zone, which gives an estimate of the average communication cost. The analytical modeling is validated through empirical studies.

Deployments of networked sensors fuel online applications that feed on real-time sensor data. This scenario calls for techniques that support the management of workloads that contain queries as well as very frequent updates. This paper compares two well-chosen approaches to exploiting the parallelism offered by modern processors for supporting such workloads. A general approach to avoiding contention among parallel hardware threads and thus exploiting the parallelism available in processors is to maintain two copies, or snapshots, of the data: one for the relatively long-duration queries and one for the frequent and very localized updates. The snapshot that receives the updates is frequently made available to queries, so that queries see up-to-date data. The snapshots may be physical or virtual. Physical snapshots are created using the C library memcpy function. Virtual snapshots are created by the fork system function that creates a new process that initially has the same data snapshot as the process it was forked from. When the new process carries out updates, this triggers the actual memory copying in a copy-on-write manner at memory page granularity. This paper characterizes the circumstances under which each technique is preferable. The use of physical snapshots is surprisingly efficient.

The reduction of greenhouse gas (GHG) emissions from transportation is essential for achieving politically agreed upon emissions reduction targets that aim to combat global climate change. So-called eco-routing and eco-driving are able to substantially reduce GHG emissions caused by vehicular transportation. To enable these, it is necessary to be able to reliably quantify the emissions of vehicles as they travel in a spatial network. Thus, a number of models have been proposed that aim to quantify the emissions of a vehicle based on GPS data from the vehicle and a 3D model of the spatial network the vehicle travels in. We develop an evaluation framework, called EcoMark, for such environmental impact models. In addition, we survey all eleven state-of-the-art impact models known to us. To gain insight into the capabilities of the models and to understand the effectiveness of the EcoMark, we apply the framework to all models.

Indoor spaces accommodate large numbers of spatial objects, e.g., points of interest (POIs), and moving populations. A variety of services, e.g., location-based services and security control, are relevant to indoor spaces. Such services can be improved substantially if they are capable of utilizing indoor distances. However, existing indoor space models do not account well for indoor distances. To address this shortcoming, we propose a data management infrastructure that captures indoor distance and facilitates distance-aware query processing. In particular, we propose a distance-aware indoor space model that integrates indoor distance seamlessly. To enable the use of the model as a foundation for query processing, we develop accompanying, efficient algorithms that compute indoor distances for different indoor entities like doors as well as locations. We also propose an indexing framework that accommodates indoor distances that are pre-computed using the proposed algorithms. On top of this foundation, we develop efficient algorithms for typical indoor, distance-aware queries. The results of an extensive experimental evaluation demonstrate the efficacy of the proposals.

The skyline of a multidimensional point set consists of the points that are not dominated by other points. In a scenario where product features are represented by multidimensional points, the skyline points may be viewed as representing competitive products. A product provider may wish to upgrade uncompetitive products to become competitive, but wants to take into account the upgrading cost. We study the top-k product upgrading problem. Given a set P of competitor products, a set T of products that are candidates for upgrade, and an upgrading cost function f that applies to T, the problem is to return the k products in T that can be upgraded to not be dominated by any products in P at the lowest cost. This problem is non-trivial due to not only the large data set sizes, but also to the many possibilities for upgrading a product. We identify and provide solutions for the different options for upgrading an uncompetitive product, and combine the solutions into a single solution. We also propose a spatial join-based solution that assumes P and T are indexed by an R-tree. Given a set of products in the same R-tree node, we derive three lower bounds on their upgrading costs. These bounds are employed by the join approach to prune upgrade candidates with uncompetitive upgrade costs. Empirical studies with synthetic and real data show that the join approach is efficient and scalable.

A range of applications call for a mobile client to continuously monitor others in close proximity. Past research on such problems has covered two extremes: It has offered totally centralized solutions, where a server takes care of all queries, and totally distributed solutions, in which there is no central authority at all. Unfortunately, none of these two solutions scales to intensive moving object tracking applications, where each client poses a query. In this paper, we formulate the moving continuous query (MCQ) problem and propose a balanced model where servers cooperatively take care of the global view and handle the majority of the workload. Meanwhile, moving clients, having basic memory and computation resources, handle small portions of the workload. This model is further enhanced by dynamic region allocation and grid size adjustment mechanisms that reduce the communication and computation cost for both servers and clients. An experimental study demonstrates that our approaches offer better scalability than competitors.

We are witnessing a proliferation of Internet-worked, geo-positioned mobile devices such as smartphones and personal navigation devices. Likewise, location-related services that target the users of such devices are proliferating. Consequently, server-side infrastructures are needed that are capable of supporting the location-related query and update workloads generated by very large populations of such moving objects.

Web search is ubiquitous in our daily lives. Caching has been extensively used to reduce the computation time of the search engine and reduce the network traffic beyond a proxy server. Another form of web search, known as online shortest path search, is popular due to advances in geo-positioning. However, existing caching techniques are ineffective for shortest path queries. This is due to several crucial differences between web search results and shortest path results, in relation to query matching, cache item overlapping, and query cost variation.

Spatial web objects that possess both a geographical location and a textual description are gaining in prevalence. This gives prominence to spatial keyword queries that exploit both location and textual arguments. Such queries are used in many web services such as yellow pages and maps services. We present SWORS, the Spatial Web Object Retrieval System, that is capable of efficiently retrieving spatial web objects that satisfy spatial keyword queries. Specifically, SWORS supports two types of queries: a) the location-aware top- k text retrieval (L k T) query that retrieves k individual spatial web objects taking into account query location proximity and text relevancy; b) the spatial keyword group (SKG) query that retrieves a group of objects that cover the query keywords and are nearest to the query location and have the shortest inter-object distances. SWORS provides browser-based interfaces for desktop and laptop computers and provides a client application for mobile devices. The interfaces and the client enable users to formulate queries and view the query results on a map. The server side stores the data and processes the queries. We use three real-life data sets to demonstrate the functionality and performance of SWORS.

Due in part to the increasing mobile use of the web and the proliferation of geo-positioning, the web is fast acquiring a significant spatial aspect. Content and users are being augmented with locations that are used increasingly by location-based services. Studies suggest that each week, several billion web queries are issued that have local intent and target spatial web objects. These are points of interest with a web presence, and they thus have locations as well as textual descriptions. This development has given prominence to spatial web data management, an area ripe with new and exciting opportunities and challenges. The research community has embarked on inventing and supporting new query functionality for the spatial web. Different kinds of spatial web queries return objects that are near a location argument and are relevant to a text argument. To support such queries, it is important to be able to rank objects according to their relevance to a query. And it is important to be able to process the queries with low latency. The talk offers an overview of key aspects of the spatial web. Based on recent results obtained by the speaker and his colleagues, the talk explores new query functionality enabled by the setting. Further, the talk offers insight into the data management techniques capable of supporting such functionality.

