Traffic prediction plays a pivotal role in contemporary web technologies, motivating various intelligent web services such as route planning and remote traffic management. Many recent proposals that target deep learning for traffic prediction solely leverage historical traffic observations to predict future ones. However, traffic prediction is always susceptible to different factors such as road networks and social events, exhibiting different modalities. Most existing methods focus on a single modality, failing to capture the comprehensive traffic patterns among various factors, resulting in sub-optimal performance. Web-sourced geo-images, e.g., satellite imagery, encompass comprehensive contextual information and offer an effective way to represent diverse modalities. To unleash the power of such geo-images, we propose VisionST, a Vision-augmented Spatial-Temporal Neural Network, which coordinates cross-modal traffic prediction with interactive geo-image encoding. To bolster resilience against highly intricate and overlapping traffic patterns, VisionST features a visual semantic extraction mechanism and a pattern-guided aggregation mechanism. The former extracts node-level visual tokens and node-to-node visual relation patterns from geo-referenced images. The latter generates relation patterns that encompass visual, spatial, and temporal aspects, constraining nodes to interact with these relation patterns for contextual information interaction. Extensive experiments on real large-scale datasets offer insight into the effectiveness of the proposed solutions, showing that VisionST consistently outperforms state-of-the-art baselines.

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%.

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

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+.

Velocity control in autonomous driving is an emerging technology that has achieved rapid progress over the last decade. However, existing velocity control studies ignore cascading disturbances in multi-lane scenarios and usually ignore the negative impact caused by harsh velocity decisions. To address these issues, we propose a reinforcement learning-based framework, called RISE (contRol velocIty for autonomouSvEhicle) to make velocity decisions for an autonomous vehicle in multi-lane traffic scenarios. To detect latent disturbances in the traffic flow, we propose a novel state encoder to learn the spatio-temporal correlation between different vehicles based on a well-designed impact graph. Afterward, we introduce an actor-critic paradigm to make velocity decisions with the aid of a hybrid reward function considering four optimization objectives: safety, efficiency, comfort, and impact. In particular, the impact term can penalize the harsh decisions of the autonomous vehicle, thus encouraging it to reduce the negative impacts on traffic flow. Further, we propose an improved RISE (RISE++) framework that incorporates a motion prediction model to augment state features for reasonable decisions, a modification of the reward function for energy efficiency, and a multi-worker paradigm for training efficiency. Extensive experiments offer evidence that the proposed framework can advance the state of the art in terms of effectiveness and efficiency.

Autonomous driving is an emerging technology that has developed rapidly over the last decade, with decision-making remaining a critical challenge, particularly due to its significant role in traffic congestion. In this paper, we propose a novel perception-and-decision framework, calledHEAD, which consists of an enHanced pErception module and a mAneuverDecision module to address this challenge. In the enhanced perception module, a graph-based state prediction model with a strategy of phantom vehicle construction is proposed to address incomplete vehicle features and predict future states in parallel. Then in the maneuver decision module, a deep reinforcement learning-based model is designed to learn a driving policy based on a parameterized action Markov decision process. A hybrid reward function takes into account aspects of safety, efficiency, comfort, and impact to guide the autonomous vehicle to make optimal maneuver decisions. To make our framework applicable to more scenarios, we further propose an improvedHEAD(HEAD++) framework that makes the autonomous vehicle adapt to various road structures, such as lane merging and diverging scenarios. Besides, we develop a style tuning module inHEAD++, which supports personalized driving style tuning. To mitigate high training costs, an efficient style tuning method with approximate gradient descent is proposed to reduce the number of training iterations. Extensive experiments demonstrate the effectiveness of our framework. Compared to state-of-the-art methods,HEAD++ reduces overall traffic disturbance by 23.3%-40.9%, lowers collision risk by 4.5%-17.8%, and improves passenger comfort by 13.1%-30.5%, while maintaining high traffic efficiency.

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%.

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.

Traffic accidents are one of the leading causes of death in the world. As an important part of the design of traffic roads, tunnels bring convenience but also have huge safety risks. To monitor road safety in real time and give timely warnings for drivers in tunnels, where the light is dark, the space is limited, and the signal is unstable, we study the problem of traffic safety evaluation based on macroscopic traffic features in road tunnels. In particular, we transform the problem into a four-classification problem. To overcome the long collection cycle of traffic crash data, we use the time-to-collision index as the standard for dividing safety levels of road sections in tunnels. To achieve the goal of collecting data in real time under the environment constraints of tunnels, we use macroscopic traffic features as input in our model. Specifically, we design a deep learning model, where the lane block can extract the interaction information of sequential road segments in the same lane, and the prediction block can integrate the results of the individual prediction of each lane and the overall prediction. An extensive emprical study with real data offers insight into the effectiveness and efficiency of the proposed model.

With the rapid advancement of mobile networks and the widespread use of mobile devices, spatial crowdsourcing, which involves assigning location-based tasks to mobile workers, has gained significant attention. However, most existing research focuses on task assignment at the current moment, overlooking the fluctuating demand and supply between tasks and workers over time. To address this issue, we introduce an adaptive task assignment problem, which aims to maximize the number of assigned tasks by dynamically adjusting task assignments in response to changing demand and supply. We develop a spatial crowdsourcing framework, namely demand-based adaptive task assignment with dynamic worker availability windows, which consists of two components including task demand prediction and task assignment. In the first component, we construct a graph adjacency matrix representing the demand dependency relationships in different regions and employ a multivariate time series learning approach to predict future task demands. In the task assignment component, we adjust tasks to workers based on these predictions, worker availability windows, and the current task assignments, where each worker has an availability window that indicates the time periods they are available for task assignments. To reduce the search space of task assignments and be efficient, we propose a worker dependency separation approach based on graph partition and a task value function with reinforcement learning. Experiments on real data demonstrate that our proposals are both effective and efficient.

With the rapid evolution of sensing techniques and the proliferation of mobile devices, spatial crowdsourcing (SC) has gained significant attention in both academia and industry. SC involves assigning location-based tasks to mobile workers, with task recommendation playing a key role in helping workers identify suitable and appealing tasks. However, most existing studies focus on task completion rate, worker satisfaction, or efficiency, without consideration of the environmental impact, e.g., pollutant emissions from the increased vehicle usage associated with SC applications like Uber, Lyft, and FoodPanda. In this study, we consider a novel problem of sustainable task recommendation in SC, which aims to minimize the environmental footprint (i.e., pollution) while maintaining acceptable levels of task completion, worker satisfaction, and overall task recommendation efficiency. We develop an innovative Sustainability-Oriented Task Recommendation framework encompassing two major components: speed-driven pollutant emission estimation and task recommendation. Specifically, the pollutant emission estimation component aims to estimate future pollutant emissions based on worker trajectories and speeds, using a context-enhanced spatio-temporal network for road speed prediction. In the task recommendation component, we provide a completion-sensitive recommendation algorithm to maximize the expected number of completed tasks. Further, we design an efficient emission-optimized KM ranking algorithm to minimize emissions. Experiments on real data offer insight into the effectiveness and efficiency of the proposals, providing valuable insights into its potential for sustainable spatial crowdsourcing.

Locality sensitive hashing (LSH) is a representative approach for nearest neighbor (NN) search in high-dimensional spaces, which is able to answer c-approximate NN (c-ANN) queries in sublinear time with constant probability. Existing advanced LSH methods leverage a plurality of novel techniques such as query-aware dynamic bucketing, virtual rehashing, and efficient indexing to achieve state-of-the-art performance. However, they rely on similar random LSH functions, which provides distance estimations that are irrelevant to the given data distribution. Therefore, the quality of the searched candi-dates is suboptimal. In this study, we reformulate the c-ANN query from the perspective of data distribution. Specifically, we propose a novel distribution-aware c-ANN query, which can guarantee the quality of searched results from the query distribution perspective. We introduce an accurately unbiased distance estimator into LSH methods, which can provide more precise distance estimations by modeling the data distribution. We also conduct rigorous theoretical analysis to prove that our methods can correctly answer the distribution-aware c-ANN query with at least a constant probability. Experiments on seven real datasets with different sizes and dimensionalities indicate that the proposed method can achieve better performance than existing LSH methods in terms of efficiency and effectiveness.

