Accurate spatio-temporal traffic prediction is essential for optimizing urban traffic management and resource allocation. To reduce the cost and complexity of cross-city deployment, recent studies have explored spatio-temporal foundation models capable of accurate zero-shot prediction. However, these models are limited to single-modal data, which restricts their capacity to capture the complexity of real-world traffic dynamics. The increasing availability of multi-modality data—such as satellite imagery and points of interest (POI)—offers a promising avenue for enhancing cross-city traffic prediction by providing richer background contexts. Despite this potential, developing foundational models for multi-modality spatio-temporal prediction presents two challenges: the availability and quality of multi-modality data vary significantly across cities, with some cities lacking certain modalities or containing noisy information; and spatial patterns are highly localized and specific to individual regions, which hinders generalization. To address these challenges, we propose MoST, a foundation model for multi-modality spatio-temporal traffic prediction. We introduce a Multi-modality Refinement Module that encodes available modality data and adaptively selects task-relevant modalities while suppressing noisy modalities. Furthermore, we design a Spatio-Temporal Prediction Module that incorporates a spatial expert selection mechanism guided by multi-modality cues. This mechanism dynamically identifies region-specific spatial patterns and assigns appropriate spatial experts to model local dependencies. Finally, we conduct extensive experiments on real-world datasets to validate the superior performance and strong generalization capability of MoST.

Accurate prediction of waterlogging depth (WD) is a critical task for urban emergency management and smart-city development. Traditional hydrological forecasting models rely heavily on high-precision weather and terrain data, and struggle to adapt to dynamic rainfall patterns. Recently, deep learning-based methods have shown promising results in capturing complex dynamic patterns. However, these data-driven models face challenges in modeling hydrological mechanisms and effectively fusing multimodal data. To address these limitations, we propose Waterlogformer, a multimodal model for WD prediction. Waterlogformer employs a dual-branch architecture to jointly model WD and rainfall time series alongside static geospatial features. It computes cumulative rainfall based on terrain to incorporate water flow accumulation mechanisms, thereby effectively capturing the impact of rainfall on WD. Furthermore, a Multimodal Fusion Prediction Module fuses multimodal data, enhanced by spatial contrastive learning to improve the model's understanding of geographic relationships. Experiment results on a real-world dataset demonstrate the superior performance of Waterlogformer. The source code is available at https://github.com/decisionintelligence/Waterlogformer.

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%.

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 financial market plays a crucial role in the modern economy by influencing capital allocation, corporate valuation, and investor behavior. However, its complex dependencies and non-stationary dynamics present significant challenges for financial stock prediction. Previous predictive approaches are typically categorized into Univariate Time Series (UTS) and Multivariate Time Series (MTS) paradigms. UTS methods overlook both cross-feature and cross-stock influences, while MTS methods can only capture one of these simultaneously. Although some recent approaches claim to model 3D Multivariate Time Series (3D-MTS) dependencies, they often discard substantial information and fail to capture the dynamics of the stock market. To address these limitations, we propose FinD3, a Financial 3D model using Dual cubic state spaces and Dynamic hypergraphs. To extract the inherent complex relationships in 3D-MTS, we propose a novel Dual Cubic State Space Model (DCSSM) to capture both cross-feature and cross-stock patterns. Furthermore, to more accurately reflect the dynamics of the stock market, we present an Evolving Hypergraph Attention (EHA) module, which captures dynamic changes in financial markets and updates the hypergraph based on a priori hypergraph. Experimental results demonstrate that FinD3 achieves state-of-the-art performance in quantitative trading performance on two real-world stock market datasets, offering a promising solution to practical quantitative trading challenges. The code is available at: https://github.com/decisionintelligence/FinD3.

Accurate land deformation prediction using InSAR (Interferometric Synthetic Aperture Radar) technology is crucial for early warning of geological disasters. However, existing prediction methods face two major challenges: cross-area association bottleneck and inadequate handling of temporal distribution heterogeneity. To address these challenges, we propose Multi-modal Adaptive Association Learning framework (MAAL). For the spatial knowledge transfer challenge, we introduce a cross-area multi-modal association learning module that integrates multi-modal (InSAR and geological text) data to enable knowledge transfer between areas with similar geological characteristics. For temporal distribution heterogeneity, we develop an adaptive evolution stage recognition module that uses distribution routers to identify different temporal patterns, then applies corresponding linear extractors to model the heterogeneous landslide evolution. Experimental validation on 889 hazardous areas demonstrates that MAAL outperforms baselines.