Web users and content are increasingly being geopositioned, and increased focus is being given to serving local content in response to web queries. This development calls for spatial keyword queries that take into account both the locations and textual descriptions of content. We study the efficient, joint processing of multiple top-k spatial keyword queries. Such joint processing is attractive during high query loads and also occurs when multiple queries are used to obfuscate a user's true query. We propose a novel algorithm and index structure for the joint processing of top-k spatial keyword queries. Empirical studies show that the proposed solution is efficient on real data sets. We also offer analytical studies on synthetic data sets to demonstrate the efficiency of the proposed solution.

This paper considers a cloud computing setting in which similarity querying of metric data is outsourced to a service provider. The data is to be revealed only to trusted users, not to the service provider or anyone else. Users query the server for the most similar data objects to a query example. Outsourcing offers the data owner scalability and a low-initial investment. The need for privacy may be due to the data being sensitive (e.g., in medicine), valuable (e.g., in astronomy), or otherwise confidential. Given this setting, the paper presents techniques that transform the data prior to supplying it to the service provider for similarity queries on the transformed data. Our techniques provide interesting trade-offs between query cost and accuracy. They are then further extended to offer an intuitive privacy guarantee. Empirical studies with real data demonstrate that the techniques are capable of offering privacy while enabling efficient and accurate processing of similarity queries.

Community Question Answering (CQA) is a popular type of service where users ask questions and where answers are obtained from other users or from historical question-answer pairs. CQA archives contain large volumes of questions organized into a hierarchy of categories. As an essential function of CQA services, question retrieval in a CQA archive aims to retrieve historical question-answer pairs that are relevant to a query question. This article presents several new approaches to exploiting the category information of questions for improving the performance of question retrieval, and it applies these approaches to existing question retrieval models, including a state-of-the-art question retrieval model. Experiments conducted on real CQA data demonstrate that the proposed techniques are effective and efficient and are capable of outperforming a variety of baseline methods significantly.

To facilitate a variety of applications, positioning systems are deployed in indoor settings. For example, Bluetooth and RFID positioning are deployed in airports to support real-time monitoring of delays as well as off-line flow and space usage analyses. Such deployments generate large collections of tracking data. Like in other data management applications, joins are indispensable in this setting. However, joins on indoor tracking data call for novel techniques that take into account the limited capabilities of the positioning systems as well as the specifics of indoor spaces. This paper proposes and studies probabilistic, spatio-temporal joins on historical indoor tracking data. Two meaningful types of join are defined. They return object pairs that satisfy spatial join predicates either at a time point or during a time interval. The predicates considered include “same X,” where X is a semantic region such as a room or hallway. Based on an analysis on the uncertainty inherent to indoor tracking data, effective join probabilities are formalized and evaluated for object pairs. Efficient two-phase hash-based algorithms are proposed for the point and interval joins. In a filter-and-refine framework, an R-tree variant is proposed that facilitates the retrieval of join candidates, and pruning rules are supplied that eliminate candidate pairs that do not qualify. An empirical study on both synthetic and real data shows that the proposed techniques are efficient and scalable.

Web users and content are increasingly being geo-positioned. This development gives prominence to spatial keyword queries, which involve both the locations and textual descriptions of content. We study the efficient processing of continuously moving top-k spatial keyword (MkSK) queries over spatial keyword data. State-of-the-art solutions for moving queries employ safe zones that guarantee the validity of reported results as long as the user remains within a zone. However, existing safe zone methods focus solely on spatial locations and ignore text relevancy. We propose two algorithms for computing safe zones that guarantee correct results at any time and that aim to optimize the computation on the server as well as the communication between the server and the client. We exploit tight and conservative approximations of safe zones and aggressive computational space pruning. Empirical studies with real data suggest that our proposals are efficient.

Location-Based Services (LBSs) constitutes one of the most popular classes of mobile services. However, while current LBSs typically target outdoor settings, we lead large parts of our lives indoors. The availability of easy-to-use and low-cost indoor positioning services is essential in also enabling indoor LBSs. Existing indoor positioning services typically use a single technology such as Wi-Fi, RFID or Bluetooth. Wi-Fi based indoor positioning is relatively easy to deploy, but does often not offer good positioning accuracy. In contrast, the use of RFID or Bluetooth for positioning requires considerable investments in equipment in order to ensure good positioning accuracy. Motivated by these observations, we propose a hybrid approach to indoor positioning. In particular, we introduce Bluetooth hotspots into an indoor space with an existing Wi-Fi infrastructure such that better positioning is achieved than what can be achieved by each technology in isolation. We design a flexible and extensible system architecture with an effective online position estimation algorithm for the hybrid system. The system is evaluated empirically in the building of our department. The results show that the hybrid approach improves positioning accuracy markedly.

An online Route Planning Service (RPS) computes a route from one location to another. Current RPSs such as Google Maps require the use of precise locations. However, some users may not want to disclose their source and destination locations due to privacy concerns. An approach that supplies fake locations to an existing service incurs a substantial loss of quality of service, and the service may well return a result that may be not helpful to the user. We propose a solution that is able to return accurate route planning results when source and destination regions are used in order to achieve privacy. The solution re-uses a standard online RPS rather than replicate this functionality, and it needs no trusted third party. The solution is able to compute the exact results without leaking of the exact locations to the RPS or un-trusted parties. In addition, we provide heuristics that reduce the number of times that the RPS needs to be queried, and we also describe how the accuracy and privacy requirements can be relaxed to achieve better performance. An empirical study offers insight into key properties of the approach.

With the proliferation of geo-positioning and geo-tagging, spatial web objects that possess both a geographical location and a textual description are gaining in prevalence, and spatial keyword queries that exploit both location and textual description are gaining in prominence. However, the queries studied so far generally focus on finding individual objects that each satisfy a query rather than finding groups of objects where the objects in a group collectively satisfy a query. We define the problem of retrieving a group of spatial web objects such that the group's keywords cover the query's keywords and such that objects are nearest to the query location and have the lowest inter-object distances. Specifically, we study two variants of this problem, both of which are NP-complete. We devise exact solutions as well as approximate solutions with provable approximation bounds to the problems. We present empirical studies that offer insight into the efficiency and accuracy of the solutions.

With the growing use of location-based services, location privacy attracts increasing attention from users, industry, and the research community. While considerable effort has been devoted to inventing techniques that prevent service providers from knowing a user's exact location, relatively little attention has been paid to enabling so-called peer-wise privacy---the protection of a user's location from unauthorized peer users. This paper identifies an important efficiency problem in existing peer-privacy approaches that simply apply a filtering step to identify users that are located in a query range, but that do not want to disclose their location to the querying peer. To solve this problem, we propose a novel, privacy-policy enabled index called the PEB-tree that seamlessly integrates location proximity and policy compatibility. We propose efficient algorithms that use the PEB-tree for processing privacy-aware range and k NN queries. Extensive experiments suggest that the PEB-tree enables efficient query processing.