Similar subtrajectory search (SimSub) aims to find a subtrajectory (i.e., a segment) from a data trajectory (the trajectory to be queried) that closely resembles the query trajectory. Compared with similar trajectory search, SimSub can capture finer-grained similarity and is vital for various trajectory analysis tasks, such as trajectory clustering and join. However, SimSub may return a subtrajectory with extremely limited length, e.g., a single point, which may not align with the expectations of real-world applications. To solve this issue, we propose a constrained SimSub (cSimSub) problem, where the length of the returned subtrajectory must be greater than or equal to a user-specified integer$C$. We demonstrate that this problem can be solved exactly with a time complexity equivalent to$C$times the complexity of the trajectory distance measurement, given that the distance function can be computed using dynamic programming (DP). We also observe that when$C=1$, the solution of cSimSub differs from the vanilla trajectory distance computation (e.g., DTW) only in the state initialization of the DP matrix. Moreover, SimSub focuses on finding a subtrajectory with successive point indexes, which limits its applicability in certain scenarios, e.g., trajectory simplification. Thus, we extend it to sSimSub for trajectory simplification, aiming to find the most similar non-continuous subsequence of a trajectory to itself, with a length constraint of$C$. The subsequence, i.e., the simplified subtrajectory, obtained from sSimSub can achieve the best self-similarity. We conduct experiments on three public available datasets to demonstrate the effectiveness of the proposals. The results show that integrating sSimSub into typical query methods, e.g., KNN query, can achieve higher accuracy of these methods in simplified trajectory databases compared with other well-known trajectory simplification algorithms.

Multivariate time series forecasting (MTSF) endeavors to predict future observations given historical data, playing a crucial role in time series data management systems. With advancements in large language models (LLMs), recent studies employ textual prompt tuning to infuse the knowledge of LLMs into MTSF. However, the deployment of LLMs often suffers from low efficiency during the inference phase. To address this problem, we introduce TimeKD, an efficient MTSF framework that leverages the calibrated language models and privileged knowledge distillation. TimeKD aims to generate high-quality future representations from the proposed cross-modality teacher model and cultivate an effective student model. The cross-modality teacher model adopts calibrated language models (CLMs) with ground truth prompts, motivated by the paradigm of Learning Under Privileged Information (LUPI). In addition, we design a subtractive cross attention (SCA) mechanism to refine these representations. To cultivate an effective student model, we propose an innovative privileged knowledge distillation (PKD) mechanism including correlation and feature distillation. PKD enables the student to replicate the teacher's behavior while minimizing their output discrepancy. Extensive experiments on real data offer insight into the effectiveness, efficiency, and scalability of the proposed TimeKD.

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

The rapid development of smart devices has fostered the growth of Spatial Crowdsourcing (SC), where workers complete spatial tasks by traveling to specific locations. Task assignment is a key issue in SC due to the inherent complexity of matching workers with these spatial tasks efficiently. Previous studies on task assignment have primarily focused on optimizing worker-task matching within a single, centralized area, often ignoring scenarios that involve multiple independent service centers across an area. To address this gap, we introduce a collaborative multi-center task assignment problem, which focuses on scenarios where an SC platform manages multiple independent service centers within an area, shifting the focus from worker-level cooperation to exploring the solutions specific to multi-center coordination. We target the imbalances between available workers and unassigned tasks among different centers, aiming to maximize the total number of assigned tasks and minimize unfairness in inter-center collaboration. In particular, we propose an Iterative Multi-center Task Assignment and Optimization (IMTAO) framework. IMTAO operates in two phases: (1) center-independent task assignment based on an efficient sequential task assignment algorithm, and (2) inter-center workforce transfer based on a game-theoretic multi-center collaboration algorithm that ensures fair collaboration through bi-directional optimization. Extensive experiments demonstrate the efficiency and effectiveness of IMTAO in enhancing task assignment and improving collaboration fairness compared to baseline methods.

Counterfactual learning is emerging as an important paradigm, rooted in causality, which promises to alleviate common issues of graph neural networks (GNNs), such as fairness and interpretability. However, as in many real-world application domains where conducting randomized controlled trials is impractical, one has to rely on available observational (factual) data to detect counterfactuals. In this paper, we introduce and tackle the problem of searching for counterfactual evidences for the GNN-based node classification task. A counterfactual evidence is a pair of nodes such that, regardless they exhibit great similarity both in the features and in their neighborhood subgraph structures, they are classified differently by the GNN. We develop effective and efficient search algorithms and a novel indexing solution that leverages both node features and structural information to identify counterfactual evidences, and generalizes beyond any specific GNN. Through various downstream applications, we demonstrate the potential of counterfactual evidences to enhance fairness and accuracy of GNNs.

Large Language Models (LLMs) have emerged as a promising paradigm for time series analytics, leveraging their massive parameters and the shared sequential nature of textual and time series data.However, a cross-modality gap exists between time series and textual data, as LLMs are pre-trained on textual corpora and are not inherently optimized for time series.In this tutorial, we provide an up-to-date overview of LLM-based cross-modal time series analytics.We introduce a taxonomy that classifies existing approaches into three groups based on cross-modal modeling strategies, e.g., conversion, alignment, and fusion, and then discuss their applications across a range of downstream tasks.In addition, we summarize several open challenges.This tutorial aims to expand the practical application of LLMs in solving real-world problems in cross-modal time series analytics while balancing effectiveness and efficiency.Participants will gain a thorough understanding of current advancements, methodologies, and future research directions in cross-modal time series analytics.

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.

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.

Searching for similar trajectories from a database is an important way for extracting human-understandable knowledge. However, due to the huge volume of trajectories and high computation complexity of distance between trajectories, it is difficult to search for exact results, which motivates the research of approximating approaches. In this study, we propose a learning to hash method for trajectory similarity computation and search, called Traj2Hash, which consists of a two-channel trajectory encoder and a hash layer to encode trajectories into Euclidean and Hamming space, respectively. The embeddings of trajectories obtained from the encoder are capable of preserving the reverse symmetric property and more representative due to the reverse augmentation and the lower-bound induced read-out layer. Moreover, we design a decomposed grid representation in the encoder to make the model lighter and better. In the model training phase, we combine a weighted mean squared error loss and a ranking-based hashing loss to enable the model similarity-aware and representations self-structured, respectively, in which a fast trajectory triplet generation method is leveraged to enrich the training corpus. Extensive experiments conducted on real data offer evidence of the effectiveness and efficiency of the proposed model.

The popularity of mobile devices has led to the increased attention of Spatial Crowdsourcing (SC), a framework that assigns location-sensitive tasks to mobile workers. Task recommendation is crucial in helping workers discover attractive tasks. Existing studies have focused on modeling workers' preferences from past task-performing patterns, but their performance is sub-optimal due to the strong coupling of sequentiality, spatiality, and temporality. Moreover, achieving the highest preference-based utility of workers in most of the existing task recommendation studies is inferior to the benefits of the SC platform and the satisfaction of workers in a long range, due to the lower task coverage rate and the poor diversity in a worker's recommended list. To address these problems, we propose a Diversity-Coverage Balanced Task Recommendation (DCBTaskRec) framework. Specifically, we first introduce a decoupled worker preference learning model that adopts self-attention networks as the backbone and decouples the modeling of multiple factors in attention scores. Additionally, we provide an optimal diveristy-aware approach to maximize the recommendation diversity while keeping high preference-based utility of workers to satisfy the multiple tastes of workers. From the side of the SC platform, we also provide two approaches (i.e., greedy coverage-aware approach and diversity-coverage balanced approach) to achieve high coverage and provide a trade-off between diversity and coverage, respectively. Extensive experiments offer insight into the effectiveness of the proposed framework.

In the era of observability, massive amounts of time series data have been collected to monitor the running status of the target system, where anomaly detection serves to identify observations that differ significantly from the remaining ones and is of utmost importance to enable value extraction from such data. While existing reconstruction-based methods have demonstrated favorable detection capabilities in the absence of labeled data, they still encounter issues of training bias on abnormal times and distribution shifts within time series. To address these issues, we propose a simple yet effective Temporal-Frequency Masked AutoEncoder (TFMAE) to detect anomalies in time series through a contrastive criterion. Specifically, TFMAE uses two Transformer-based autoencoders that respectively incorporate a window-based temporal masking strategy and an amplitude-based frequency masking strategy to learn knowledge without abnormal bias and reconstruct anomalies by the extracted normal information. Moreover, the dual autoencoder undergoes training through a contrastive objective function, which minimizes the discrepancy of representations from temporal-frequency masked autoencoders to highlight anomalies, as it helps alleviate the negative impact of distribution shifts. Finally, to prevent over-fitting, TFMAE adopts adversarial training during the training phase. Extensive experiments conducted on seven datasets provide evidence that our model is able to surpass the state-of-the-art in terms of anomaly detection accuracy.