Time series classification (TSC) is an important task in time series analysis. Existing TSC methods mainly train on each single domain separately, suffering from a degradation in accuracy when the samples for training are insufficient in certain domains. The pre-training and fine-tuning paradigm provides a promising direction for solving this problem. However, time series from different domains are substantially divergent, which challenges the effective pre-training on multi-source data and the generalization ability of pre-trained models. To handle this issue, we introduce Augmented Series and Image Contrastive Learning for Time Series Classification (AimTS), a pre-training framework that learns generalizable representations from multi-source time series data. We propose a two-level prototype-based contrastive learning method to effectively utilize various augmentations in multi-source pre-training, which learns representations for TSC that can be generalized to different domains. In addition, considering augmentations within the single time series modality are insufficient to fully address classification problems with distribution shift, we introduce the image modality to supplement structural information and establish a series-image contrastive learning to improve the generalization of the learned representations for TSC tasks. Extensive experiments show that after multi-source pre-training, AimTS achieves good generalization performance, enabling efficient learning and even few-shot learning on various downstream TSC datasets.

Recent research in Text-to-SQL translation has primarily adopted in-context learning methods leveraging large language models (LLMs), achieving significant progress. However, these methods face challenges in adapting to natural language questions of varying difficulty and the relevance of the few-shot examples provided. In this paper, we propose an adaptive in-context learning approach with difficulty-aware instruction and retrieval-augmented generation to enhance the performance of Text-to-SQL translation (AID-SQL). First, we introduce adaptive instructions for LLMs, which employ precise difficulty classification to apply difficulty-adaptive generative guidelines and chain of thought (CoT) templates for varying difficulty levels. We automatically incorporate few-shot examples retrieved through the knowledge base into the CoT template to construct CoT-enhanced examples, which improves the capability of LLMs with retrieval-augmented generation (RAG). Furthermore, considering that current RAG methods struggle to effectively measure the contribution of retrieved examples in solving the specific task of Text-to-SQL translation, we train a ranking model that can better bridge the semantic and structural gap between NL questions and SQL queries. This approach can better understand semantic information and allows for retrieving examples that are more beneficial to the final problem-solving. We evaluate our method on five benchmarks. Our method achieves competitive performance compared with existing methods.

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.

Patch-wise Transformer based time series forecasting achieves superior accuracy. However, this superiority relies heavily on intricate model design with massive parameters, rendering both training and inference expensive, thus preventing their deployments on edge devices with limited resources and low latency requirements. In addition, existing methods often work in an autoregressive manner, which take into account only historical values, but ignore valuable, easy-to-obtain context information, such as weather forecasts, date and time of day. To contend with the two limitations, we propose LiPFormer, a novel Lightweight Patch-wise Transformer with weak data enriching. First, to simplify the Transformer backbone, LiPFormer employs a novel lightweight cross-patch attention and a linear transformationbased attention to eliminate Layer Normalization and Feed Forward Network, two heavy components in existing Transformers. Second, we propose a lightweight, weak data enriching module to provide additional, valuable weak supervision to the training. It enhances forecasting accuracy without significantly increasing model complexity as it does not involve expensive, human-labeling but using easily accessible context information. This facilitates the weak data enriching to plug-and-play on existing models. Extensive experiments on nine benchmark time series datasets demonstrate that LiPFormer outperforms state-of-the-art methods in accuracy, while significantly reducing parameter scale, training duration, and GPU memory usage. Deployment on an edge device reveals that LiPFormer takes only 1/3 inference time compared to classic Transformers. In addition, we demonstrate that the weak data enriching can integrate seamlessly into various Transformer based models to enhance their accuracy, suggesting its generality.

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.

Developing effective path representations has become increasingly essential across various fields within intelligent transportation. Although pre-trained path representation learning models have shown improved performance, they predominantly focus on the topological structures from single modality data, i.e., road networks, overlooking the geometric and contextual features associated with path-related images, e.g., remote sensing images. Similar to human understanding, integrating information from multiple modalities can provide a more comprehensive view, enhancing both representation accuracy and generalization. However, variations in information granularity impede the semantic alignment of road network-based paths (road paths) and image-based paths (image paths), while the heterogeneity of multi-modal data poses substantial challenges for effective fusion and utilization. In this paper, we propose a novel Multi-modal, Multi-granularity Path Representation Learning Framework (MM-Path), which can learn a generic path representation by integrating modalities from both road paths and image paths. To enhance the alignment of multi-modal data, we develop a multi-granularity alignment strategy that systematically associates nodes, road sub-paths, and road paths with their corresponding image patches, ensuring the synchronization of both detailed local information and broader global contexts. To address the heterogeneity of multi-modal data effectively, we introduce a graph-based cross-modal residual fusion component designed to comprehensively fuse information across different modalities and granularities. Finally, we conduct extensive experiments on two large-scale real-world datasets under two downstream tasks, validating the effectiveness of the proposed MM-Path.