Studies find that at least 20% of web queries have local intent; and the fraction of queries with local intent that originate from mobile properties may be twice as high. The emergence of standardized support for location providers in web browsers, as well as of providers of accurate locations, enables so-called hyper-local web querying where the location of a user is accurate at a much finer granularity than with IP-based positioning. This paper addresses the problem of determining the importance of points of interest, or places, in local-search results. In doing so, the paper proposes techniques that exploit logged directions queries. A query that asks for directions from a location a to a location b is taken to suggest that a user is interested in traveling to b and thus is a vote that location b is interesting. Such user-generated directions queries are particularly interesting because they are numerous and contain precise locations. Specifically, the paper proposes a framework that takes a user location and a collection of near-by places as arguments, producing a ranking of the places. The framework enables a range of aspects of directions queries to be exploited for the ranking of places, including the frequency with which places have been referred to in directions queries. Next, the paper proposes an algorithm and accompanying data structures capable of ranking places in response to hyper-local web queries. Finally, an empirical study with very large directions query logs offers insight into the potential of directions queries for the ranking of places and suggests that the proposed algorithm is suitable for use in real web search engines.

Given a set of multidimensional points, a skyline query returns the interesting points that are not dominated by other points. It has been observed that the actual cardinality (s) of a skyline query result may differ substantially from the desired result cardinality (k), which has prompted studies on how to reduce s for the case where k;s. Based on these observations, the paper proposes a new approach, called skyline ordering, that forms a skyline-based partitioning of a given data set such that an order exists among the partitions. Then, set-wide maximization techniques may be applied within each partition. Efficient algorithms are developed for skyline ordering and for resolving size constraints using the skyline order. The results of extensive experiments show that skyline ordering yields a flexible framework for the efficient and scalable resolution of arbitrary size constraints on skyline queries.

Users of mobile services wish to retrieve nearby points of interest without disclosing their locations to the services. This article addresses the challenge of optimizing the query performance while satisfying given location privacy and query accuracy requirements. The article's proposal, SpaceTwist, aims to offer location privacy for k nearest neighbor ( k NN) queries at low communication cost without requiring a trusted anonymizer. The solution can be used with a conventional DBMS as well as with a server optimized for location-based services. In particular, we believe that this is the first solution that expresses the server-side functionality in a single SQL statement. In its basic form, SpaceTwist utilizes well-known incremental NN query processing on the server. When augmented with a server-side granular search technique, SpaceTwist is capable of exploiting relaxed query accuracy guarantees for obtaining better performance. We extend SpaceTwist with so-called ring ranking, which improves the communication cost, delayed termination, which improves the privacy afforded the user, and the ability to function in spatial networks in addition to Euclidean space. We report on analytical and empirical studies that offer insight into the properties of SpaceTwist and suggest that our proposal is indeed capable of offering privacy with very good performance in realistic settings.

Online social networks often involve very large numbers of users who share very large volumes of content. This content is increasingly being tagged with geo-spatial and temporal coordinates that may then be used in services. For example, a service may retrieve photos taken in a certain region. The resulting geo-aware social networks (GeoSNs) pose privacy threats beyond those found in location-based services. Content published in a GeoSN is often associated with references to multiple users, without the publisher being aware of the privacy preferences of those users. Moreover, this content is often accessible to multiple users. This renders it difficult for GeoSN users to control which information about them is available and to whom it is available. This paper addresses two privacy threats that occur in GeoSNs: location privacy and absence privacy. The former concerns the availability of information about the presence of users in specific locations at given times, while the latter concerns the availability of information about the absence of an individual from specific locations during given periods of time. The challenge addressed is that of supporting privacy while still enabling useful services. We believe this is the first paper to formalize these two notions of privacy and to propose techniques for enforcing them. The techniques offer privacy guarantees, and the paper reports on empirical performance studies of the techniques.

The availability of indoor positioning renders it possible to deploy location-based services in indoor spaces. Many such services will benefit from the efficient support for k nearest neighbor (kNN) queries over large populations of indoor moving objects. However, existing kNN techniques fall short in indoor spaces because these differ from Euclidean and spatial network spaces and because of the limited capabilities of indoor positioning technologies. To contend with indoor settings, we propose the new concept of minimal indoor walking distance (MIWD) along with algorithms and data structures for distance computing and storage; and we differentiate the states of indoor moving objects based on a posi-tioning device deployment graph, utilize these states in effective object indexing structures, and capture the uncertainty of object lo-cations. On these foundations, we study the probabilistic threshold kNN (PTkNN) query. Given a query location q and a probability threshold T, this query returns all subsets of k objects that have probability larger than T of containing the kNN query result of q. We propose a combination of three techniques for processing this query. The first uses the MIWD metric to prune objects that are too far away. The second uses fast probability estimates to prune unqualified objects and candidate result subsets. The third uses ef-ficient probability evaluation for computing the final result on the remaining candidate subsets. An empirical study using both syn-thetic and real data shows that the techniques are efficient.

It has long been observed that database management systems focus on traditional business applications, and that few people use a database management system outside their workplace. Many have wondered what it will take to enable the use of data management technology by a broader class of users and for a much wider range of applications.

Community Question Answering (CQA) has emerged as a popular type of service where users ask and answer questions and access historical question-answer pairs. CQA archives contain very large volumes of questions organized into a hierarchy of categories. As an essential function of CQA services, question retrieval in a CQA archive aims to retrieve historical question-answer pairs that are relevant to a query question. In this paper, we present a new approach to exploiting category information of questions for improving the performance of question retrieval, and we apply the approach to existing question retrieval models, including a state-of-the-art question retrieval model. Experiments conducted on real CQA data demonstrate that the proposed techniques are capable of outperforming a variety of baseline methods significantly.

The location-aware keyword query returns ranked objects that are near a query location and that have textual descriptions that match query keywords. This query occurs inherently in many types of mobile and traditional web services and applications, e.g., Yellow Pages and Maps services. Previous work considers the potential results of such a query as being independent when ranking them. However, a relevant result object with nearby objects that are also relevant to the query is likely to be preferable over a relevant object without relevant nearby objects. The paper proposes the concept of prestige-based relevance to capture both the textual relevance of an object to a query and the effects of nearby objects. Based on this, a new type of query, the Location-aware top- k Prestige-based Text retrieval (L k PT) query, is proposed that retrieves the top- k spatial web objects ranked according to both prestige-based relevance and location proximity. We propose two algorithms that compute L k PT queries. Empirical studies with real-world spatial data demonstrate that L k PT queries are more effective in retrieving web objects than a previous approach that does not consider the effects of nearby objects; and they show that the proposed algorithms are scalable and outperform a baseline approach significantly.

With the increasing deployment and use of GPS-enabled devices, massive amounts of GPS data are becoming available. We propose a general framework for the mining of semantically meaningful, significant locations, e.g., shopping malls and restaurants, from such data. We present techniques capable of extracting semantic locations from GPS data. We capture the relationships between locations and between locations and users with a graph. Significance is then assigned to locations using random walks over the graph that propagates significance among the locations. In doing so, mutual reinforcement between location significance and user authority is exploited for determining significance, as are aspects such as the number of visits to a location, the durations of the visits, and the distances users travel to reach locations. Studies using up to 100 million GPS records from a confined spatio-temporal region demonstrate that the proposal is effective and is capable of outperforming baseline methods and an extension of an existing proposal.

