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.

In the realm of multi-behavior sequential recommendation (MBSR), the complexity and heterogeneity of user interactions pose substantial challenges for sequence modeling. Existing studies involve significant efforts in combining different modules to learn more expressive multi-behavior sequence representations or designing strategies to extract user preferences related to the target behavior. Despite their effectiveness, these methods neglect a thorough analysis of how behavioral information shapes the probability distribution for next-item prediction, which is crucial for accurately modeling user preferences. To this end, we first analyze the learning distribution of MBSR, shedding light on the significance of target behavior in next-item prediction. Building upon this insight, we propose a Behavior-Aware Regularization approach for multi-behavior sequential Rec ommendation (BAR4Rec), where we introduce a regularization loss function to preserve the intrinsic constraints of target behavior. In this way, the target probability distribution is extracted from the whole distribution and naturally evolves into a more compatible and tractable form, thus facilitating model design and training. We evaluate the proposed method on three real-world datasets, and the results validate the efficacy of our approach.

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.

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.

Road traffic forecasting is crucial in real-world intelligent transportation scenarios like traffic dispatching and path planning in city management and personal traveling. Spatio-temporal graph neural networks (STGNNs) stand out as the mainstream solution in this task. Nevertheless, the quadratic complexity of remarkable dynamic spatial modeling-based STGNNs has become the bottleneck over large-scale traffic data. From the spatial data management perspective, we present a novel Transformer framework called PatchSTG to efficiently and dynamically model spatial dependencies for large-scale traffic forecasting with interpretability and fidelity. Specifically, we design a novel irregular spatial patching to reduce the number of points involved in the dynamic calculation of Transformer. The irregular spatial patching first utilizes the leaf K-dimensional tree (KDTree) to recursively partition irregularly distributed traffic points into leaf nodes with a small capacity, and then merges leaf nodes belonging to the same subtree into occupancy-equaled and non-overlapped patches through padding and backtracking. Based on the patched data, depth and breadth attention are used interchangeably in the encoder to dynamically learn local and global spatial knowledge from points in a patch and points with the same index of patches. Experimental results on four real world large-scale traffic datasets show that our PatchSTG achieves train speed and memory utilization improvements up to 10x and 4x with the state-of-the-art performance.

Approximate nearest neighbor search is fundamental in information retrieval. Previous partition-based methods enhance search efficiency by probing partial partitions, yet they face two common issues. In the query phase, a common strategy is to probe partitions based on the distance ranks of a query to partition centroids, which inevitably probes irrelevant partitions as it ignores data distribution. In the partition construction phase, all partition-based methods face the boundary problem that separates a query's nearest neighbors to multiple partitions, resulting in a long-tailed kNN distribution and degrading the optimal nprobe (i.e., the number of probing partitions). To address this gap, we propose LIRA, a LearnIng-based queRy-aware pArtition framework. Specifically, we propose a probing model to directly probe the partitions containing the kNN of a query, which can reduce probing waste and allow for query-aware probing with nprobe individually. Moreover, we incorporate the probing model into a learning-based redundancy strategy to mitigate the adverse impact of the long-tailed kNN distribution on search efficiency. Extensive experiments on real-world vector datasets demonstrate the superiority of LIRA in the trade-off among accuracy, latency, and query fan-out. The codes are available at https://github.com/SimoneZeng/LIRA-ANN-search.

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.

Autonomous driving is an emerging technology that has advanced rapidly over the last decade. Modern transportation is expected to benefit greatly from a wise decision-making framework of autonomous vehicles, including the improvement of mobility and the minimization of risks and travel time. However, existing methods either ignore the complexity of environments only fitting straight roads, or ignore the impact on surrounding vehicles during optimization phases, leading to weak environmental adaptability and incomplete optimization objectives. To address these limitations, we propose a pArameterized decision-making framework with mU lti-modality percepTiOn based on deep reinforcement learning, called AUTO. We conduct a comprehensive perception to capture the state features of various traffic participants around the autonomous vehicle, based on which we design a graph-based model to learn a state representation of the multi-modal semantic features. To distinguish between lane-following and lane-changing, we decompose an action of the autonomous vehicle into a parameterized action structure that first decides whether to change lanes and then computes an exact action to execute. A hybrid reward function takes into account aspects of safety, traffic efficiency, passenger comfort, and impact to guide the framework to generate optimal actions. In addition, we design a regularization term and a multi-worker paradigm to enhance the training. Extensive experiments offer evidence that AUTO can advance state-of-the-art in terms of both macroscopic and microscopic effectiveness.