Spatio-Temporal (ST) data science, which includes sensing, managing, and mining large-scale data across space and time, is fundamental to understanding complex systems in domains such as urban computing, climate science, and intelligent transportation. Traditional deep learning approaches have significantly advanced this field, particularly in the stage of ST data mining. However, these models remain task-specific and often require extensive labeled data. Inspired by the success of Foundation Models (FM), especially large language models, researchers have begun exploring the concept of Spatio-Temporal Foundation Models (STFMs) to enhance adaptability and generalization across diverse ST tasks. Unlike prior architectures, STFMs empower the entire workflow of ST data science, ranging from data sensing, management, to mining, thereby offering a more holistic and scalable approach. Despite rapid progress, a systematic study of STFMs for ST data science remains lacking. This survey aims to provide a comprehensive review of STFMs, categorizing existing methodologies and identifying key research directions to advance ST general intelligence.

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.

Probabilistic time series imputation has been widely applied in real-world scenarios due to its ability for uncertainty estimation and denoising diffusion probabilistic models (DDPMs) have achieved great success in probabilistic time series imputation tasks with its power to model complex distributions. However, current DDPM-based probabilistic time series imputation methodologies are confronted with two types of challenges: 1) The backbone modules of the denoising parts are not capable of achieving sequence modeling with low time complexity. 2) The architecture of denoising modules can not handle the dependencies in the time series data effectively. To address the first challenge, we explore the potential of state space model, namely Mamba, as the backbone denoising module for DDPMs. To tackle the second challenge, we carefully devise several SSM-based blocks for time series data modeling. Experimental results demonstrate that our approach can achieve state-of-the-art time series imputation results on multiple real-world datasets. Our datasets and code are available at https://github.com/decisionintelligence/SSD-TS/

Multivariate time series forecasting is crucial for various applications, such as financial investment, energy management, weather forecasting, and traffic optimization. However, accurate forecasting is challenging due to two main factors. First, real-world time series often show heterogeneous temporal patterns caused by distribution shifts over time. Second, correlations among channels are complex and intertwined, making it hard to model the interactions among channels precisely and flexibly.

Spatio-temporal data are foundational to understanding and modeling dynamic real-world phenomena such as human mobility, traffic flow, epidemic spread, and urban dynamics.With the growing availability of location-aware web data and the rise of intelligent urban infrastructures, analyzing spatio-temporal patterns has become both highly valuable and technically challenging.This tutorial provides a comprehensive overview of spatio-temporal data analytics, unifying perspectives from data management, research methodology, and emerging foundation models.We begin with a review of spatio-temporal data management systems, introducing the core data models, spatial-temporal indexing techniques, and scalable architectures for storing and querying large-scale mobility data.We then delve into trajectory learning, covering methods for prediction, generation, and reconstruction of movement sequences at the individual level.Next, we explore spatio-temporal graph learning, which focuses on forecasting region-level dynamics using dynamic graph neural networks.Multi-region, multi-task, and multi-domain spatio-temporal learning will be identified and introduced in detail.Finally, we present advanced learning frameworks that integrate federated learning, continual learning, and LLM-based approaches to build privacy-preserving, scalable, and adaptive spatio-temporal models.Through the lens of recent methodological and systemlevel advances, this tutorial bridges algorithmic design and practical deployment of spatio-temporal learning systems.It is suitable for researchers and practitioners working in machine learning, data mining, geospatial analysis, and intelligent systems.

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.

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.

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.

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.

Significant wave height (SWH) is a vital metric in marine science, and accurate SWH estimation is crucial for various applications, e.g., marine energy development, fishery, early warning systems for potential risks, etc. Traditional SWH estimation methods that are based on numerical models and physical theories are hindered by computational inefficiencies. Recently, machine learning has emerged as an appealing alternative to improve accuracy and reduce computational time. However, due to limited observational technology and high costs, the scarcity of real-world data restricts the potential of machine learning models. To overcome these limitations, we propose an ocean SWH estimation framework, namely Orca. Specifically, Orca enhances the limited spatio-temporal reasoning abilities of classic LLMs with a novel spatiotemporal aware encoding module. By segmenting the limited buoy observational data temporally, encoding the buoys' locations spatially, and designing prompt templates, Orca capitalizes on the robust generalization ability of LLMs to estimate significant wave height effectively with limited data. Experimental results on the Gulf of Mexico demonstrate that Orca achieves state-of-the-art performance in SWH estimation.

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.

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.

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.

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.

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.

Flink clusters often suffer from hotspot issues where the monitored job delay and CPU usage keep rising and remain high. This necessitates the detection of anomalous time series to pinpoint the hotspot machines. However, the state-of-the-art unsupervised time series anomaly detection (UTAD) methods are ineffective in this scenario. We identify two main reasons for this. First, the hotspot scenario requires us to pay particular attention to Flink-specific anomalies, e.g., slow-rising and high-level anomalies, which the existing methods struggle to address. Second, the state-of-the-art anomaly detection methods often assume that training datasets do not contain anomalies, but the data collected from the running Flink clusters contains noise, which causes these methods to learn anomalous patterns as normal patterns. In this paper, we first conduct experiments to analyze why existing methods fail in the Flink scenario. To tackle these challenges, we propose a cross-contrastive approach to learn the context information for each timestamp to enable Flink-specific anomaly detection. Then, to address noisy anomalies, we incorporate prior knowledge to set an anomaly boundary to prevent the model from learning anomalous patterns. Extensive experiments show that our method not only outperforms existing methods in the Flink scenario but also achieves state-of-the-art results on public benchmark datasets.