Optimization of join queries based on average selectivities is suboptimal in highly correlated databases. In such databases, relations are naturally divided into partitions, each partition having substantially different statistical characteristics. It is very compelling to discover such data partitions during query optimization and create multiple plans for a given query, one plan being optimal for a particular combination of data partitions. This scenario calls for the sharing of state among plans, so that common intermediate results are not recomputed. We study this problem in a setting with a routing-based query execution engine based on eddies [1]. Eddies naturally encapsulate horizontal partitioning and maximal state sharing across multiple plans. We define the notion of a conditional join plan , a novel representation of the search space that enables us to address the problem in a principled way. We present a low-overhead greedy algorithm that uses statistical summaries based on graphical models . Experimental results suggest an order of magnitude faster execution time over traditional optimization for high correlations, while maintaining the same performance for low correlations.

Community Question Answering (CQA) has emerged as a popular type of service meeting a wide range of information needs. Such services enable users to ask and answer questions and to access existing question-answer pairs. CQA archives contain very large volumes of valuable user-generated content and have become important information resources on the Web. To make the body of knowledge accumulated in CQA archives accessible, effective and efficient question search is required. Question search in a CQA archive aims to retrieve historical questions that are relevant to new questions posed by users. This paper proposes a category-based framework for search in CQA archives. The framework embodies several new techniques that use language models to exploit categories of questions for improving question-answer search. Experiments conducted on real data from Yahoo! Answers demonstrate that the proposed techniques are effective and efficient and are capable of outperforming baseline methods significantly.

Indoor spaces accommodate large populations of individuals. The continuous range monitoring of such objects can be used as a foundation for a wide variety of applications, e.g., space planning, way finding, and security. Indoor space differs from outdoor space in that symbolic locations, e.g., rooms, rather than Euclidean positions or spatial network locations are important. In addition, positioning based on presence sensing devices, rather than, e.g., GPS, is assumed. Such devices report the objects in their activation ranges. We propose an incremental, query-aware continuous range query processing technique for objects moving in this setting. A set of critical devices is determined for each query, and only the observations from those devices are used to continuously maintain the query result. Due to the limitations of the positioning devices, queries contain certain and uncertain results. A maximum-speed constraint on object movement is used to refine the latter results. A comprehensive experimental study with both synthetic and real data suggests that our proposal is efficient and scalable.

One of the most desired and challenging services in collective transport systems is the real-time prediction of the near-future travel times of scheduled vehicles, especially public buses, thus improving the experience of the transportation users, who may be able to better schedule their travel, and also enabling system operators to perform real-time monitoring. While travel-time prediction has been researched extensively during the past decade, the accuracies of existing techniques fall short of what is desired, and proposed mathematical prediction models are often not transferable to other systems because the properties of the travel-time-related data of vehicles are highly context-dependent, making the models difficult to fit. We propose a framework for evaluating various predictability types of the data independently of the model, and we also compare predictability analysis results of travel times with the actual prediction errors for real bus trajectories. We have applied the proposed framework to real-time data collected from buses operating in Copenhagen, Denmark.

Social networking and content sharing service providers, e.g., Facebook and Google Maps, enable their users to upload and share a variety of user-generated content, including location data such as points of interest. Users wish to share location data through an (untrusted) service provider such that trusted friends can perform spatial queries on the data. We solve the problem by transforming the location data before uploading them. We contribute spatial transformations that re-distribute locations in space and a transformation that employs cryptographic techniques. The data owner selects transformation keys and shares them with the trusted friends. Without the keys, it is infeasible for an attacker to reconstruct the exact original data points from the transformed points. These transformations achieve different tradeoffs between query efficiency and data security. In addition, we describe an attack model for studying the security properties of the transformations. Empirical studies suggest that the proposed methods are secure and efficient.

Online forums contain huge amounts of valuable user-generated content. In current forum systems, users have to passively wait for other users to visit the forum systems and read/answer their questions. The user experience for question answering suffers from this arrangement. In this paper, we address the problem of "pushing" the right questions to the right persons, the objective being to obtain quick, high-quality answers, thus improving user satisfaction. We propose a framework for the efficient and effective routing of a given question to the top-k potential experts (users) in a forum, by utilizing both the content and structures of the forum system. First, we compute the expertise of users according to the content of the forum system--this is to estimate the probability of a user being an expert for a given question based on the previous question answering of the user. Specifically, we design three models for this task, including a profile-based model, a thread-based model, and a cluster-based model. Second, we re-rank the user expertise measured in probability by utilizing the structural relations among users in a forum system. The results of the two steps can be integrated naturally in a probabilistic model that computes a final ranking score for each user. Experimental results show that the proposals are very promising.

This paper presents the implementation of a novel seamless indoor/outdoor positioning service for mobile users.The service is being made available in the Streamspin system(www.streamspin.com), an open platform for the creation and delivery of location-based services. Streamspin seeks to enable the delivery of truly ubiquitous location-based services by integrating GPS and Wi-Fi location fingerprinting. The paper puts focus on key aspects of the seamless handover between outdoor to indoor positioning. Several different handover solutions are presented,and their applicability is evaluated with respect to positioning accuracy and battery consumption of the mobile device.

The tracking of the locations of moving objects in large indoor spaces is important, as it enables a range of applications related to, e.g., security and indoor navigation and guidance. This paper presents a graph model based approach to indoor tracking that offers a uniform data management infrastructure for different symbolic positioning technologies, e.g., Bluetooth and RFID. More specifically, the paper proposes a model of indoor space that comprises a base graph and mappings that represent the topology of indoor space at different levels. The resulting model can be used for one or several indoor positioning technologies. Focusing on RFID-based positioning, an RFID specific reader deployment graph model is built from the base graph model. This model is then used in several algorithms for constructing and refining trajectories from raw RFID readings. Empirical studies with implementations of the models and algorithms suggest that the paper's proposals are effective and efficient.

The conventional Internet is acquiring a geo-spatial dimension. Web documents are being geo-tagged, and geo-referenced objects such as points of interest are being associated with descriptive text documents. The resulting fusion of geo-location and documents enables a new kind of top- k query that takes into account both location proximity and text relevancy. To our knowledge, only naive techniques exist that are capable of computing a general web information retrieval query while also taking location into account. This paper proposes a new indexing framework for location-aware top- k text retrieval. The framework leverages the inverted file for text retrieval and the R-tree for spatial proximity querying. Several indexing approaches are explored within the framework. The framework encompasses algorithms that utilize the proposed indexes for computing the top- k query, thus taking into account both text relevancy and location proximity to prune the search space. Results of empirical studies with an implementation of the framework demonstrate that the paper's proposal offers scalability and is capable of excellent performance.

