Multivariate time series (MTS) anomaly detection identifies abnormal patterns where each timestamp contains multiple variables. Existing MTS anomaly detection methods fall into three categories: reconstruction-based, prediction-based, and classifier-based methods. However, these methods face three key challenges: (1) Unsupervised learning methods, such as reconstruction-based and prediction-based methods, rely on error thresholds, which can lead to inaccuracies; (2) Semi-supervised methods mainly model normal dataand often underuse anomaly labels, limiting detection of subtle anomalies; (3) Supervised learning methods, such as classifier-based approaches, often fail to capture local relationships, incur high computational costs, and are constrained by the scarcity of labeled data. To address these limitations, we proposeMoon, a supervised modality conversion-based multivariate time series anomaly detection framework.Moonenhances the efficiency and accuracy of anomaly detection while providing detailed anomaly analysis reports. First,Moonintroduces a novel multivariate Markov Transition Field (MV-MTF) technique to convert numeric time series data into image representations, capturing relationships across variables and timestamps. Since numeric data retains unique patterns that cannot be fully captured by image conversion alone,Moonemploys a Multimodal-CNN to integrate numeric and image data through a feature fusion model with parameter sharing, enhancing training efficiency. Finally, a SHAP-based anomaly explainer identifies key variables contributing to anomalies, improving interpretability. Extensive experiments on six real-world MTS datasets demonstrate thatMoonoutperforms six state-of-the-art methods by up to 93% in efficiency, 4% in accuracy and, 10.8% in interpretation performance.

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

With the growing availability of large data repositories within and across organizations, it is becoming feasible to selectively acquire data in the wild for data augmentation, tailored to specific downstream tasks. However, current methodologies concentrate primarily on single-dimensional augmentation tasks for tabular data, such as increasing the number of data points or enriching features. These approaches are not designed for trajectory data, which cannot fully utilize its spatio-temporal characteristics, resulting in suboptimal performance.

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

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

Trajectory similarity computation in road networks is crucial for data analytics. However, both non-learning-based and learning-based methods face challenges. First, they suffer from low accuracy due to manual parameter selection for model training and the omission of key spatio-temporal features in road networks. Second, they have low efficiency, stemming from the high time complexity of similarity computation and the time-consuming training process. Third, learning-based methods struggle with poor model generality due to the small size of available training samples. To address these challenges, we propose an effective and efficient trajectory similarity learning framework for road networks, called SimRN. To our knowledge, SimRN is the first deep reinforcement learning (DRL) approach for trajectory similarity computation. Specifically, SimRN consists of three key modules: the spatio-temporal prompt information extraction (STP) module, the trajectory representation based on DRL (TrajRL) module, and the graph contrastive learning (GCL) module. The STP module captures spatio-temporal features from road networks to improve the training of the trajectory representation. The TrajRL module automatically selects optimal parameters and enables parallel training, improving both trajectory representation and the efficiency of similarity computations. The GCL module employs a self-supervised contrastive learning paradigm to generate sufficient samples while preserving spatial constraints and temporal dependencies of trajectories. Extensive experiments on two real-world datasets, compared with three state-of-the-art methods, show that SimRN: (i) improves accuracy by 20%–40%, (ii) achieves speedups of 2–4x, and (iii) demonstrates strong generality, enabling effective similarity learning with very small sample sizes.

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

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

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

Time series classification assigns predefined labels or classes to sequences of data points ordered chronologically, which is a fundamental task for time series analysis. Existing time series classification methods mainly focus on a specific type of time series (i.e., univariate time series or multivariate time series), while failing to support both of them efficiently and effectively. In addition, most of existing multivariate time series classification methods model all variables collectively, resulting in protracted computational times and suboptimal accuracy. In this paper, we introduce TSec, an innovative time series classification framework that exhibits high training efficiency and classification accuracy for both univariate time series and multivariate time series. During online classification, TSec first involves sequence segmentation and de-duplication, and then employs pre-trained models to perform classifications. To opti-mize the classification performance, TSec (i) utilizes correlation analysis to reveal closely interconnected groups of variables within multivariate time series data; (ii) incorporates time series alignment and different sliding windows to generate potential shapelets; (iii) applies PAA and SAX techniques to eliminate duplicates, thereby enhancing the quality of shapelets; (iv) adopts Bi-GRU and GCN-GRU models to effectively capture the characteristics of the two types of time series. Extensive experiments on 112 public univariate time series datasets and 26 public multivariate time series datasets show that TSec can achieve both high efficiency and accuracy compared with the state-of-the-art 19 toolkits.