With the proliferation of GPS-equipped edge devices, huge trajectory data is generated and accumulated in various domains, motivating a variety of urban applications. Due to the limited acquisition capabilities of edge devices, a lot of trajectories are recorded at a low sampling rate, which may lead to the effectiveness drop of urban applications. We aim to recover a high-sampled trajectory based on the low-sampled trajectory in free space, i.e., without road network information, to enhance the usability of trajectory data and support urban applications more effectively. Recent proposals targeting trajectory recovery often assume that trajectories are available at a central location, which fail to handle the decentralized trajectories and hurt privacy. To bridge the gap between decentralized training and trajectory recovery, we propose a lightweight framework, LightTR, for federated trajectory recovery based on a client-server architecture, while keeping the data decentralized and private in each client/platform center (e.g., each data center of a company). Specifically, considering the limited processing capabilities of edge devices, LightTR encompasses a light local trajectory embedding module that offers improved computational efficiency without compromising its feature extraction capabilities. LightTR also features a meta-knowledge enhanced local-global training scheme to reduce communication costs between the server and clients and thus further offer efficiency improvement. Extensive experiments demonstrate the effectiveness and efficiency of the proposed framework.

Privacy concerns have led to the rise of federated recommender systems (FRS), which can create personalized models across distributed clients. However, FRS is vulnerable to poisoning attacks, where malicious users manipulate gradients to promote their target items intentionally. Existing attacks against FRS have limitations, as they depend on specific models and prior knowledge, restricting their real-world applicability. In our exploration of practical FRS vulnerabilities, we devise a model-agnostic and prior-knowledge-free attack, named PIECK (Popular Item Embedding based Attack). The core module of PIECK is popular item mining, which leverages embedding changes during FRS training to effectively identify the popular items. Built upon the core module, PIECK branches into two diverse solutions: The PIECKIPE solution employs an item popularity enhancement module, which aligns the embeddings of targeted items with the mined popular items to increase item exposure. The PIECKUEA further enhances the robustness of the attack by using a user embedding approximation module, which approximates private user embeddings using mined popular items. Upon identifying PIECK, we evaluate existing federated defense methods and find them ineffective against PIECK, as poisonous gradients inevitably overwhelm the cold target items. We then propose a novel defense method by introducing two regularization terms during user training, which constrain item popularity enhancement and user embedding approximation while preserving FRS performance. We evaluate PIECK and its defense across two base models, three real datasets, four top-tier attacks, and six general defense methods, affirming the efficacy of both PIECK and its defense.

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

High-dimensional approximate K nearest neighbor search (AKNN) is a fundamental task for various applications, including information retrieval. Most existing algorithms for AKNN can be decomposed into two main components, i.e., candidate generation and distance comparison operations (DCOs). While different methods have unique ways of generating candidates, they all share the same DCO process. In this study, we focus on accelerating the process of DCOs that dominates the time cost in most existing AKNN algorithms. To achieve this, we propose an Data-Aware Distance Estimation approach, called DADE , which approximates the exact distance in a lower-dimensional space. We theoretically prove that the distance estimation in DADE is unbiased in terms of data distribution. Furthermore, we propose an optimized estimation based on the unbiased distance estimation formulation. In addition, we propose a hypothesis testing approach to adaptively determine the number of dimensions needed to estimate the exact distance with sufficient confidence. We integrate DADE into widely-used AKNN search algorithms, e.g., IVF and HNSW , and conduct extensive experiments to demonstrate the superiority.

Portfolio management is an important yet challenging task in AI for FinTech, which aims to allocate investors' budgets among different assets to balance the risk and return of an investment. In this study, we propose a general M ulti-object I ve framework with contro LL able r I sk for p O rtfolio ma N agement ( MILLION ), which consists of two main phases, i.e., return-related maximization and risk control. Specifically, in the return-related maximization phase, we introduce two auxiliary objectives, i.e., return rate prediction, and return rate ranking, combined with portfolio optimization to remit the overfitting problem and improve the generalization of the trained model to future markets. Subsequently, in the risk control phase, we propose two methods, i.e., portfolio interpolation and portfolio improvement, to achieve fine-grained risk control and fast risk adaption to a user-specified risk level. For the portfolio interpolation method, we theoretically prove that the risk can be perfectly controlled if the to-be-set risk level is in a proper interval. In addition, we also show that the return rate of the adjusted portfolio after portfolio interpolation is no less than that of the min-variance optimization, as long as the model in the reward maximization phase is effective. Furthermore, the portfolio improvement method can achieve greater return rates while keeping the same risk level compared to portfolio interpolation. Extensive experiments are conducted on three real-world datasets. The results demonstrate the effectiveness and efficiency of the proposed framework.

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.

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 an exponentially growing number of graphs from disparate repositories, there is a strong need to analyze a graph database containing an extensive collection of small- or medium-sized data graphs (eg chemical compounds). Although subgraph enumeration and subgraph mining have been proposed to bring insights into a graph database by a set of subgraph structures, they often end up with similar or homogenous topologies, which is undesirable in many graph applications. To address this limitation, we propose theTop-k Edge-Diversified Patterns Discovery problemto retrieve a set of subgraphs that cover the maximum number of edges in a database. To efficiently process such query, we present a generic and extensible framework called$\textsc {Ted}^+$which achieves a guaranteed approximation ratio to the optimal result. Three optimization strategies are further developed to improve the performance, and a lightweight version calledTedLiteis designed for even larger graph databases. Experimental studies on real-world datasets demonstrate the superiority of$\textsc {Ted}^+$to traditional techniques.

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

Automation in road vehicles is an emerging technology that has developed rapidly over the last decade. There have been many inter-disciplinary challenges posed on existing transportation infrastructure by autonomous vehicles. In this paper, we conduct an algorithmic study on when and how an autonomous vehicle should change its lane, which is a fundamental problem in vehicle automation field and root cause of most ‘phantom’ traffic jams. We propose a prediction-and-decision framework, called Cheetah (Changelanesmartforautonomous vehicle), which aims to optimize the lane changing maneuvers of autonomous vehicle while minimizing its impact on surrounding vehicles. In the prediction phase, Cheetah learns the spatio-temporal dynamics from historical trajectories of surrounding vehicles with a deep model (GAS-LED model) and predict their corresponding actions in the near future. A global attention mechanism and state sharing strategy are also incorporated to achieve higher accuracy and better convergence efficiency. Then in the decision phase, Cheetah looks for optimal lane change maneuvers for the autonomous vehicle by taking into account a few factors such as speed, impact on other vehicles and safety issues. A tree-based adaptive beam search algorithm is designed to reduce the search space and improve accuracy. In order to make our framework applicable to more scenarios, we further propose an improved Cheetah (Cheetah+) framework that makes the autonomous vehicle adapt for exiting a road and meet the requirement for driving comfort. Extensive experiments offer evidence that the proposed framework can advance the state of the art in terms of effectiveness and efficiency.

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

Uncertainty is an essential consideration for time series forecasting tasks. In this work, we focus on quantifying the uncertainty of traffic forecasting from a unified perspective. We develop a novel traffic forecasting framework, namely Deep Spatio-Temporal Uncertainty Quantification (DeepSTUQ), which can estimate both aleatoric and epistemic uncertainty. Specifically, we first leverage a spatio-temporal model to model the complex spatio-temporal correlations of traffic data. Subsequently, two independent sub-neural networks maximizing the heterogeneous log-likelihood are developed to estimate aleatoric uncertainty. To estimate epistemic uncertainty, we combine the merits of variational inference and deep ensembling by integrating the Monte Carlo dropout and the Adaptive Weight Averaging re-training methods, respectively. Furthermore, to relax the Gaussianity assumption, mitigate overfitting, and improve horizon-wise uncertainty quantification performance, we define a new calibration method called Multi-horizon Conformal Calibration (MHCC). Finally, we provide a theoretical analysis of the proposed unified approach based on the PAC-Bayes theory. Extensive experiments are conducted on four public datasets, and the empirical results suggest that the proposed method outperforms state-of-the-art methods in terms of both point prediction and uncertainty quantification.