The increased deployment of sensors and data communication networks yields data management workloads with update loads that are intense, skewed, and highly bursty. Query loads resulting from location-based services are expected to exhibit similar characteristics. In such environments, index structures can easily become performance bottlenecks. We address the need for indexing that is adaptive to the workload characteristics, called workload-aware, in order to cover the space in between maintaining an accurate index, and having no index at all. Our proposal, QU-Trade, extends R-tree type indexing and achieves workload-awareness by controlling the underlying index's filtering quality. QU-Trade safely drops index updates, increasing the overlap in the index when the workload is update-intensive, and it restores the filtering capabilities of the index when the workload becomes query-intensive. This is done in a non-uniform way in space so that the quality of the index remains high in frequently queried regions, while it deteriorates in frequently updated regions. The adaptation occurs online, without the need for a learning phase. We apply QU-Trade to the R-tree and the TPR-tree, and we offer analytical and empirical studies. In the presence of substantial workload skew, QU-Trade can achieve index update costs close to zero and can also achieve virtually the same query cost as the underlying index.

Moving object indexing and query processing is a well studied research topic, with applications in areas such as intelligent transport systems and location-based services. While much existing work explicitly or implicitly assumes a deterministic object movement model, real-world objects often move in more complex and stochastic ways. This paper investigates the possibility of a marriage between moving-object indexing and probabilistic object modeling. Given the distributions of the current locations and velocities of moving objects, we devise an efficient inference method for the prediction of future locations. We demonstrate that such prediction can be seamlessly integrated into existing index structures designed for moving objects, thus improving the meaningfulness of range and nearest neighbor query results in highly dynamic and uncertain environments. The paper reports on extensive experiments on the B x -tree that offer insights into the properties of the paper's proposal.

The use of centralized, real-time position tracking is proliferating in the areas of logistics and public transportation. Real-time positions can be used to provide up-to-date information to a variety of users, and they can also be accumulated for uses in subsequent data analyses. In particular, historical data in combination with real-time data may be used to predict the future travel times of vehicles more accurately, thus improving the experience of the users who rely on such information. We propose a Nearest-Neighbor Trajectory (NNT) technique that identifies the historical trajectory that is the most similar to the current, partial trajectory of a vehicle. The historical trajectory is then used for predicting the future movement of the vehicle. The paper's specific contributions are two-fold. First, we define distance measures and a notion of nearest neighbor that are specific to trajectories of vehicles that travel along known routes. In empirical studies with real data from buses, we evaluate how well the proposed distance functions are capable of predicting future vehicle movements. Second, we propose a main-memory index structure that enables incremental similarity search and that is capable of supporting varying-length nearest neighbor queries.

In a mobile service scenario, users query a server for nearby points of interest but they may not want to disclose their locations to the service. Intuitively, location privacy may be obtained at the cost of query performance and query accuracy. The challenge addressed is how to obtain the best possible performance, subjected to given requirements for location privacy and query accuracy. Existing privacy solutions that use spatial cloaking employ complex server query processing techniques and entail the transmission of large quantities of intermediate result. Solutions that use transformation-based matching generally fall short in offering practical query accuracy guarantees. Our proposed framework, called SpaceTwist, rectifies these shortcomings for k nearest neighbor (kNN) queries. Starting with a location different from the user's actual location, nearest neighbors are retrieved incrementally until the query is answered correctly by the mobile terminal. This approach is flexible, needs no trusted middleware, and requires only well-known incremental NN query processing on the server. The framework also includes a server-side granular search technique that exploits relaxed query accuracy guarantees for obtaining better performance. The paper reports on empirical studies that elicit key properties of SpaceTwist and suggest that the framework offers very good performance and high privacy, at low communication cost.

Applications in areas such as logistics, cargo delivery, and collective transport involve the management of fleets of vehicles that are expected to travel along known routes according to schedules. There is a fundamental need by the infrastructure surrounding the vehicles to know the actual status of the vehicles. Since the vehicles deviate from their schedules due to road construction, accidents, and other unexpected conditions, it is necessary for the vehicles to communicate with the infrastructure. Frequent updates introduce high communication costs, and server-side updates easily become a bottleneck. This paper presents techniques that enable the tracking of vehicle positions and arrival times at scheduled stops with little communication, while still offering the desired accuracy to the infrastructure of the status of the vehicles. Experimental results with real GPS data from buses show that the proposed techniques are capable of reducing the number of updates significantly compared to a state-of-the art approach where vehicles issue updates at pre-defined positions along their routes.

Progress in science and engineering relies on the ability to measure, reliably and in detail, pertinent properties of artifacts under design. Progress in the area of database-index design thus relies on empirical studies based on prototype implementations of indexes. This paper proposes a benchmark that targets techniques for the indexing of the current and near-future positions of moving objects. This benchmark enables the comparison of existing and future indexing techniques. It covers important aspects of such indexes that have not previously been covered by any benchmark. Notable aspects covered include update efficiency, query efficiency, concurrency control, and storage requirements. Next, the paper applies the benchmark to half a dozen notable moving-object indexes, thus demonstrating the viability of the benchmark and offering new insight into the performance properties of the indexes.

As mobile devices with positioning capabilities continue to proliferate, data management for so-called trajectory databases that capture the historical movements of populations of moving objects becomes important. This paper considers the querying of such databases for convoys, a convoy being a group of objects that have traveled together for some time. More specifically, this paper formalizes the concept of a convoy query using density-based notions, in order to capture groups of arbitrary extents and shapes. Convoy discovery is relevant for real-life applications in throughput planning of trucks and carpooling of vehicles. Although there has been extensive research on trajectories in the literature, none of this can be applied to retrieve correctly exact convoy result sets. Motivated by this, we develop three efficient algorithms for convoy discovery that adopt the well-known filter-refinement framework. In the filter step, we apply line-simplification techniques on the trajectories and establish distance bounds between the simplified trajectories. This permits efficient convoy discovery over the simplified trajectories without missing any actual convoys. In the refinement step, the candidate convoys are further processed to obtain the actual convoys. Our comprehensive empirical study offers insight into the properties of the paper's proposals and demonstrates that the proposals are effective and efficient on real-world trajectory data.

This paper describes an open, extendable, and scalable system that supports the delivery of context-dependent content to mobile users. The system enables users to receive content from multiple content providers that matches their demographic data, active profiles, and context such as location and time. The system also allows users to subscribe to specific services. In addition, it allows users to provide their own content and services, by either using the system's publicly available interface or by filling out one of the service-configuration templates.

This paper considers the problem of efficiently maintaining a clustering of a dynamic set of data points that move continuously in two-dimensional Euclidean space. This problem has received little attention and introduces new challenges to clustering. The paper proposes a new scheme that is capable of incrementally clustering moving objects. This proposal employs a notion of object dissimilarity that considers object movement across a period of time, and it employs clustering features that can be maintained efficiently in incremental fashion. In the proposed scheme, a quality measure for incremental clusters is used for identifying clusters that are not compact enough after certain insertions and deletions. An extensive experimental study shows that the new scheme performs significantly faster than traditional ones that frequently rebuild clusters. The study also shows that the new scheme is effective in preserving the quality of moving-object clusters.