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.

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.

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.

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.

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.

Predictive autoscaling is a key enabler for optimizing cloud resource allocation in Alibaba Cloud's computing platforms, which dynamically adjust the Elastic Compute Service (ECS) instances based on predicted user demands to ensure Quality of Service (QoS). However, user demands in the cloud are often highly complex, with high uncertainty and scale-sensitive temporal dependencies, thus posing great challenges for accurate prediction of future demands. These in turn make autoscaling challenging---autoscaling needs to properly account for demand uncertainty while maintaining a reasonable trade-off between two contradictory factors, i.e., low instance running costs vs. low QoS violation risks. To address the above challenges, we propose a novel predictive autoscaling framework MagicScaler , consisting of a Multi-scale attentive Gaussian process based predictor and an uncertainty-aware scaler. First, the predictor carefully bridges the best of two successful prediction methodologies---multi-scale attention mechanisms, which are good at capturing complex, multi-scale features, and stochastic process regression, which can quantify prediction uncertainty, thus achieving accurate demand prediction with quantified uncertainty. Second, the scaler takes the quantified future demand uncertainty into a judiciously designed loss function with stochastic constraints, enabling flexible trade-off between running costs and QoS violation risks. Extensive experiments on three clusters of Alibaba Cloud in different Chinese cities demonstrate the effectiveness and efficiency of MagicScaler , which outperforms other commonly adopted scalers, thus justifying our design choices.

Multiple time series forecasting plays an essential role in many applications. Solutions based on graph neural network (GNN) that deliver state-of-the-art forecasting performance use the relation graph which can capture historical correlations among time series. However, in real world, it is common that correlations among time series evolve across time, resulting in dynamic relation graph, where the future correlations may be different from those in history. To address this problem, we propose multiple time series forecasting with dynamic graph modeling (MTSF-DG) that is able to learn historical relation graphs and predicting future relation graphs to capture the dynamic correlations. We also propose a causal GNN to extract features from both kinds of relation graphs efficiently. Then we propose a reasoning network to explicitly learn the variant influence from historical timestamps to future timestamps for final forecasting. Extensive experiments on six benchmark datasets show that MTSF-DG consistently outperforms state-of-the-art baselines, and justify our design with dynamic relation graph modeling.

Multivariate time series forecasting has long received significant attention in real-world applications, such as energy consumption and traffic prediction. While recent methods demonstrate good forecasting abilities, they have three fundamental limitations. (i).Discrete neural architectures:Interlacing individually parameterized spatial and temporal blocks to encode rich underlying patterns leads to discontinuous latent state trajectories and higher forecasting numerical errors. (ii).High complexity:Discrete approaches complicate models with dedicated designs and redundant parameters, leading to higher computational and memory overheads. (iii).Reliance on graph priors:Relying on predefined static graph structures limits their effectiveness and practicability in real-world applications. In this paper, we address all the above limitations by proposing a continuous model to forecastMultivariateTime series with dynamicGraph neuralOrdinaryDifferentialEquations (MTGODE). Specifically, we first abstract multivariate time series into dynamic graphs with time-evolving node features and unknown graph structures. Then, we design and solve a neural ODE to complement missing graph topologies and unify both spatial and temporal message passing, allowing deeper graph propagation and fine-grained temporal information aggregation to characterize stable and precise latent spatial-temporal dynamics. Our experiments demonstrate the superiorities ofMTGODEfrom various perspectives on five time series benchmark datasets.

PM$_{2.5}$concentration forecasting is important yet challenging. First, complicated local fluctuations in PM$_{2.5}$concentrations disturb modeling global trends. Second, forecasting errors are often accumulated through an autoregressive process. To contend with the two challenges, we propose aCategoryGuidance based PM${_{2.5}}$sequenceForecasting training framework (CGF) to enhance the performance of existing PM${_{2.5}}$concentration forecasting models. CGF contains a Category based Representation Learning (CRL) module and a Category based Self-paced Learning (CSL) module, both of which utilize PM${_{2.5}}$category information that is easily obtained and publicly available. First, CRL employs category information to guide forecasting models to produce more robust hidden representations that are insensitive to local fluctuations, thus alleviating the negative impact of local fluctuations. Second, CSL adaptively selects real PM${_{2.5}}$concentration values versus autoregressive PM${_{2.5}}$forecast values when training forecasting models, helping alleviate error accumulations. The CGF framework is applied to existing PM${_{2.5}}$forecasting models, and the experimental results on two real-world datasets demonstrate that CGF is able to consistently improve the accuracy of existing forecasting models. Furthermore, to validate the generality of CGF, we conduct extensional experiments in two other time-series prediction tasks, including exchange rate forecasting and electricity forecasting. The experimental results also verify the effectiveness of CGF.