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

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

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

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

The growth in sensor technologies, IoT devices, and information systems has opened up new opportunities for managing time series data across various domains. Despite significant progress, existing time series management systems face two crucial limitations: insufficient functionality and inadequate adaptability. This highlights the need for more comprehensive systems that not only improve data quality and analysis but also effectively manage the variety and volume of time series data. This paper presents TENDS, a time series management system based on model selection. TENDS uniquely combines advanced functionalities for imputation, prediction, and anomaly detection, offering a comprehensive analysis of time series data. It features (i) an effective model selection mechanism to adapt to various data types and to improve efficiency; (ii) fourteen state-of-the-art prediction methods and three state-of-the-art imputation methods; and (iii) a dynamic expert knowledge base for anomaly detection, evolving continuously with new data to ensure accuracy. TENDS boasts a comprehensive suite of visualization tools. With its configurable offline and online interfaces, TENDS (i) provides extensive flexibility in model selection and parameter adjustment, (ii) facilitates easy visualization of training results, and (iii) supports real-time documentation and statistical analysis of time series.

Selecting a good execution plan can significantly improve the query efficiency of Spark SQL. Several machine learning-based techniques have been proposed to select good execution plans for DBMS, but none of them perform well on Spark SQL due to the following issues. (1) Limited compatibility with Spark SQL: these approaches rely on physical operator enumeration, while Spark SQL doesn't support it; (2) Unreliable cost estimation: they often select execution plans with poor performance due to inaccurate cost estimation; (3) Time-consuming plan enumeration: they take much time to generate a large number of candidate execution plans in Spark SQL. To overcome these issues, in this paper, we propose LEAP, the first learned query optimizer tailored for Spark SQL, which can be integrated seamlessly into Spark SQL and solves the compatibility issue. Also, to avoid the unreliable cost value estimation, LEAP selects execution plans with an estimation-free method, which directly performs comparisons between the plans. Furthermore, LEAP employs an efficient progressive plan enumeration algorithm with pruning techniques to find better plans with fewer enumerations. Extensive experiments on three public benchmarks show the effectiveness of LEAP. It reduces the end-to-end execution time of the native optimizer by up to 54% and other learned methods by up to 94%.

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

Spatio-temporal trajectory analytics are useful in diversified applications such as urban planning, infrastructure development, and vehicular networks. Trajectory similarity measure, which aims to evaluate the distance between two trajectories, is a fundamental functionality of trajectory analytics. In this paper, we propose a comprehensive survey that investigates all the most common and representative spatio-temporal trajectory measures. First, we provide an overview of spatio-temporal trajectory measures in terms of three hierarchical perspectives: Non-learning versus Learning, Free Space versus Road Network, and Standalone versus Distributed. Next, we present an evaluation benchmark by designing five real-world transformation scenarios. Based on this benchmark, extensive experiments are conducted to study the effectiveness, robustness, efficiency, and scalability of each measure, which offers guidelines for trajectory measure selection among multiple techniques and applications such as trajectory data mining, deep learning, and distributed processing. Specifically, i) Effectiveness: In terms of trajectory length, DFD and Seg-Frechet are length-sensitive, while OWD and Hausdorff always return same results when varying query trajectory length. In terms of trajectory shape, LCRS and LORS are able to effectively find similar trajectories for query trajectories with different shapes; ii) Robustness: Learning based measures are more robust compared with non-learning based ones. Among non-learning based measures, DFD, Hausdorff, OWD and Seg-Frechet are relatively non-sensitive to noises and different sampling rates; and iii) Efficiency& Scalability: Compared to non-learning based measures, learning based and distributed based measures are more efficient and scalable.

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

Entity alignment (EA) aims to find equivalent entities in different knowledge graphs (KGs). State-of-the-art EA approaches generally use Graph Neural Networks (GNNs) to encode entities. However, most of them train the models and evaluate the results in a full-batch fashion, which prohibits EA from being scalable on large-scale datasets. To enhance the usability of GNN-based EA models in real-world applications, we present SEA, a scalable entity alignment system that enables to (i) train large-scale GNNs for EA, (ii) speed up the normalization and the evaluation process, and (iii) report clear results for users to estimate different models and parameter settings. SEA can be run on a computer with merely one graphic card. Moreover, SEA encompasses six state-of-the-art EA models and provides access for users to quickly establish and evaluate their own models. Thus, SEA allows users to perform EA without being involved in tedious implementations, such as negative sampling and GPU-accelerated evaluation. With SEA, users can gain a clear view of the model performance. In the demonstration, we show that SEA is user-friendly and is of high scalability even on computers with limited computational resources.