Skyline queries are well suited when retrieving data according to multiple criteria. While most previous work has assumed a centralized setting this paper considers skyline querying in a mobile and distributed setting, where each mobile device is capable of holding only a portion of the whole dataset; where devices communicate through mobile ad hoc networks; and where a query issued by a mobile user is interested only in the user’s local area, although a query generally involves data stored on many mobile devices due to the storage limitations. We present techniques that aim to reduce the costs of communication among mobile devices and reduce the execution time on each single mobile device. For the former, skyline query requests are forwarded among mobile devices in a deliberate way, such that the amount of data to be transferred is reduced. For the latter, specific optimization measures are proposed for resource-constrained mobile devices. We conduct extensive experiments to show that our proposal performs efficiently in real mobile devices and simulated wireless ad hoc networks.

This paper assumes a setting where a population of objects move continuously in the Euclidean plane. The position of each object, modeled as a linear function from time to points, is assumed known. In this setting, the paper studies the querying for dense regions. In particular, the paper defines a particular type of density query with desirable properties and then proceeds to propose an algorithm for the efficient computation of density queries. While the algorithm may exploit any existing index for the current and near-future positions of moving objects, the Bx-tree is used. The paper reports on an extensive empirical study, which elicits the performance properties of the algorithm.

This paper describes an approach to incorporating the notion of expiration time into data management based on the relational model. Expiration times indicate when tuples cease to be current in a database. The paper presents a formal data model and a query algebra that handle expiration times transparently and declaratively. In particular, expiration times are exposed to users only on insertion and update, and when triggers fire due to the expiration of a tuple; for queries, they are handled behind the scenes and do not concern the user. Notably, tuples are removed automatically from (materialised) query results as they expire in the (base) relations. For application developers, the benefits of using expiration times are leaner application code, lower transaction volume, smaller databases, and higher consistency for replicated data with lower overhead. Expiration times turn out to be especially useful in open architectures and loosely-coupled systems, which abound on the World Wide Web as well as in mobile networks, be it as Web Services or as ad hoc and intermittent networks of mobile devices.

With the emergence of an infrastructure that enables the geo-positioning of on-line, mobile users, the management of so-called moving objects has emerged as an active area of research. Among the indexing techniques for efficiently answering predictive queries on moving-object positions, the recent Bx-tree is based on the B+-tree and is relatively easy to integrate into an existing DBMS. However, the Bx-tree is sensitive to data skew. This paper proposes a new query processing algorithm for the B^x-tree that fully exploits the available data statistics to reduce the query enlargement that is needed to guarantee perfect recall, thus significantly improving robustness. The new technique is empirically evaluated and compared with four other approaches and with the TPR-tree, a competitor that is based on the R*-tree. The results indicate that the new index is indeed more robust than its predecessor-it significantly reduces the number of I/O operations per query for the workloads considered. In many settings, the TPR-tree is outperformed as well.

With the proliferation of wireless communications and geo-positioning, e-services are envisioned that exploit the positions of a set of continuously moving users to provide context-aware functionality to each individual user. Because advances in disk capacities continue to outperform Moore's Law, it becomes increasingly feasible to store online all the position information obtained from the moving e-service users. With the much slower advances in I/O speeds and many concurrent users, indexing techniques are of the essence in this scenario.Existing indexing techniques come in two forms. Some techniques capture the position of an object up until the time of the most recent position sample, while other techniques represent an object's position as a constant or linear function of time and capture the position from the current time and into the (near) future. This article offers an indexing technique capable of capturing the positions of moving objects at all points in time. The index substantially modifies partial persistence techniques, which support transaction time, to support valid time for monitoring applications. The performance of a timeslice query is independent of the number of past position samples stored for an object. No existing indices exist with these characteristics.

Our goal is to support transaction-time functionality that enables the coexistence of ordinary, non-temporal tables with transaction-time tables. In such a system, each transaction updating a transaction-time or snapshot table must include a timestamp for its updated data that correctly reflects the serialization order of the transactions, including transactions on ordinary tables. A serious issue is coping with SQL CURRENT/spl I.bar/TIME functions, which should return a time consistent with a transaction's timestamp and serialization order. Prior timestamping techniques cannot support such junctions with this desired semantics. We show how to compatibly extend conventional database functionality for transaction-time support by exploiting the database system lock manager and by utilizing a spectrum of optimizations.

Although significant effort has been put into the development of efficient spatio-temporal indexing techniques for moving objects, little attention has been given to the development of techniques that efficiently support queries about the past, present, and future positions of objects. The provisioning of such techniques is challenging, both because of the nature of the data, which reflects continuous movement, and because of the types of queries to be supported. This paper proposes the BBx -index structure, which indexes the positions of moving objects, given as linear functions of time, at any time. The index stores linearized moving-object locations in a forest of B+ -trees. The index supports queries that select objects based on temporal and spatial constraints, such as queries that retrieve all objects whose positions fall within a spatial range during a set of time intervals. Empirical experiments are reported that offer insight into the query and update performance of the proposed technique.

With the continued advances in wireless communications, geo-positioning, and consumer electronics, an infrastructure is emerging that enables location-based services that rely on the tracking of the continuously changing positions of entire populations of service users, termed moving objects. This scenario is characterized by large volumes of updates, for which reason location update technologies become important. A setting is assumed in which a central database stores a representation of each moving object's current position. This position is to be maintained so that it deviates from the user's real position by at most a given threshold. To do so, each moving object stores locally the central representation of its position. Then, an object updates the database whenever the deviation between its actual position (as obtained from a GPS device) and the database position exceeds the threshold. The main issue considered is how to represent the location of a moving object in a database so that tracking can be done with as few updates as possible. The paper proposes to use the road network within which the objects are assumed to move for predicting their future positions. The paper presents algorithms that modify an initial road-network representation, so that it works better as a basis for predicting an object's position; it proposes to use known movement patterns of the object, in the form of routes; and, it proposes to use acceleration profiles together with the routes. Using real GPS-data and a corresponding real road network, the paper offers empirical evaluations and comparisons that include three existing approaches and all the proposed approaches.

With wireless communications and geo-positioning being widely available, it becomes possible to offer new e-services that provide mobile users with information about other mobile objects. This paper concerns active, ordered k-nearest neighbor queries for query and data objects that are moving in road networks. Such queries may be of use in many services.Specifically, we present an easily implementable data model that serves well as a foundation for such queries. We also present the design of a prototype system that implements the queries based on the data model. The algorithm used for the nearest neighbor search in the prototype is presented in detail. In addition, the paper reports on results from experiments with the prototype system.

With the proliferation of mobile computing, the ability to index efficiently the movements of mobile objects becomes important. Objects are typically seen as moving in two-dimensional (x,y) space, which means that their movements across time may be embedded in the three-dimensional (x,y,t) space. Further, the movements are typically represented as trajectories, sequences of connected line segments. In certain cases, movement is restricted, and specifically in this paper, we aim at exploiting that movements occur in transportation networks to reduce the dimensionality of the data. Briefly, the idea is to reduce movements to occur in one spatial dimension. As a consequence, the movement data becomes two-dimensional (x,t). The advantages of considering such lower-dimensional trajectories are the reduced overall size of the data and the lower-dimensional indexing challenge. Since off-the-shelf systems typically do not offer higher-dimensional indexing, this reduction in dimensionality allows us to use such DBMSes to store and index trajectories. Moreover, we argue that, given the right circumstances, indexing these dimensionality-reduced trajectories can be more efficient than using a three-dimensional index. This hypothesis is verified by an experimental study that incorporates trajectories stemming from real and synthetic road networks.