Recent years have witnessed a revolution in Spatial Crowdsourcing (SC), in which people with mobile connectivity can perform spatio-temporal tasks that involve traveling to specified locations. In this paper, we identify and study in depth a new multi-center-based task allocation problem in the context of SC, where multiple allocation centers exist. In particular, we aim to maximize the total number of the allocated tasks while minimizing the allocated task number difference. To solve the problem, we propose a two-phase framework, called Task Allocation with Geographic Partition, consisting of a geographic partition and a task allocation phase. The first phase divides the whole study area based on the allocation centers by using both a basic Voronoi diagram-based algorithm and an adaptive weighted Voronoi diagram-based algorithm. In the allocation phase, we utilize a Reinforcement Learning method to achieve the task allocation, where a graph neural network with the attention mechanism is used to learn the embeddings of allocation centers, delivery points, and workers. To further improve the efficiency, we propose an early stopping optimization strategy for the adaptive weighted Voronoi diagram-based algorithm in the geographic partition phase and give a distance-constrained graph pruning strategy for the Reinforcement Learning method in the task allocation phase. Extensive experiments give insight into the effectiveness and efficiency of the proposed solutions.

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.

The pervasiveness of GPS-enabled devices and wireless communication technologies results in a proliferation of traffic data in intelligent transportation systems, where traffic prediction is often essential to enable reliability and safety. Many recent studies target traffic prediction using deep learning techniques. They model spatio-temporal dependencies among traffic states by deep learning and achieve good overall performance. However, existing studies ignore the bias on traffic prediction models, which refers to non-uniformed performance distribution across road segments, especially the significantly poor prediction results on certain road segments. To solve this issue, we propose a framework named spatio-temporal mixture-of-experts (ST-MoE) that aims to eliminate the bias on traffic prediction. In general, we refer to any traffic prediction model as the based model, and adopt the proposed ST-MoE framework as a plug-in to debias. ST-MoE uses stacked convolution-based networks to learn spatio-temporal representations of individual patterns of road segments and then adaptively assigns appropriate expert layers (sub-networks) to different patterns through a spatio-temporal gating network. To this end, the patterns can be distinguished, and biased performance among road segments can be eliminated by experts tailored for specific patterns, which also further improves the overall prediction accuracy of the base model. Extensive experimental results on various base models and real-world datasets prove the effectiveness of ST-MoE.

With the proliferation of wireless and mobile devices, Spatial Crowdsourcing (SC) attracts increasing attention, where task assignment plays a critically important role. However, recent task assignment solutions in SC often assume that data is stored in a central station while ignoring the issue of privacy leakage. To enable decentralized training and privacy protection, we propose a federated task assignment framework with personalized location-preference learning, which performs efficient task assignment while keeping the data decentralized and private in each platform center (e.g., a delivery center of an SC company). The framework consists of two phases: personalized federated location-preference learning and task assignment. Specifically, in the first phase, we design a personalized location-preference learning model for each platform center by simultaneously considering the location information and data heterogeneity across platform centers. Based on workers' location preference, the task assignment phase aims to achieve effective and efficient task assignment by means of the Kuhn-Munkres (KM) algorithm and the newly proposed conditional degree-reduction algorithm. Extensive experiments on real-world data show the effectiveness of the proposed framework.

The widespread use of positioning devices has given rise to many trajectories, with each having three explicit attributes: user ID, location ID, and time-stamp and an implicit attribute: activity type (akin to "topic" in text mining). To model these trajectories, existing works learn different attribute representations by either introducing latent activity types based on topic models or transforming the location and time context into a low-dimensional space via embedding techniques. In this paper, we propose a holistic approach named Human Mobility Representation Model (HMRM) to simultaneously produce the vector representations of all four (explicit and implicit) attributes. We evaluate HMRM on two real check-in datasets collected from Foursquare. Experimental results show that HMRM could not only improve the performance of capturing latent activity types, but also learn better trajectory embeddings.

Uncertainty is an essential consideration for time series forecasting tasks. In this work, we specifically focus on quantifying the uncertainty of traffic forecasting. To achieve this, we develop Deep Spatio-Temporal Uncertainty Quantification (DeepSTUQ), which can estimate both aleatoric and epistemic uncertainty. We first leverage a spatio-temporal model to model the complex spatio-temporal correlations of traffic data. Subsequently, two independent sub-neural networks maximizing the heterogeneous log-likelihood are developed to estimate aleatoric uncertainty. For estimating epistemic uncertainty, we combine the merits of variational inference and deep ensembling by integrating the Monte Carlo dropout and the Adaptive Weight Averaging re-training methods, respectively. Finally, we propose a post-processing calibration approach based on Temperature Scaling, which improves the model’s generalization ability to estimate uncertainty. Extensive experiments are conducted on four public datasets, and the empirical results suggest that the proposed method outperforms state-of-the-art methods in terms of both point prediction and uncertainty quantification.

Query optimization is of great importance for the performance of a database, in which cardinality and cost estimation have a pivotal role. To enable accurate cardinality and cost estimation, we propose a novel framework based on bidirectional compressor and ensemble networks called BICE. In particular, we design a feature extractor composed of four sub-encoders, which can extract various types of information in a query plan tree and hybrid learning strategies for encoding. We encode joins based on a graph embedding method and design parallel networks for filters to improve the encoding efficiency. Then we propose a bidirectional LSTM-based compressor to learn the encoding and obtain fixed-length vectors, reducing the learning difficulty of the estimation model. Finally, we propose different data sampling strategies based on Bayesian neural networks and active learning, and an ensemble model is established based on transfer learning, which enables accurate estimation and adaptation to large-scale data queries. Extensive experiments offer insight into the effectiveness and efficiency of the proposed framework.

In many complex systems, devices are typically monitored and generating massive multivariate time series. However, due to the complex patterns and little useful labeled data, it is a great challenge to detect anomalies from these time series data. Existing methods either rely on less regularizations, or require a large number of labeled data, leading to poor accuracy in anomaly detection. To overcome the limitations, in this paper, we propose an adversarial autoencoder anomaly detection and interpretation framework named DAEMON, which performs robustly for various datasets. The key idea is to use two discriminators to adversarially train an autoencoder to learn the normal pattern of multivariate time series, and thereafter use the reconstruction error to detect anomalies. The robustness of DAEMON is guaranteed by the regularization of hidden variables and reconstructed data using the adversarial generation method. An unsupervised approach used to detect anomalies is proposed. Moreover, in order to help operators better diagnose anomalies, DAEMON provides anomaly interpretation by computing the gradients of anomalous data. An extensive empirical study on real data offers evidence that the framework is capable of outperforming state-of-the-art methods in terms of the overall F1-score and interpretation accuracy for time series anomaly detection.

Trajectory-User Linking (TUL) aiming to identify users of anonymous trajectories, has recently received increasing attention due to its wide range of applications, such as criminal investigation and personalized recommendation systems. In this paper, we propose a flexible Semi-Supervised framework for Trajectory-User Linking, namely S2TUL, which includes five components: trajectory-level graph construction, trajectory relation modeling, location-level sequential modeling, a classification layer and greedy trajectory-user relinking. The first two components are proposed to model the relationships among trajectories, in which three homogeneous graphs and two heterogeneous graphs are firstly constructed and then delivered into the graph convolutional networks for converting the discrete identities to hidden representations. Since the graph constructions are irrelevant to the corresponding users, the unlabelled trajectories can also be included in the graphs, which enables the framework to be trained in a semi-supervised way. Afterwards, the location-level sequential modeling component is designed to capture fine-grained intra-trajectory information by passing the trajectories into the sequential neural networks. Finally, these two level representations are concatenated into a classification layer to predict the user of the input trajectory. In the testing phase, a greedy trajectory-user relinking method is proposed to assure the linking results satisfy the timespan overlap constraint. We conduct extensive experiments on three public datasets with six representative competitors. The evaluation results demonstrate the effectiveness of the proposed framework.