Entity alignment (EA) is a fundamental data integration task that identifies equivalent entities between different knowledge graphs (KGs). Temporal Knowledge graphs (TKGs) extend traditional knowledge graphs by introducing timestamps, which have received increasing attention. State-of-the-art time-aware EA studies have suggested that the temporal information of TKGs facilitates the performance of EA. However, existing studies have not thoroughly exploited the advantages of temporal information in TKGs. Also, they perform EA by pre-aligning entity pairs, which can be labor-intensive and thus inefficient. In this paper, we present DualMatch that effectively fuses the relational and temporal information for EA. DualMatch transfers EA on TKGs into a weighted graph matching problem. More specifically, DualMatch is equipped with an unsupervised method, which achieves EA without necessitating the seed alignment. DualMatch has two steps: (i) encoding temporal and relational information into embeddings separately using a novel label-free encoder, Dual-Encoder; and (ii) fusing both information and transforming it into alignment using a novel graph-matching-based decoder, GM-Decoder. DualMatch is able to perform EA on TKGs with or without supervision, due to its capability of effectively capturing temporal information. Extensive experiments on three real-world TKG datasets offer the insight that DualMatch significantly outperforms the state-of-the-art methods.

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

Hosting database services on cloud systems has become a common practice. This has led to the increasing volume of database workloads, which provides the opportunity for pattern analysis. Discovering workload patterns from a business logic perspective is conducive to better understanding the trends and characteristics of the database system. However, existing workload pattern discovery systems are not suitable for large-scale cloud databases which are commonly employed by the industry. This is because the workload patterns of large-scale cloud databases are generally far more complicated than those of ordinary databases. In this paper, we propose Alibaba Workload Miner (AWM), a real-time system for discovering workload patterns in complicated large-scale workloads. AW M encodes and discovers the SQL query patterns logged from user requests and optimizes the querying processing based on the discovered patterns. First, Data Collection & Preprocessing Module collects streaming query logs and encodes them into high-dimensional feature embeddings with rich semantic contexts and execution features. Next, Online Workload Mining Module separates encoded query by business groups and discovers the workload patterns for each group. Meanwhile, Offline Training Module collects labels and trains the classification model using the labels. Finally, Pattern-based Optimizing Module optimizes query processing in cloud databases by exploiting discovered patterns. Extensive experimental results on one synthetic dataset and two real-life datasets (extracted from Alibaba Cloud databases) show that AW M enhances the accuracy of pattern discovery by 66% and reduce the latency of online inference by 22%, compared with the state-of-the-arts.

Time series forecasting, that predicts events through a sequence of time, has received increasing attention in past decades. The diverse range of time series forecasting models presents a challenge for selecting the most suitable model for a given dataset. As such, the Alibaba Cloud database monitoring system must address the issue of selecting an optimal forecasting model for a single time series data. While several model selection frameworks, including AutoAI-TS, have been developed to predict a dataset, their effectiveness may be limited as they may not adapt well to all types of time series, resulting in reduced prediction accuracy. Alternatively, models such as AutoForecast, which train on individual data points, may offer better adaptability but are limited by longer training time required. In this paper, we introduce SimpleTS, a versatile framework for time series forecasting that exhibits high efficiency and accuracy across all types of time series data. When performing an online prediction task, SimpleTS first classifies input time series into one type, and then efficiently selects the most suitable prediction model for this type. To optimize performance, SimpleTS (i) clusters models with similar performance to improve the efficiency of classification; (ii) uses soft labeling and weighted representation learning to achieve higher classification accuracy for different time series types. Extensive experiments on 3 private datasets and 52 public datasets show that SimpleTS outperforms the state-of-the-art toolkits in terms of both training time and prediction accuracy.

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

Entity alignment (EA) aims at finding equivalent entities in different knowledge graphs (KGs). Embedding-based approaches have dominated the EA task in recent years. Those methods face problems that come from the geometric properties of embedding vectors, including hubness and isolation. To solve these geometric problems, many normalization approaches have been adopted for EA. However, the increasing scale of KGs renders it hard for EA models to adopt the normalization processes, thus limiting their usage in real-world applications. To tackle this challenge, we present ClusterEA, a general framework that is capable of scaling up EA models and enhancing their results by leveraging normalization methods on mini-batches with a high entity equivalent rate. ClusterEA contains three components to align entities between large-scale KGs, including stochastic training, ClusterSampler, and SparseFusion. It first trains a large-scale Siamese GNN for EA in a stochastic fashion to produce entity embeddings. Based on the embeddings, a novel ClusterSampler strategy is proposed for sampling highly overlapped mini-batches. Finally, ClusterEA incorporates SparseFusion, which normalizes local and global similarity and then fuses all similarity matrices to obtain the final similarity matrix. Extensive experiments with real-life datasets on EA benchmarks offer insight into the proposed framework, and suggest that it is capable of outperforming the state-of-the-art scalable EA framework by up to 8 times in terms of [email protected]

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

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