Advances in wireless communications, positioning technology, and other hardware technologies combine to enable a range of applications that use a mobile user's geo-spatial data to deliver online, location-enhanced services, often referred to as location-based services. Assuming that the service users are constrained to a transportation network, this paper develops data structures that model road networks, the mobile users, and stationary objects of interest. The proposed framework encompasses two supplementary road network representations, namely a two-dimensional representation and a graph representation. These capture aspects of the problem domain that are required in order to support the querying that underlies the envisioned location-based services.

Multiple representations occur when information about the same geographic entity is represented electronically more than once. This occurs frequently in practice, and it invariably results in the occurrence of inconsistencies among the different representations. We propose to resolve this situation by introducing a multiple representation management system (MRMS), the schema of which includes rules that specify how to identify representations of the same entity, rules that specify consistency requirements, and rules used to restore consistency when necessary. In this paper, we demonstrate by means of a prototype and a real-world case study that it is possible to implement a multiple representation schema language on top of an object-relational database management system. Specifically, it is demonstrated how it is possible to map the constructs of the language used for specifying the multiple representation schema to functionality available in Oracle. Though some limitations exist, Oracle has proven to be a suitable platform for implementing an MRMS.

Visionaries predict that the Internet will soon extend to billions of wireless devices, or objects, a substantial fraction of which will offer their changing positions to location-based services. This paper assumes an Internet-service scenario where objects that have not reported their position within a specified duration of time are expected to no longer be interested in, or of interest to, the service. Due to the possibility of many "expiring" objects, a highly dynamic database results. The paper presents an R-tree based technique for the indexing of the current positions of such objects. Different types of bounding regions are studied, and new algorithms are provided for maintaining the tree structure. Performance experiments indicate that, when compared to the approach where the objects are not assumed to expire, the new indexing technique can improve search performance by a factor of two or more without sacrificing update performance.

Presents a powerful and easy-to-use technique for aggregation-based data reduction that enables the gradual change of the data from being detailed to being increasingly aggregated. The technique enables huge storage gains while retaining the data that is essential to the users, and it preserves the ability to query original and reduced data in an integrated manner.

With the proliferation of wireless communications and the rapid advances in technologies for tracking the positions of continuously moving objects, algorithms for efficiently answering queries about large numbers of moving objects increasingly are needed. One such query is the reverse nearest neighbor (RNN) query that returns the objects that have a query object as their closest object. While algorithms have been proposed that compute RNN queries for non-moving objects, there have been no proposals for answering RNN queries for continuously moving objects. Another such query is the nearest neighbor (NN) query, which has been studied extensively and in many contexts. Like the RNN query, the NN query has not been explored for moving query and data points. This paper proposes an algorithm for answering RNN queries for continuously moving points in the plane. As a part of the solution to this problem and as a separate contribution, an algorithm for answering NN queries for continuously moving points is also proposed. The results of performance experiments are reported.

Multiple representation of geographic information occurs when a real-world entity is represented more than once in the same or different databases. In this paper we propose a new approach to the modeling of multiply represented entities and the relationships among the entities and their representations. A multiple representation management system is outlined that can manage multiple representations consistently over a number of autonomous databases. Central to our approach is the multiple representation schema language that is used to configure the system. It provides an intuitive and declarative means of modeling multiple representations and specifying rules that are used to maintain consistency, match objects representing the same entity, and restore consistency if necessary.

Rapid, sustained advances in key computing hardware technologies combine to enable a new class of computing services that aim to meet the needs of mobile users. These ubiquitous and intelligent services adapt to each user's particular preferences and current circumstances, i.e., they are personalized. The services exploit data available front multiple sources, including data on past interactions with users, data accessible via the Internet, and data obtained from sensors. The user's geographical location is particularly central to these services. We outline some of the research challenges that aim to meet the computing of such services. In particular, focus is on update and query processing in the context of georeferenced data, where certain challenges related to the data representation, indexing and precomputation are described.

Time-referenced data are pervasive in most real-world databases. Recent advances in temporal query languages show that such database applications may benefit substantially from built-in temporal support in the DBMS. To achieve this, temporal query optimization and evaluation mechanisms must be provided, either within the DBMS proper or as a source level translation from temporal queries to conventional SQL. This paper proposes a new approach: using a middleware component on top of a conventional DBMS. This component accepts temporal SQL statements and produces a corresponding query plan consisting of algebraic as well as regular SQL parts. The algebraic parts are processed by the middleware, while the SQL parts are processed by the DBMS. The middleware uses performance feedback from the DBMS to adapt its partitioning of subsequent queries into middleware and DBMS parts. The paper describes the architecture and implementation of the temporal middleware component, termed TANGO, which is based on the Volcano extensible query optimizer and the XXL query processing library. Experiments with the system demonstrate the utility of the middleware's internal processing capability and its cost-based mechanism for apportioning the processing between the middleware and the underlying DBMS.

Most real-world databases contain substantial amounts of time-referenced, or temporal, data. Recent advances in temporal query languages show that such database applications may benefit substantially from built-in temporal support in the DBMS. To achieve this, temporal query representation, optimization, and processing mechanisms must be provided. This paper presents a foundation for query optimization that integrates conventional and temporal query optimization and is suitable for both conventional DBMS architectures and ones where the temporal support is obtained via a layer on top of a conventional DBMS. This foundation captures duplicates and ordering for all queries, as well as coalescing for temporal queries, thus generalizing all existing approaches known to the authors. It includes a temporally extended relational algebra to which SQL and temporal SQL queries may be mapped, six types of algebraic equivalences, concrete query transformation rules that obey different equivalences, a procedure for determining which types of transformation rules are applicable for optimizing a query, and a query plan enumeration algorithm.

On-Line Analytical Processing (OLAP) systems based on a multidimensional view of data have found widespread use in business applications and are being used increasingly in non-standard applications.These systems provide good performance and ease-of-use.However, the complex structures and relationships inherent in data in non-standard applications are not accommodated well by OLAP systems.In contrast, object database systems are built to handle such complexity, but do not support OLAP-type querying well.This paper presents the concepts and techniques underlying a flexible, "multi-model" federated system that enables OLAP users to exploit simultaneously the features of OLAP and object database systems.The system allows data to be handled using the most appropriate data model and technology: OLAP systems for multidimensional data and object database systems for more complex, general data.Additionally, physical data integration can be avoided.As a vehicle for demonstrating the capabilities of the system, a prototypical OLAP language is defined and extended to naturally support queries that involve data in object databases.The language permits selection criteria that reference object data, queries that return combinations of OLAP and object data, and queries that group multidimensional data according to object data.The system is designed to be aggregation-safe, in the sense that it exploits the aggregation semantics of the data to prevent incorrect or meaningless query results.A prototype implementation of the system is reported.