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.

The rapid increase of traffic data generated by different sensing systems opens many opportunities to improve transportation services. An important opportunity is to enable stochastic routing that computes the arrival time probabilities for each suggested route instead of only the expected travel time. However, traffic datasets typically have many missing values, which prevents the construction of stochastic speeds. To address this limitation, we propose the Stochastic Spatio-Temporal Graph Convolutional Network (SST-GCN) architecture that accurately imputes missing speed distributions in a road network. SST-GCN combines Temporal Convolutional Networks and Graph Convolutional Networks into a single framework to capture both spatial and temporal correlations between road segments and time intervals. Moreover, to cope with datasets with many missing values, we propose a novel self-adaptive context-aware diffusion process that regulates the propagated information around the network, avoiding the spread of false information. We extensively evaluate the effectiveness of SST-GCN on real-world datasets, showing that it achieves from 4.6% to 50% higher accuracy than state-of-the-art baselines using three different evaluation metrics. Furthermore, multiple ablation studies confirm our design choices and scalability to large road networks.

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.

Traffic time series forecasting is challenging due to complex spatio-temporal dynamics-time series from different locations often have distinct patterns; and for the same time series, patterns may vary across time, where, for example, there exist certain periods across a day showing stronger temporal correlations. Although recent forecasting models, in particular deep learning based models, show promising results, they suf-fer from being spatio-temporal agnostic. Such spatio-temporal agnostic models employ a shared parameter space irrespective of the time series locations and the time periods and they assume that the temporal patterns are similar across locations and do not evolve across time, which may not always hold, thus leading to sub-optimal results. In this work, we propose a framework that aims at turning spatio-temporal agnostic models to spatio-temporal aware models. To do so, we encode time series from different locations into stochastic variables, from which we generate location-specific and time-varying model parameters to better capture the spatio-temporal dynamics. We show how to integrate the framework with canonical attentions to enable spatio-temporal aware attentions. Next, to compensate for the additional overhead introduced by the spatio-temporal aware model parameter generation process, we propose a novel window attention scheme, which helps reduce the complexity from quadratic to linear, making spatio-temporal aware attentions also have competitive efficiency. We show strong empirical evidence on four traffic time series datasets, where the proposed spatio-temporal aware attentions outperform state-of-the-art methods in term of accuracy and efficiency.

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.

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.

Retrosynthetic planning, which aims to find a reaction pathway to synthesize a target molecule, plays an important role in chemistry and drug discovery. This task is usually modeled as a search problem. Recently, data-driven methods have attracted many research interests and shown promising results for retrosynthetic planning. We observe that the same intermediate molecules are visited many times in the searching process, and they are usually independently treated in previous tree-based methods (e.g., AND-OR tree search, Monte Carlo tree search). Such redundancies make the search process inefficient. We propose a graph-based search policy that eliminates the redundant explorations of any intermediate molecules. As searching over a graph is more complicated than over a tree, we further adopt a graph neural network to guide the search over graphs. Meanwhile, our method can search a batch of targets together in the graph and remove the inter-target duplication in the tree-based search methods. Experimental results on two datasets demonstrate the effectiveness of our method. Especially on the widely used USPTO benchmark, we improve the search success rate to 99.47%, advancing previous state-of-the-art performance for 2.6 points.

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.

Ranking paths becomes an increasingly important functionality in many transportation services, where multiple paths connecting a source-destination pair are offered to drivers. We study ranking such paths under specific contexts. More specifically, we model ranking as a regression problem where we assign a ranking score to each path with the help of historical trajectories. To solve the regression problem, we first propose an effective training data enriching method to obtain a compact and diversified set of training paths using historical trajectories. Next, we propose a multi-task learning framework that considers features representing both candidate paths and contexts. Specifically, a road network embedding is proposed to embed paths into feature vectors by considering both road network topology and spatial properties. By modeling different departure times as a temporal graph, graph embedding is used to embed departure times. The objective function not only considers the discrepancies on ranking scores but also the reconstruction errors of the spatial properties of the paths, which in turn improves the final ranking estimation. Empirical studies on a substantial trajectory data set offer insight into the designed properties of the proposed framework and indicating that it is effective and practical in real world settings.