Abstract Multi-key membership testing plays a crucial role in computing systems and networking applications, encompassing web search, mail systems, distributed databases, firewalls, and network routing. Traditional approaches, such as the Bloom filter, encounter limitations within this specific context. Addressing these challenges, we propose the Multi-key Learned Bloom Filter (MLBF), a hybrid method that combines machine learning techniques with the Bloom filter. The MLBF introduces a value-interaction-based multi-key classifier and a multi-key Bloom filter. Furthermore, we introduce an Interval-based MLBF approach, which categorizes keys into specific intervals based on data distribution to minimize the False Positive Rate (FPR). Additionally, MLBF incorporates an out-of-distribution (OOD) detection component to identify data shifts. Through extensive experimental evaluations on three authentic datasets, we demonstrate the superiority of the proposed MLBF in terms of FPR and query efficiency.

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

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

The pervasiveness of GPS-enabled devices and wireless communication technologies flourish the market of Spatial Crowdsourcing (SC), which consists of location-based tasks and requires workers to be at specific locations physically to complete them. In this work, we study the problem of worker-churn-based task assignment in SC, where tasks are assigned by considering workers’ churn. In particular, we aim to maximize the total rewards of task assignments based on the worker churn prediction. To solve the problem, we propose a two-phase framework, which consists of a worker churn prediction and a task assignment phase. In the first phase, we use an LSTM-based model to extract latent feelings of workers based on historical data and then estimate idle time intervals of workers. In the assignment phase, we design an efficient greedy algorithm and a Kuhn-Munkras-based algorithm that can achieve the optimal task assignment. To improve the accuracy of the idle time interval estimation for workers, we adopt a context-dependent LSTM model, which involves interactions between inputs and their context. We further optimize the original task assignment framework by proposing a travel distance optimization strategy to reduce the overall travel distance. Extensive experiments offer insight into the effectiveness and efficiency of the proposed solutions.

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.

Spatial crowdsourcing is one of the prime movers for the orchestration of location-based tasks, and task recommendation is a crucial means to help workers discover attractive tasks. While a number of existing studies have focused on modeling workers’ geographical preferences in task recommendation, they ignore the phenomenon of workers’ travel intention drifts across geographical areas, i.e., workers tend to have different intentions when they travel in different areas, which discounts the task recommendation quality of existing methods especially for workers that travel in unfamiliar out-of-town areas. To address this problem, we propose an Adaptive Task Recommendation ( AdaTaskRec ) framework. Specifically, we first give a novel two-module worker preference learning architecture that can calculate workers’ preferences for POIs (that tasks are associated with) in different areas adaptively based on workers’ current locations. If we detect that a worker is in the hometown area, then we apply the hometown preference learning module, which hybrids different strategies to aggregate workers’ travel intentions into their preferences while considering the transition and the sequence patterns among locations. Otherwise, we invoke the out-of-town preference learning module, which is to capture workers’ preferences by learning their travel intentions and transferring their hometown preferences into their out-of-town ones. Additionally, to improve task recommendation effectiveness, we propose a dynamic top- k recommendation method that sets different k values dynamically according to the numbers of neighboring workers and tasks. We also give an extra-reward-based and a fair top- k recommendation method, which introduce the extra rewards for tasks based on their recommendation rounds and consider exposure-based fairness of tasks, respectively. Extensive experiments offer insight into the effectiveness of the proposed framework.

Query cost estimation, which is to estimate the query plan cost and query execution cost, is of utmost importance to query optimizers. Query plan cost estimation heavily relies on accurate cardinality estimation, and query execution cost estimation gives good hints on query latency, both of which are challenging in database management systems. Despite decades of research, existing studies either over-simplify the models only using histograms and polynomial calculation that leads to inaccurate estimates, or over-complicate them by using cumbersome neural networks with the requirements for large amounts of training data hence poor computational efficiency. Besides, most of the studies ignore the diversity of query plan structures. In this work, we propose a plan-based query cost estimation framework, called Saturn, which can eStimate cardinality and latency accurately and efficiently, for any query plan structures. Saturn first encodes each query plan tree into a compressed vector by using a traversal-based query plan autoencoder to cope with diverse plan structures. The compressed vectors can be leveraged to distinguish different query types, which is highly useful for downstream tasks. Then a pseudo label generator is designed to acquire all cardinality and latency labels with the execution part of the query plans in the training workload, which can significantly reduce the overhead of collecting the real cardinality and latency labels. Finally, a chain-wise transfer learning module is proposed to estimate the cardinality and latency of the query plan in a pipeline paradigm, which further enhances the efficiency. An extensive empirical study on benchmark data offers evidence that Saturn outperforms the state-of-the-art proposals in terms of accuracy, efficiency, and generalizability for query cost estimation.

The increasing proliferation of networked and geo-positioned mobile devices brings about increased opportunities for Spatial Crowdsourcing (SC), which aims to enable effective location-based task assignment. We propose and study a novel SC framework, namely Task Assignment with Task Publication Time Recommendation. The framework consists of two phases, task publication time recommendation and task assignment. More specifically, the task publication time recommendation phase hybrids different learning models to recommend the suitable publication time for each task to ensure the timely task assignment and completion while reducing the waiting time of the task requester at the SC platform. We use a cross-graph neural network to learn the representations of task requesters by integrating the obtained representations from two semantic spaces and utilize the self-attention mechanism to learn the representations of task-publishing sequences from multiple perspectives. Then a fully connected layer is used to predict suitable task publication time based on the obtained representations. In the task assignment phase, we propose a greedy and a minimum cost maximum flow algorithm to achieve the efficient and the optimal task assignment, respectively. An extensive empirical study demonstrates the effectiveness and efficiency of our framework.

The ubiquity of mobile devices and the accompanying deployment of sensing technologies have resulted in a massive amount of trajectory data. One important fundamental task is trajectory similarity computation, which is to determine how similar two trajectories are. To enable effective and efficient trajectory similarity computation, we propose a novel robust model, namely Contrastive Learning based Trajectory Similarity Computation (CL-TSim). Specifically, we employ a contrastive learning mechanism to learn the latent representations of trajectories and then calculate the dissimilarity between trajectories based on these representations. Compared with sequential auto-encoders that are the mainstream deep learning architectures for trajectory similarity computation, CL-TSim does not require a decoder and step-by-step reconstruction, thus improving the training efficiency significantly. Moreover, considering the non-uniform sampling rate and noisy points in trajectories, we adopt two type of augmentations, i.e., point dowm-sampling and point distorting, to enhance the robustness of the proposed model. Extensive experiments are conducted on two widely-used real-world datasets, i.e., Porto and ChengDu, which demonstrate the superior effectiveness and efficiency of the proposed model.

With the fast-paced development of mobile networks and the widespread usage of mobile devices, Spatial Crowdsourcing (SC) has drawn increasing attention in recent years. SC has the potential for collecting information for a broad range of applications such as on-demand local delivery and on-demand transportation. One of the critical issues in SC is task assignment that allocates location-based tasks (e.g., delivering food and packages) to appropriate moving workers (i.e., intelligent device carriers). In this paper, we study a loyalty-based task assignment problem, which aims to maximize the overall rewards of workers while considering worker loyalty. We propose a two-phase framework to solve the problem, including a worker loyalty prediction and a task assignment phase. In the first phase, we use a model based on an efficient time series prediction method called Prophet and an Entropy Weighting method to extract workers' short-term and long-term loyalty and then predict workers' current loyalty scores. In the task assignment phase, we design a Kuhn-Munkras-based algorithm that achieves the optimal task assignment and an efficient Degree-Reduction-based algorithm with minority first scheme. Extensive experiments offer insight into the effectiveness and efficiency of the proposed solutions.

Spatial Crowdsourcing (SC) is ubiquitous in the online world today. As we have transitioned from crowdsourcing applications (e.g., Wikipedia) to SC applications (e.g., Uber), there is a substantial precedent that SC systems have a responsibility not only to effective task assignment but also to privacy protection. To address these often-conflicting responsibilities, we propose a framework, Task Assignment with Federated Preference Learning, which performs task assignment based on worker preferences while keeping the data decentralized and private in each platform center (e.g., each delivery center of an SC company). The framework includes two phases, i.e., a federated preference learning and a task assignment phase. Specifically, in the first phase, we design a local preference model for each platform center based on historical data. Meanwhile, the horizontal federated learning with a client-server structure is introduced to collaboratively train these local preference models under the orchestration of a central server. The task assignment phase aims to achieve effective and efficient task assignment by considering workers' preferences. Extensive evaluations over real data show the effectiveness and efficiency of the paper's proposals.