Most real-world database applications contain a substantial portion of time references, or temporal data. Recent advances in temporal query languages show that such database applications could benefit substantially from built-in temporal support in the DBMS. To achieve this, temporal query representation, optimization and processing mechanisms must be provided. This paper presents a general algebraic foundation for query optimization that integrates conventional and temporal query optimization and is suitable for providing temporal support both via a stand-alone temporal DBMS and via a layer on top of a conventional DBMS. By capturing duplicate removal and retention and order preservation for all queries, as well as coalescence for temporal queries, this foundation formalizes and generalizes existing approaches.

The coming years will witness dramatic advances in wireless communications as well as positioning technologies. As a result, tracking the changing positions of objects capable of continuous movement is becoming increasingly feasible and necessary. The present paper proposes a novel, R*-tree based indexing technique that supports the efficient querying of the current and projected future positions of such moving objects. The technique is capable of indexing objects moving in one-, two-, and three-dimensional space. Update algorithms enable the index to accommodate a dynamic data set, where objects may appear and disappear, and where changes occur in the anticipated positions of existing objects. A comprehensive performance study is reported.

Most data managed by existing, real-world database applications is time referenced. Often, two temporal aspects of data are of interest, namely valid time, when data is true in the mini-world, and transaction time, when data is current in the database, resulting in so-called bitemporal data. Like spatial data, bitemporal data thus has associated two-dimensional regions. Such data is in part naturally now-relative: some data is true until the current time, and some data is part of the current database state. So, unlike for spatial data, bitemporal data regions may grow continuously. Existing indices, e.g., B/sup +/and R-trees, typically do not contend well with even small amounts of now-relative data. In contrast, the 4-R index presented in the paper is capable of indexing general bitemporal data efficiently. The different kinds of growing data regions are transformed into stationary regions, which are then indexed by R*-trees. Queries are also transformed to counter the data transformations, yielding a technique with perfect precision and recall. Performance studies indicate that the technique is competitive with the best existing index; and unlike this existing index, the new technique does not require extension of the DBMS kernel.

This paper presents a conceptual modeling language for spatiotemporal applications that offers built-in support for capturing spatially referenced, time-varying information. More specifically, the well-known object-oriented Unified Modeling Language (UML) is extended to capture the semantics of spatiotemporal data. The extension, Extended Spatiotemporal UML, maintains language clarity and simplicity by introducing a small base set of fundamental modeling constructs: spatial, temporal, and thematic. These constructs can then be combined and applied at attribute, attribute group, association, and/or class levels of the object-oriented model; where the attribute group is an additional construct introduced for attributes with the same spatiotemporal properties. A formal functional specification of the semantic modeling constructs and their symbolic combinations is given and an example is used to illustrate the simplicity and flexibility of this approach.

Spatio-temporal databases deal with geometries changing over time. The goal of our work is to provide a DBMS data model and query language capable of handling such time-dependent geometries, including those changing continuously that describe moving objects . Two fundamental abstractions are moving point and moving region , describing objects for which only the time-dependent position, or position and extent, respectively, are of interest. We propose to present such time-dependent geometries as attribute data types with suitable operations, that is, to provide an abstract data type extension to a DBMS data model and query language. This paper presents a design of such a system of abstract data types. It turns out that besides the main types of interest, moving point and moving region, a relatively large number of auxiliary data types are needed. For example, one needs a line type to represent the projection of a moving point into the plane, or a “moving real” to represent the time-dependent distance of two points. It then becomes crucial to achieve (i) orthogonality in the design of the system, i.e., type constructors can be applied unifomly; (ii) genericity and consistency of operations, i.e., operations range over as many types as possible and behave consistently; and (iii) closure and consistency between structure and operations of nontemporal and related temporal types. Satisfying these goal leads to a simple and expressive system of abstract data types that may be integrated into a query language to yield a powerful language for querying spatio-temporal data, including moving objects. The paper formally defines the types and operations, offers detailed insight into the considerations that went into the design, and exemplifies the use of the abstract data types using SQL. The paper offers a precise and conceptually clean foundation for implementing a spatio-temporal DBMS extension.

In order to better support current and new applications, the major DBMS vendors are stepping beyond uninterpreted binary large objects, termed BLOBs, and are beginning to offer extensibility features that allow external developers to extend the DBMS with, e.g., their own data types and accompanying access methods. Existing solutions include DB2 extenders, Informix DataBlades, and Oracle cartridges. Extensible systems offer new and exciting opportunities for researchers and third party developers alike. The paper reports on an implementation of an Informix DataBlade for the GR-tree, a new R-tree based index. This effort represents a stress test of the perhaps currently most extensible DBMS, in that the new DataBlade aims to achieve better performance, not just to add functionality. The paper provides guidelines for how to create an access method DataBlade, describes the sometimes surprising challenges that must be negotiated during DataBlade development, and evaluates the extensibility of the Informix Dynamic Server.

Online Analytical Processing (OLAP) systems considerably ease the process of analyzing business data and have become widely used in industry. Such systems primarily employ multidimensional data models to structure their data. However current multidimensional data models fall short in their abilities to model the complex data found in some real world application domains. The paper presents nine requirements to multidimensional data models, each of which is exemplified by a real world, clinical case study. A survey of the existing models reveals that the requirements not currently met include support for many-to-many relationships between facts and dimensions, built-in support for handling chance and time, and support for uncertainty as well as different levels of granularity in the data. The paper defines an extended multidimensional data model, and an associated algebra, which address all nine requirements.

Online analytical processing (OLAP) technologies are being used widely, but the lack of effective means of handling data imprecision, which occurs when exact values are not known precisely or are entirely missing, represents a major obstacle in applying these technologies in many domains. The paper develops techniques for handling imprecision that aim to maximally reuse existing OLAP modeling constructs such as dimension hierarchies and granularities. With imprecise data available in the database, queries are tested to determine whether or not they may be answered precisely given the available data; if not, alternative queries unaffected by the imprecision are suggested. When processing queries affected by imprecision, techniques are proposed that take into account the imprecision in the grouping of the data, in the subsequent aggregate computation, and in the presentation of the imprecise result to the user. The approach is capable of exploiting existing OLAP query processing techniques such as pre-aggregation, yielding an effective approach with low computational overhead and that may be implemented using current technology.

Data warehouses as well as a wide range of other databases exhibit a strong temporal orientation: it is important to track the temporal variation of data over several months or years. In addition, databases often exhibit append-only characteristics where old data is retained while new data is appended. Performing joins efficiently on large databases such as these is essential to obtain good overall query processing performance. The paper presents a sort-merge based incremental algorithm for time oriented data. While incremental computation techniques have proven competitive in many settings, they also introduce a space overhead in the form of differential files. For the temporal data explored here, this overhead is avoided because the differential files are already part of the database. In addition, data is naturally sorted, leaving only merging. The incremental algorithm works in a partitioned storage environment and does not assume the availability of indices, making it a competitor to sort based and nested-loop joins. The paper presents analytical as well as simulation based characterizations of the performance of the join.