Correlated time series forecasting plays an essential role in many cyber-physical systems, where entities interact with each other over time. To enable accurate forecasting, it is essential to capture both the temporal dynamics and the correlations among different entities. To capture the former, two popular types of models, recurrent neural networks (RNNs) and temporal convolution networks (TCNs), are employed. To capture the latter, a graph is constructed to reflect certain relationships among entities and then graph convolution (GC) is applied upon the graph to capture the correlations among the entities. The state-of-the-art forecasting accuracy is achieved by models that combine RNNs or TCNs with GC. However, they neither capture distinct temporal dynamics that exist among different entities nor consider the entity correlations that evolve across time. In this paper, rather than proposing yet another new end-to-end forecasting model, we aim at providing a framework to enhance existing forecasting models, where we propose generic plugins that can be easily integrated into existing solutions to solve the two challenges and thus further enhance their accuracy. Specifically, we propose two plugin neural networks that are able to better capture distinct temporal dynamics for different entities and dynamic entity correlations across time, so that forecasting accuracy is improved while model parameters to be learned are reduced. Experimental results on three real-world correlated time series data sets demonstrate that the proposed framework with the two plugin networks is able to achieve the above goals.

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.

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.

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.

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.

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.

Modern navigation services often provide multiple paths connecting the same source and destination for users to select. Hence, ranking such paths becomes increasingly important, which directly affects service quality. We present PathRank, a data-driven framework for ranking paths based on historical trajectories. If a trajectory used path P from source s to destination d, PathRank considers this as an evidence that P is preferred over all other paths from s to d. Thus, a path that is similar to P should have a larger ranking score than a path that is dissimilar to P. Based on this intuition, PathRank models path ranking as a regression problem that assigns each path a ranking score. We first propose an effective method to generate a compact set of diversified paths using trajectories as training data. Next, we propose an end-to-end deep learning framework to solve the regression problem. In particular, a spatial network embedding is proposed to embed each vertex to a feature vector by considering the road network topology. Since a path is represented by a sequence of vertices, which is now a sequence of feature vectors after embedding, recurrent neural network is applied to model the sequence. Empirical studies on a substantial trajectory data set offer insight into the designed properties of the proposed framework and indicating that it is effective and practical.

In a cyber-physical system (CPS), different entities often interact with each other across time. With the development of various sensing technologies, the time-varying interactions among entities are often recorded as multiple, correlated time series. A typical CPS is a road transportation system, where the traffic on different road segments interact with each other. Traffic sensors are often deployed to capture travel speeds on different road segments, which results in multiple, potentially correlated, speed time series. Under this setting, an increasingly pertinent task is to forecast future speeds, which is essential in a wide variety of traffic planning scenarios. We present a system for correlated time series forecast. The system is able to employ different learning algorithms to perform correlated time series forecast, which facilities end users to choose the most appropriate algorithm for their specific service. The system is developed and integrated into aSTEP, a spatio-temporal data analytic platform developed by Aalborg University, and is tested using a wide variety of correlated time series data, including a user demand time series from a local mobility-as-a-service company.

High-quality location based services rely on complete and accurate information of road segments. However, the attributes of road segments in online maps are often incomplete. For example, to compute fastest routes, a navigation system requires information, such as speed limits and road categories, of all road segments. While in OpenStreeMap, such attributes are often missing for many road segments. To contend with incomplete attributes, we propose a system that is able to utilize different machine learning techniques, including both non-deep learning and deep learning algorithms, to fill in the missing attributes. The system is developed and integrated into aSTEP, a spatio-temporal data analytic platform developed by Aalborg University, and is tested using data collected from four major Danish cities.

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.

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.

With the rise of sustainable energy sources, such as wind power, the energy production, and thus the energy price, fluctuates. Meanwhile, we are witnessing an increasing amount of electric vehicles, which soon will represent a substantial fraction of the electricity demand. Under this setting, the so-called vehicle-to-grid technology, which enables electric vehicles to sell electricity back to the power grid, appears to be an effective mean to reduce the charging costs for electric vehicles. We demonstrate a system that makes optimal scheduling for electric vehicle fleet owners using vehicle-to-grid. The principle of the scheduling is to charge electric vehicles when electricity is cheap and sell electricity back to the power grid when it is expensive, while making sure that the electric vehicles are sufficiently charged when they need to be used, e.g., 8 am in the morning. The system is integrated as part of aSTEP, a spatio-temporal data analytics platform developed at Aalborg University. In collaboration with a transportation-as-a-service company in Denmark, the system is tested through a use case that involves an electric vehicle fleet.

Thanks to recent e-commerce growth, the parcel delivery industry is booming. We demonstrate a system that provides a practical solution for scheduling and planning parcel delivery routes. Given a parcel delivery workload, e.g., the number of parcels to be delivered and the sizes of the parcels, the system tries to identify a set of delivery routes such that the workload is satisfied and the total delivery cost is minimized. The system is developed on top of aSTEP, a spatio-temporal data analytics platform developed at Aalborg University, and is tested with parcel delivery workloads provided by a large logistic company in Denmark.