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

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

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

Join order selection plays an important role in DBMS query optimizers. The problem aims to find the optimal join order with the minimum cost, and usually becomes an NP-hard problem due to the exponentially increasing search space. Recent advanced studies attempt to use deep reinforcement learning (DRL) to generate better join plans than the ones provided by conventional query optimizers. However, DRL-based methods require time-consuming training, which is not suitable for online applications that need frequent periodic re-training. In this paper, we propose a novel framework, namely efficient Join Order selection learninG with Graph-basEd Representation (JOGGER). We firstly construct a schema graph based on the primary-foreign key relationships, from which table representations are well learned to capture the correlations between tables. The second component is the state representation, where a graph convolutional network is utilized to encode the query graph and a tailored-tree-based attention module is designed to encode the join plan. To speed up the convergence of DRL training process, we exploit the idea of curriculum learning, in which queries are incrementally added into the training set according to the level of difficulties. We conduct extensive experiments on JOB and TPC-H datasets, which demonstrate the effectiveness and efficiency of the proposed solutions.

With the rapid development of mobile networks and the widespread usage of mobile devices, Location-Based Adver-tising (LBA), which allows an advertiser to promote products or services to targeted customers in a suitable location, has drawn increasing attention. Recommending an optimal location by delivering appealing advertisements to potential customers is crucial for the advertiser. Existing recommendation models (such as collaborative filtering) are insufficient for solving the data sparsity and cold-start issue (e.g., no historical advertisement records in new domains) in LBA problems. To tackle the defi-ciency mentioned above, we propose a novel location-based ad-vertising recommendation framework: CityCross. The CityCross framework consists of a data extraction module and a learning module. The data extraction module conducts commercial and POI feature extractions from the LBA platform, and Gaode Map, respectively. The learning module is dedicated to learning the relevant knowledge of advertisement in a new domain by utilizing the attention-based semantic information, cross-city knowledge association, and the local neighbors' knowledge. The top-k locations are identified by a modified linear regression model based on the learned knowledge. Finally, we conduct extensive experiments on two real datasets to verify the superiority of the proposed approach.

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.

Although many applications take subtrajectories as basic units for analysis, there is little research on the similar subtrajectory search problem aiming to return a portion of a trajectory (i.e., subtrajectory), which is the most similar to a query trajectory. We find that in some special cases, when a grid-based metric is used, this problem can be formulated as a reading comprehension problem, which has been studied extensively in the field of natural language processing (NLP). By this formulation, we can obtain faster models with better performance than existing methods. However, due to the difference between natural language and trajectory (e.g., spatial relationship), it is impossible to directly apply NLP models to this problem. Therefore, we propose a Similar Subtrajectory Search with a Graph Neural Networks framework. This framework contains four modules including a spatial-aware grid embedding module, a trajectory embedding module, a query-context trajectory fusion module, and a span prediction module. Specifically, in the spatial-aware grid embedding module, the spatial-based grid adjacency is constructed and delivered to the graph neural network to learn spatial-aware grid embedding. The trajectory embedding module aims to model the sequential information of trajectories. The purpose of the query-context trajectory fusion module is to fuse the information of the query trajectory to each grid of the context trajectories. Finally, the span prediction module aims to predict the start and the end of a subtrajectory for the context trajectory, which is the most similar to the query trajectory. We conduct comprehensive experiments on two real world datasets, where the proposed framework outperforms the state-of-the-art baselines consistently and significantly.

With the explosion of GPS-enabled smartphones and social media platforms, geo-social networks are increasing as tools for businesses to promote their products or services. Influence maximization, which aims to maximize the expected spread of influence in the networks, has drawn increasing attention. However, most recent work tries to study influence maximization by only considering geographic distance, while ignoring the influence of users’ spatio-temporal behavior on information propagation or location promotion, which can often lead to poor results. To relieve this problem, we propose a Similarity-aware Influence Maximization (SIM) model to efficiently maximize the influence spread by taking the effect of users’ spatio-temporal behavior into account, which is more reasonable to describe the real information propagation. We first calculate the similarity between users according to their historical check-ins, and then we propose a Propagation to Consumption (PTC) model to capture both online and offline behaviors of users. Finally, we propose two greedy algorithms to efficiently maximize the influence spread. The extensive experiments over real datasets demonstrate the efficiency and effectiveness of the proposed algorithms.

The widespread use of positioning devices has given rise to many trajectories, with each having three explicit attributes:user ID,location ID, andtime-stampand an implicit attribute:activity type(akin to “topic” in text mining). To model these trajectories, existing works learn different attribute representations by either introducing latent activity types based on topic models or transforming the location and time context into a low-dimensional space via embedding techniques. In this paper, we propose a holistic approach named Human Mobility Representation Model (HMRM) to simultaneously produce the vector representations of all four (explicit and implicit) attributes. The merits of HMRM lie in that: (1) it models the latent activity types and learns trajectory attribute embeddings in an integrated manner, and (2) it connects the activity-related distributions and these attributes embeddings by adding a newly designed collaborative learning component, and makes them mutually exchanged to take the best of both worlds. We apply HMRM to both unsupervised and supervised tasks including two activity evaluation tasks and two embedding evaluation tasks, on two real check-in datasets collected from Foursquare. Experimental results show that HMRM could not only improve the performance of capturing latent activity types, but also learn better trajectory embeddings.

With the ubiquity of smart devices, Spatial Crowdsourcing (SC) has emerged as a new transformative platform that engages mobile users to perform spatio-temporal tasks by physically traveling to specified locations. Thus, various SC techniques have been studied for performance optimization, among which one of the major challenges is how to assign workers the tasks that they are really interested in and willing to perform. In this paper, we propose a novel preference-aware spatial task assignment system based on workers’ temporal preferences, which consists of two components:History-based Context-aware Tensor Decomposition (HCTD) for workers’ temporal preferences modelingandpreference-aware task assignment. We model workers’ preferences with a three-dimension tensor (worker-task-time). Supplementing the missing entries of the tensor through HCTD with the assistant of historical data and other two context matrices, we recover workers’ preferences for different categories of tasks in different time slots. Several preference-aware individual task assignment algorithms are then devised, aiming to maximize the total number of task assignments at every time instance, in which we give higher priorities to the workers who are more interested in the tasks. In order to make our proposed framework applicable to more scenarios, we further optimize the original framework by proposing strategies to allow each task to be assigned to a group of workers such that the task can be completed by these workers simultaneously, wherein workers’ tolerable waiting time, consensus, and tasks’ rewards are taken into consideration. We conduct extensive experiments using a real dataset, verifying the practicability of our proposed methods.

Accurately recommending the next point of interest (POI) has become a fundamental problem with the rapid growth of location-based social networks. However, sparse, imbalanced check-in data and diverse user check-in patterns pose severe challenges for POI recommendation tasks. Knowledge-aware models are known to be primary in leveraging these problems. However, as most knowledge graphs are constructed statically, sequential information is yet integrated. In this work, we propose a meta-learned sequential-knowledge-aware recommender (Meta-SKR), which utilizes sequential, spatio-temporal, and social knowledge to recommend the next POI for a location-based social network user. The framework mainly contains four modules. First, in the graph construction module, a novel type of knowledge graph—the sequential knowledge graph, which is sensitive to the check-in order of POIs—is built to model users’ check-in patterns. To deal with the problem of data sparsity, a meta-learning module based on latent embedding optimization is then introduced to generate user-conditioned parameters of the subsequent sequential-knowledge-aware embedding module, where representation vectors of entities (nodes) and relations (edges) are learned. In this embedding module, gated recurrent units are adapted to distill intra- and inter-sequential knowledge graph information. We also design a novel knowledge-aware attention mechanism to capture information surrounding a given node. Finally, POI recommendation is provided by inferring potential links of knowledge graphs in the prediction module. Evaluations on three real-world check-in datasets show that Meta-SKR can achieve high recommendation accuracy even with sparse data.