As the non-fungible token (NFT) market flourishes, price prediction emerges as a pivotal direction for investors gaining valuable insight to maximize returns. However, existing works suffer from a lack of practical definitions and standardized evaluations, limiting their practical application. Moreover, the influence of users' multi-behaviour transactions that are publicly accessible on NFT price is still not explored and exhibits challenges. In this paper, we address these gaps by presenting a practical and hierarchical problem definition. This approach unifies both collection-level and token-level task and evaluation methods, which cater to varied practical requirements of investors. To further understand the impact of user behaviours on the variation of NFT price, we propose a general wallet profiling framework and develop a COmmunity enhanced Multi-bEhavior Transaction graph model, named COMET. COMET profiles wallets with a comprehensive view and considers the impact of diverse relations and interactions within the NFT ecosystem on NFT price variations, thereby improving prediction performance. Extensive experiments conducted in our deployed system demonstrate the superiority of COMET, underscoring its potential in the insight toolkit for NFT investors.

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.

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.

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.

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.

Trajectory prediction enables the fast and accurate response of autonomous driving navigation in complex and dense traffics. In this paper, we present a novel trajectory prediction network called Heterogeneous Graph Aggregation (HeGA) for high-density heterogeneous traffic, where the traffic agents of various categories interact densely with each other. To predict the trajectory of a target agent, HeGA first automatically selects neighbors that interact with it by our proposed adaptive neighbor selector, and then aggregates their interactions based on a novel two-phase aggregation transformer block. At last, the historical residual connection LSTM enhances the historical information awareness and decodes the spatial coordinates as the prediction results. Extensive experiments on real data demonstrate that the proposed network significantly outperforms the existing state-of-the-art competitors by over 27% on average displacement error (ADE) and over 31% on final displacement error (FDE). We also deploy HeGA in a state-of-the-art framework for autonomous driving, demonstrating its superior applicability based on three simulated environments with different densities and complexities.

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.

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.

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.

Human mobility recovery is of great importance for a wide range of location-based services. However, recovering human mobility is not trivial because of three challenges: 1) complex transition patterns among locations; 2) multi-level periodicity and shifting periodicity of human mobility; 3) sparsity of the collected trajectory data. In this paper, we propose PeriodicMove, a neural attention model based on graph neural network for human mobility recovery from lengthy and sparse trajectories. In PeriodicMove, we first construct a directed graph for each trajectory and capture complex location transition patterns using graph neural network. Then, we design two attention mechanisms which capture multi-level periodicity and shifting periodicity of human mobility respectively. Finally, a spatial-aware loss function is proposed to incorporate spatial proximity into the model optimization, which alleviates the data sparsity problem. We perform extensive experiments and the evaluation results demonstrate that PeriodicMove yields significant improvements over the competitors on two representative real-life mobility datasets. In addition, by providing high-quality mobility data, our model can benefit a variety of mobility-oriented downstream applications.

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%.

Multivariate time series forecasting has been drawing increasing attention due to its prevalent applications. It has been commonly assumed that leveraging latent dependencies between pairs of variables can enhance prediction accuracy. However, most existing methods suffer from static variable relevance modeling and ignorance of correlation between temporal scales, thereby failing to fully retain the dynamic and periodic interdependencies among variables, which are vital for long- and short-term forecasting. In this paper, we propose METRO, a generic framework with multi-scale temporal graphs neural networks, which models the dynamic and cross-scale variable correlations simultaneously. By representing the multivariate time series as a series of temporal graphs, both intra- and inter-step correlations can be well preserved via message-passing and node embedding update. To enable information propagation across temporal scales, we design a novel sampling strategy to align specific steps between higher and lower scales and fuse the cross-scale information efficiently. Moreover, we provide a modular interpretation of existing GNN-based time series forecasting works as specific instances under our framework. Extensive experiments conducted on four benchmark datasets demonstrate the effectiveness and efficiency of our approach. METRO has been successfully deployed onto the time series analytics platform of Huawei Cloud, where a one-month online test demonstrated that up to 20% relative improvement over state-of-the-art models w.r.t. RSE can be achieved.

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.