Cyber-physical systems often consist of entities that interact with each other over time. Meanwhile, as part of the continued digitization of industrial processes, various sensor technologies are deployed that enable us to record time-varying attributes (a.k.a., time series) of such entities, thus producing correlated time series. To enable accurate forecasting on such correlated time series, this paper proposes two models that combine convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The first model employs a CNN on each individual time series, combines the convoluted features, and then applies an RNN on top of the convoluted features in the end to enable forecasting. The second model adds additional auto-encoders into the individual CNNs, making the second model a multi-task learning model, which provides accurate and robust forecasting. Experiments on a large real-world correlated time series data set suggest that the proposed two models are effective and outperform baselines in most settings.

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.

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.

Motivated by many practical applications in logistics and mobility-as-a-service, we study the top-k optimal sequenced routes (KOSR) querying on large, general graphs where the edge weights may not satisfy the triangle inequality, e.g., road network graphs with travel times as edge weights. The KOSR querying strives to find the top-k optimal routes (i.e., with the top-k minimal total costs) from a given source to a given destination, which must visit a number of vertices with specific vertex categories (e.g., gas stations, restaurants, and shopping malls) in a particular order (e.g., visiting gas stations before restaurants and then shopping malls). To efficiently find the top-k optimal sequenced routes, we propose two algorithms PruningKOSR and StarKOSR. In PruningKOSR, we define a dominance relationship between two partially-explored routes. The partially-explored routes that can be dominated by other partially-explored routes are postponed being extended, which leads to a smaller searching space and thus improves efficiency. In StarKOSR, we further improve the efficiency by extending routes in an A manner. With the help of a judiciously designed heuristic estimation that works for general graphs, the cost of partially explored routes to the destination can be estimated such that the qualified complete routes can be found early. In addition, we demonstrate the high extensibility of the proposed algorithms by incorporating Hop Labeling, an effective label indexing technique for shortest path queries, to further improve efficiency. Extensive experiments on multiple real-world graphs demonstrate that the proposed methods significantly outperform the baseline method. Furthermore, when k = 1, StarKOSR also outperforms the state-of-the-art method for the optimal sequenced route queries.

The classical K Shortest Paths (KSP) problem, which identifies the k shortest paths in a directed graph, plays an important role in many application domains, such as providing alternative paths for vehicle routing services. However, the returned k shortest paths may be highly similar, i.e., sharing significant amounts of edges, thus adversely affecting service qualities. In this paper, we formalize the K Shortest Paths with Diversity (KSPD) problem that identifies top-k shortest paths such that the paths are dissimilar with each other and the total length of the paths is minimized. We first prove that the KSPD problem is NP-hard and then propose a generic greedy framework to solve the KSPD problem in the sense that (1) it supports a wide variety of path similarity metrics which are widely adopted in the literature and (2) it is also able to efficiently solve the traditional KSP problem if no path similarity metric is specified. The core of the framework includes the use of two judiciously designed lower bounds, where one is dependent on and the other one is independent on the chosen path similarity metric, which effectively reduces the search space and significantly improves efficiency. Empirical studies on 5 real-world and synthetic graphs and 5 different path similarity metrics offer insight into the design properties of the proposed general framework and offer evidence that the proposed lower bounds are effective.

We study stochastic routing in the PAth-CEntric (PACE) uncertain road network model. In the PACE model, uncertain travel times are associated with not only edges but also some paths. The uncertain travel times associated with paths are able to well capture the travel time dependency among different edges. This significantly improves the accuracy of travel time distribution estimations for arbitrary paths, which is a fundamental functionality in stochastic routing, compared to classic uncertain road network models where uncertain travel times are associated with only edges. Based on the PACE model, we investigate the shortest path with on-time arrival reliability (SPOTAR) problem. Given a source, a destination, and a travel time budget, the SPOTAR problem aims at finding a path that maximizes the on-time arrival probability. We develop a generic algorithm with different speedup strategies to solve the SPOTAR problem under the PACE model. Empirical studies with substantial GPS trajectory data offer insight into the design properties of the proposed algorithm and confirm that the algorithm is effective.

We demonstrate aSTEP, a spatio-temporal data management and analytics platform developed at Aalborg University (a.k.a. aau) that aims at providing a range of core functionalities for outdoor location-based service, indoor locationbased service, and location-based social networks, which facilitates application developers to develop their own, specific locationbased services on top of aSTEP. aSTEP also consolidates many recent research results on spatio-temporal data management and analytics, and serves as a testbed for exploring advanced solutions to a range of challenges related to spatio-temporal data management and analytics, e.g., Mobility-as-a-Service, dataintensive routing. In addition, from education perspectives, every spring semester aSTEP accommodates some 30 to 40 software engineering students' group-based bachelor projects at the Department of Computer Science, Aalborg University.

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.