The pervasiveness of GPS-enabled devices and wireless communication technologies flourish the market of Spatial Crowdsourcing (SC), which consists of location-based tasks and requires workers to physically be at specific locations to complete them. In this work, we study the problem of Worker Churn based Task Assignment in SC, where tasks are to be assigned by considering workers' churn. In particular, we aim to achieve the highest total rewards of task assignments based on the worker churn prediction. To solve the problem, we propose a two-phase framework, which consists of a worker churn prediction phase and a task assignment phase. In the first phase, we use an LSTM-based model to extract the latent feelings of workers based on the historical data and then estimate the idle time intervals of workers. In the assignment phase, we design an efficient greedy algorithm and a Kuhn-Munkras (KM)-based algorithm that can achieve the optimal task assignment. Extensive experiments offer insight into the effectiveness and efficiency of the proposed solutions.

Recent years have witnessed a revolution in Spatial Crowdsourcing (SC), in which people with mobile connectivity can perform spatio-temporal tasks that involve travel to specified locations. In this paper, we identify and study in depth a new multi-center-based task allocation problem in the context of SC, where multiple allocation centers exist. In particular, we aim to maximize the total number of the allocated tasks while minimizing the average allocated task number difference. To solve the problem, we propose a two-phase framework, called Task Allocation with Geographic Partition, consisting of a geographic partition phase and a task allocation phase. The first phase is to divide the whole study area based on the allocation centers by using both a basic Voronoi diagram-based algorithm and an adaptive weighted Voronoi diagram-based algorithm. In the allocation phase, we utilize a Reinforcement Learning method to achieve the task allocation, where a graph neural network with the attention mechanism is used to learn the embeddings of allocation centers, delivery points and workers. Extensive experiments give insight into the effectiveness and efficiency of the proposed solutions.

In many complex systems, devices are typically monitored and generating massive multivariate time series. However, due to the complex patterns and little useful labeled data, it is a great challenge to detect anomalies from these time series data. Existing methods either rely on less regularizations, or require a large number of labeled data, leading to poor accuracy in anomaly detection. To overcome those limitations, in this paper, we propose an unsupervised anomaly detection framework, called DAEMON (Adversarial Autoencoder Anomaly Detection Interpretation), which performs robustly for various datasets. The key idea is to use two discriminators to adversarially train an autoencoder to learn the normal pattern of multivariate time series, and thereafter use the reconstruction error to detect anomalies. The robustness of DAEMON is guaranteed by the regularization of hidden variables and reconstructed data using the adversarial generation method. Moreover, in order to help operators better diagnose anomalies, DAEMON provides anomaly interpretation based on the reconstruction error of the constituent univariate time series. Experiment results on four real datasets show that DAEMON can achieve an overall F1-score of 0.94, outperforming state-of-the-art methods. In addition, the anomaly interpretation accuracy of DAEMON can achieve 97%.

With the fast-paced development of mobile networks and the widespread usage of mobile devices, Spatial Crowdsourcing (SC), which refers to assigning location-based tasks to moving workers, has drawn increasing attention in recent years. One of the critical issues in SC is task assignment that allocates tasks to appropriate workers. In this paper, we propose a novel SC problem, namely Coalition-based Task Assignment (CTA), where the spatial tasks (e.g., house removals, furniture installation) may require more than one workers (forming a coalition) to cooperate in order to maximize the overall rewards of workers. To tackle the CTA problem, we design both greedy method and equilibrium-based method. In particular, the greedy method aims to form a set of worker coalitions greedily to perform the tasks, in which we introduce an acceptance possibility to find the high-value task assignments. In the equilibrium-based algorithm, workers form coalitions in sequence and update their strategy (i.e., selecting a best-response task) at their turn, in order to maximize their own utility (i.e., reward of the coalition they stay in) until Nash equilibrium is reached. Since the equilibrium point obtained by the best-response approach is not unique and optimal in terms of total rewards, we further propose a simulated annealing scheme to find a better Nash equilibrium. The extensive experiments demonstrate the efficiency and effectiveness of the proposed methods on both real and synthetic datasets.

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 popularity of GPS-enable smart devices and the development of wireless network, Spatial Crowdsourcing (SC), as a framework for assigning location-sensitive tasks to moving workers, has received wide attention in recent years. In real-world scenarios, some complex tasks exist that may not be completed by a single worker. In this case, the tasks are often assigned to multiple workers, which is called group task assignment. However, the assignment of tasks that satisfy all group members in an even way remains a challenge. To this end, we propose a novel preference-aware group task assignment framework that includes two components: Mutual Information-based Preference Modeling (MIPM) and Preference-aware Group Task Assignment (PGTA). Specifically, MIPM learns the preferences of worker groups by maximizing the mutual information among workers based on the worker-task interaction data and the group-task interaction data, where an attention mechanism is used. PGTA adopts an optimal task assignment algorithm based on tree decomposition to assign tasks to appropriate worker groups, which aims to maximize the overall number of assigned tasks while giving priority to the groups of workers that are more interested in the tasks. Finally, extensive experiments are conducted, verifying the effectiveness and practicality of the proposed solutions.

Automation in road vehicles is an emerging technology that has developed rapidly over the last decade. There have been many inter-disciplinary challenges posed on existing transportation infrastructure by autonomous vehicles (AV). In this paper, we conduct an algorithmic study on when and how an autonomous vehicle should change its lane, which is a fundamental problem in vehicle automation field and root cause of most 'phantom' traffic jams. We propose a prediction-and-search framework, called Cheetah (Change lane smart for autonomous vehicle), which aims to optimize the lane changing maneuvers of autonomous vehicle while minimizing its impact on surrounding vehicles. In the prediction phase, Cheetah learns the spatio-temporal dynamics from historical trajectories of surrounding vehicles with a deep model (GAS-LED) and predict their corresponding actions in the near future. A global attention mechanism and state sharing strategy are also incorporated to achieve higher accuracy and better convergence efficiency. Then in the search phase, Cheetah looks for optimal lane change maneuvers for the autonomous vehicle by taking into account a few factors such as speed, impact on other vehicles and safety issues. A tree-based adaptive beam search algorithm is designed to reduce the search space and improve accuracy. Extensive experiments on real and synthetic data evidence that the proposed framework excels state-of-the-art competitors with respect to both effectiveness and efficiency.

The join operations, including both equi and non-equi joins, are essential to the complex data analytics in the big data era. However, they are not inherently supported by existing DSPEs (Distributed Stream Processing Engines). The state-of-the-art join solutions on DSPEs rely on either complicated routing strategies or resource-inefficient processing structures, which are susceptible to dynamic workload, especially when the DSPEs face various join predicate operations and skewed data distribution. In this paper, we propose a new cost-effective stream join framework, named A-DSP (Adaptive Dimensional Space Processing), which enhances the adaptability of real-time join model and minimizes the resource used over the dynamic workloads. Our proposal includes: 1) a join model generation algorithm devised to adaptively switch between different join schemes so as to minimize the number of processing task required; 2) a load-balancing mechanism which maximizes the processing throughput; and 3) a lightweight algorithm designed for cutting down unnecessary migration cost. Extensive experiments are conducted to compare our proposal against state-of-the-art solutions on both benchmark and real-world workloads. The experimental results verify the effectiveness of our method, especially on reducing the operational cost under pay-as-you-go pricing scheme.

Recommender systems have shown great potential to solve the information explosion problem and enhance user experience in various online applications. To tackle data sparsity and cold start problems in recommender systems, researchers propose knowledge graphs (KGs) based recommendations by leveraging valuable external knowledge as auxiliary information. However, most of these works ignore the variety of data types (e.g., texts and images) in multi-modal knowledge graphs (MMKGs). In this paper, we propose Multi-modal Knowledge Graph Attention Network (MKGAT) to better enhance recommender systems by leveraging multi-modal knowledge. Specifically, we propose a multi-modal graph attention technique to conduct information propagation over MMKGs, and then use the resulting aggregated embedding representation for recommendation. To the best of our knowledge, this is the first work that incorporates multi-modal knowledge graph into recommender systems. We conduct extensive experiments on two real datasets from different domains, results of which demonstrate that our model MKGAT can successfully employ MMKGs to improve the quality of recommendation system.