The classical K Shortest Paths (KSP) problem, which identifies the k shortest paths in a directed graph, plays an important role in many application domains, such as providing alternative paths for vehicle routing services. However, the returned k shortest paths may be highly similar, i.e., sharing significant amounts of edges, thus adversely affecting service qualities. In this paper, we formalize the K Shortest Paths with Diversity (KSPD) problem that identifies top-k shortest paths such that the paths are dissimilarwith each other and the total length of the paths is minimized. We first prove that the KSPD problem is NP-hard and then propose a generic greedy framework to solve the KSPD problem in the sense that (1) it supports a wide variety of path similarity metrics which are widely adopted in the literature and (2) it is also able to efficiently solve the traditional KSP problem if no path similarity metric is specified. The core of the framework includes the use of two judiciously designed lower bounds, where one is dependent on and the other one is independent on the chosen path similarity metric, which effectively reduces the search space and significantly improves efficiency. Empirical studies on five real-world and synthetic graphs and five different path similarity metrics offer insight into the design properties of the proposed general framework and offer evidence that the proposed lower bounds are effective.

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.

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 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.

When planning routes, drivers usually consider a multitude of different travel costs, e.g., distances, travel times, and fuel consumption. Different drivers may choose different routes between the same source and destination because they may have different driving preferences (e.g., time-efficient driving v.s. fuel-efficient driving). However, existing routing services support little in modeling multiple travel costs and personalization-they usually deliver the same routes that minimize a single travel cost (e.g., the shortest routes or the fastest routes) to all drivers. We study the problem of how to recommend personalized routes to individual drivers using big trajectory data. First, we provide techniques capable of modeling and updating different drivers' driving preferences from the drivers' trajectories while considering multiple travel costs. To recommend personalized routes, we provide techniques that enable efficient selection of a subset of trajectories from all trajectories according to a driver's preference and the source, destination, and departure time specified by the driver. Next, we provide techniques that enable the construction of a small graph with appropriate edge weights reflecting how the driver would like to use the edges based on the selected trajectories. Finally, we recommend the shortest route in the small graph as the personalized route to the driver. Empirical studies with a large, real trajectory data set from 52,211 taxis in Beijing offer insight into the design properties of the proposed techniques and suggest that they are efficient and effective.

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.

Social network platforms and location-based services are increasingly popular in people's daily lives. The combination of them results in location-based social media where people are connected not only through the friendship in the social network but also by their geographical locations in reality. This duality makes it possible to query and make use of social media data in novel ways. In this work, we formulate a novel and useful problem called top-k local user search (TkLUS for short) from tweets with geo-tags. Given a location q, a distance r, and a set of keywords W, the TkLUS query finds the top-k users who have posted tweets relevant to the desired keywords in W at a place within the distance r from q. TkLUS queries are useful in many application scenarios such as friend recommendation, spatial decision, etc. We design a set of techniques to answer such queries efficiently. First, we propose two local user ranking methods that integrate text relevance and location proximity in a TkLUS query. Second, we construct a hybrid index under a scalable framework, which is aware of keywords as well as locations, to organize high volume geo-tagged tweets. Furthermore, we devise two algorithms for processing TkLUS queries. Finally, we conduct an experimental study using real tweet data sets to evaluate the proposed techniques. The experimental results demonstrate the efficiency, effectiveness and scalability of our proposals.

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.

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.

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 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.

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.

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.

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.

Trajectory search plays an important role in various applications such as trip planning and recommendation. However, most existing studies only focus on spatial proximity but ignore individual users' preferences. For example, it is inappropriate to recommend a route containing gravel roads to travelers without off-road vehicles. To accommodate various user preferences, we develop MOIR/UOTS, a trip recommendation system that supports User Oriented Trajectory Search (UOTS) [5], [7] based on our previous MOIR platform [2], [4]. Meanwhile, MOIR/UOTS also functions as a test-bed system for exploring and evaluating novel trajectory searching algorithms. In particular, we demonstrate how ordinary users can interact with MOIR/UOTS to search for trajectories with their preferences, and how MOIR/UOTS helps researchers to evaluate the performance of various algorithms.

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.

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.

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.

There have been major advances on automatically constructing large knowledge bases by extracting relational facts from Web and text sources. However, the world is dynamic: periodic events like sports competitions need to be interpreted with their respective timepoints, and facts such as coaching a sports team, holding political or business positions, and even marriages do not hold forever and should be augmented by their respective timespans. This paper addresses the problem of automatically harvesting temporal facts with such extended time-awareness. We employ pattern-based gathering techniques for fact candidates and construct a weighted pattern-candidate graph. Our key contribution is a system called PRAVDA based on a new kind of label propagation algorithm with a judiciously designed loss function, which iteratively processes the graph to label good temporal facts for a given set of target relations. Our experiments with online news and Wikipedia articles demonstrate the accuracy of this method.

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.

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.

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.

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.