With the rapid development of mobile networks and the widespread usage of mobile devices, spatial crowdsourcing, which refers to assigning location-based tasks to moving workers, has drawn increasing attention. One of the major issues in spatial crowdsourcing is task assignment, which allocates tasks to appropriate workers. However, existing works generally assume the static offline scenarios, where the spatio-temporal information of all the workers and tasks is determined and known a priori. Ignorance of the dynamic spatio-temporal distributions of workers and tasks can often lead to poor assignment results. In this work we study a novel spatial crowdsourcing problem, namely Predictive Task Assignment (PTA), which aims to maximize the number of assigned tasks by taking into account both current and future workers/tasks that enter the system dynamically with location unknown in advance. We propose a two-phase data-driven framework. The prediction phase hybrids different learning models to predict the locations and routes of future workers and designs a graph embedding approach to estimate the distribution of future tasks. In the assignment component, we propose both greedy algorithm for large-scale applications and optimal algorithm with graph partition based decomposition. Extensive experiments on two real datasets demonstrate the effectiveness of our framework.

Abstract With the pervasiveness of GPS-enabled smart devices and increased wireless communication technologies, spatial crowdsourcing (SC) has drawn increasing attention in assigning location-sensitive tasks to moving workers. In real-world scenarios, for the complex tasks, SC is more likely to assign each task to more than one worker, called group task assignment (GTA), for the reason that an individual worker cannot complete the task well by herself. It is a challenging issue to assign worker groups the tasks that they are interested in and willing to perform. In this paper, we propose a novel framework for group task assignment based on worker groups’ preferences, which includes two components: social impact-based preference modeling (SIPM) and preference-aware group task assignment (PGTA). SIPM employs a bipartite graph embedding model and the attention mechanism to learn the social impact-based preferences of different worker groups on different task categories. PGTA utilizes an optimal task assignment algorithm based on the tree decomposition technique to maximize the overall task assignments, in which we give higher priorities to the worker groups showing more interests in the tasks. We further optimize the original framework by proposing strategies to improve the effectiveness of group task assignment, wherein a deep learning method and the group consensus are taken into consideration. Extensive empirical studies verify that the proposed techniques and optimization strategies can settle the problem nicely.

The pervasiveness of GPS-enabled devices and wireless communication technologies results in massive trajectory data, incurring expensive cost for storage, transmission, and query processing. To relieve this problem, in this paper we propose a novel framework for compressing trajectory data, REST (Reference-based Spatio-temporal trajectory compression), by which a raw trajectory is represented by concatenation of a series of historical (sub-)trajectories (called reference trajectories) that form the compressed trajectory within a given spatio-temporal deviation threshold. In order to construct a reference trajectory set that can most benefit the subsequent compression, we propose three kinds of techniques to select reference trajectories wisely from a large dataset such that the resulting reference set is more compact yet covering most footprints of trajectories in the area of interest. To address the computational issue caused by the large number of combinations of reference trajectories that may exist for resembling a given trajectory, we propose efficient greedy algorithms that run in the blink of an eye and dynamic programming algorithms that can achieve the optimal compression ratio. Compared to existing work on trajectory compression, our framework has few assumptions about data such as moving within a road network or moving with constant direction and speed, and better compression performance with fairly small spatio-temporal loss. In addition, by indexing the reference trajectories directly with an in-memory R-tree and building connections to the raw trajectories with inverted index, we develop an extremely efficient algorithm that can answer spatio-temporal range queries over trajectories in their compressed form. Extensive experiments on a real taxi trajectory dataset demonstrate the superiority of our framework over existing representative approaches in terms of both compression ratio and efficiency.

With the proliferation of GPS-enabled smart devices and increased availability of wireless network, spatial crowdsourcing (SC) has been recently proposed as a framework to automatically request workers (i.e., smart device carriers) to perform location-sensitive tasks (e.g., taking scenic photos, reporting events). In this paper, we study a destination-aware task assignment problem that concerns the optimal strategy of assigning each task to proper worker such that the total number of completed tasks can be maximized whilst all workers can reach their destinations before deadlines after performing assigned tasks. Finding the global optimal assignment turns out to be an intractable problem since it does not imply optimal assignment for individual worker. Observing that the task assignment dependency only exists amongst subsets of workers, we utilize tree-decomposition technique to separate workers into independent clusters and develop an efficient depth-first search algorithm with progressive bounds to prune non-promising assignments. In order to make our proposed framework applicable to more scenarios, we further optimize the original framework by proposing strategies to reduce the overall travel cost and allow each task to be assigned to multiple workers. Extensive empirical studies verify that the proposed technique and optimization strategies perform effectively and settle the problem nicely.

The time signals behind a user's historical behaviors are important for better inferring what she prefers to interact with at the next time. For the attention-based recommendation methods, relative position encoding and time intervals division are two common ways to model the time signal behind each behavior. They either only consider the relative position of each behavior in the behavior sequence, or process the continuous temporal features into discrete category features for subsequent tasks, which can hardly capture the dynamic preferences of a user. In addition, although the existing recommendation methods have considered both long-term preference and short-term preference, they ignore the fact that the long-term preference of a user may be multi-faceted, and it is difficult to learn a user's fine-grained short-term preference. In this paper, we propose a Dynamic Multi-faceted Fine-grained Preference model (DMFP), where the multi-hops attention mechanism and the feature-level attention mechanism together with a vertical convolution operation are adopted to capture users' multi-faceted long-term preference and fine-grained short-term preference, respectively. Therefore, DMFP can better support the next item recommendation. Extensive experiments on three real-world datasets illustrate that our model can improve the effectiveness of the recommendation compared with the state-of-the-art methods.

Exploring Hidden Points of Interest (H-POIs), which are rarely referred in online search and recommendation systems due to insufficient check-in records, benefits business and individuals. In this work, we investigate how to eliminate the hidden feature of H-POIs by enhancing conventional crowdsourced ranking aggregation framework with heterogeneous (i.e., H-POI and Popular Point of Interest (P-POI)) pairwise tasks. We propose a two-phase solution focusing on both effectiveness and efficiency. In offline phase, we substantially narrow down the search space by retrieving a set of geo-textual valid heterogeneous pairs as the initial candidates and develop two practical data-driven strategies to compute worker qualities. In the online phase, we minimize the cost of assessment by introducing an active learning algorithm to jointly select pairs and workers with worker quality, uncertainty of P-POI rankings and uncertainty of the model taken into account. In addition, a (Minimum Spanning) Tree-constrained Skip search strategy is proposed for the purpose of reducing search time cost. Empirical experiments based on real POI datasets verify that the ranking accuracy of H-POIs can be greatly improved with small number of query iterations.

The pervasiveness of GPS-enabled devices and wireless communication technologies results in massive trajectory data, incurring expensive cost for storage, transmission, and query processing. To relieve this problem, in this paper we propose a novel framework for compressing trajectory data, REST (Reference-based Spatio-temporal trajectory compression), by which a raw trajectory is represented by concatenation of a series of historical (sub-)trajectories (called reference trajectories) that form the compressed trajectory within a given spatio-temporal deviation threshold. In order to construct a reference trajectory set that can most benefit the subsequent compression, we propose three kinds of techniques to select reference trajectories wisely from a large dataset such that the resulting reference set is more compact yet covering most footprints of trajectories in the area of interest. To address the computational issue caused by the large number of combinations of reference trajectories that may exist for resembling a given trajectory, we propose efficient greedy algorithms that run in the blink of an eye and dynamic programming algorithms that can achieve the optimal compression ratio. Compared to existing work on trajectory compression, our framework has few assumptions about data such as moving within a road network or moving with constant direction and speed, and better compression performance with fairly small spatio-temporal loss. Extensive experiments on a real taxi trajectory dataset demonstrate the superiority of our framework over existing representative approaches in terms of both compression ratio and efficiency.

With the proliferation of GPS-enabled smart devices and increased availability of wireless network, spatial crowdsourcing (SC) has been recently proposed as a framework to automatically request workers (i.e., smart device carriers) to perform location-sensitive tasks (e.g., taking scenic photos, reporting events). In this paper we study a destination-aware task assignment problem that concerns the optimal strategy of assigning each task to proper worker such that the total number of completed tasks can be maximized whilst all workers can reach their destinations before deadlines after performing assigned tasks. Finding the global optimal assignment turns out to be an intractable problem since it does not imply optimal assignment for individual worker. Observing that the task assignment dependency only exists amongst subsets of workers, we utilize tree-decomposition technique to separate workers into independent clusters and develop an efficient depth-first search algorithm with progressive bounds to prune non-promising assignments. Our empirical studies demonstrate that our proposed technique is quite effective and settle the problem nicely.