Trajectory anomaly detection, as a fundamental operation for moving object pattern discovery, plays an irreplaceable and critical role in spatio-temporal location-based services. Conducting online detection based on the current positions and their contextual semantics can significantly enhance the value of trajectory data. However, existing approaches suffer from two fundamental limitations: 1) treat trajectories as indivisible sequences or apply rigid segmentation strategies, and 2) use of a single detection model that struggles to adapt to concept drift caused by evolving trajectory distributions. Such limitations make it impossible to detect abnormal trajectories in a timely and semantically comprehensive manner. To fill this gap, we propose Stamp, a novel framework for Semantic-aware sub-Trajectory Anomaly detection with a diffusion Multi-model Pool. In particular, Stamp comprises three key innovations: 1. It employs a semantic-driven dynamic segmentation mechanism that identifies natural breakpoints in trajectories based on changes in road semantics, rather than fixed rules. 2. It enhances trajectory representation by embedding road network semantic vectors, capturing both spatial geometry and functional urban characteristics. 3. It employs a pool of diffusion models that dynamically evolves through reliability assessment, similarity measurement, and strategic merging operations, ensuring adaptability to concept drift while leveraging the superior generative capabilities of diffusion models over traditional autoencoders. Experimental results demonstrate that Stamp improves detection efficiency by 35%, AUPR by 5.6%, and F1-score by 2.7% on two large-scale real-world urban trajectory datasets when compared to state-of-the-art methods, demonstrating its effectiveness for real-time anomaly detection in complex urban environments.

Trajectory metric learning, which supports the trajectory similarity search, is one of the most fundamental tasks in spatial-temporal data analysis. However, existing trajectory metric learning methods rely on massive labels of pairwise trajectory distance, and thus cannot be applied to few-shot scenarios frequently occurring in real-world applications. Though performance drops caused by insufficient labels can be alleviated by knowledge distillation, we demonstrate that they cannot be directly applied to few-shot trajectory metric learning due to the domain shift problem. To this end, this paper proposes invariant and relaxed learning enhanced knowledge distillation method TMLKD for few-shot trajectory metric learning, such that domain-invariant representation and rank knowledge can be distilled. Specifically, in the representation learning phase, it first employs an adversarial sub-network to distinguish domain-specific and domain-invariant information, so as to distill transferable representation knowledge from teacher models. To mitigate the few-shot problem in student model training, we further enrich sparse labels of the target domain by utilizing the rank knowledge revealed in teachers' predictions. Particularly, TMLKD employs a list-wise learning-to-rank approach to learn the relaxed trajectory ranking orders instead of focusing on all the samples inefficiently. Finally, to guide accurate distillation, we adaptively assign reliability of teacher prediction by utilizing the ground-truth labels, to avoid misleading the student model with low-quality teacher predictions. Extensive experiments on three real-world datasets demonstrate the superiority of our model.

Multi-Modal Knowledge Graphs (MMKGs), comprising relational triples and related multi-modal data (e.g., text and images), usually suffer from the problems of low coverage and incompleteness. To mitigate this, existing studies introduce a fundamental MMKG fusion task, i.e., Multi-Modal Entity Alignment (MMEA) that identifies equivalent entities across multiple MMKGs. Despite MMEA's significant advancements, effectively integrating MMKGs remains challenging, mainly stemming from two core limitations: 1) entity ambiguity, where real-world entities across different MMKGs may possess multiple corresponding counterparts or alternative identities; and 2) severe noise within multi-modal data. To tackle these limitations, a new task MMER (Multi-Modal Entity Resolution), which expands the scope of MMEA to encompass entity ambiguity, is introduced. To tackle this task effectively, we develop a novel model ADMH-ER (Adaptive Denoising Multi-modal Hybrid for Entity Resolution) that incorporates several crucial modules: 1) multi-modal knowledge encoders, which are crafted to obtain entity representations based on multi-modal data sources; 2) an adaptive denoising multi-modal hybrid module that is designed to tackle challenges including noise interference, multi-modal heterogeneity, and semantic irrelevance across modalities; and 3) a hierarchical multi-objective learning strategy, which is proposed to ensure diverse convergence capabilities among different learning objectives. Experimental results demonstrate that ADMH-ER outperforms state-of-the-art methods.

With the popularization of mobile applications and the timely acquisition of fresh data, real-time clustering and its evolution analysis have become the primary operations for data processing and knowledge discovery. Such continuous queries on massive objects are computation-intensive tasks in dynamic scenarios. However, existing clustering techniques are incompetent to achieve decent performance when computation-intensive operations frequently occur in streaming scenarios, which is caused by two challenges: (i) uncertainty of the clustering frequency; (ii) unpredictable distribution evolution. Hence, it is critical to find a lightweight model that can cluster the high-speed dynamic instances while exploiting the evolution amid different clustering results. This paper focuses on the problem of real-time clustering on streaming data in computation-intensive and high-dynamics tasks, through a framework Ocean, consisting of the Online clustering algorithm and evolution analysis. Particularly, the framework conceives a flexible composite window to augment the knowledge mining, achieving a proper real-time response in various scenarios. The evolution analysis supports full life-cycle detection, improving the adaptability to dynamic concept drifts and multiple patterns. Inspired by the grid partition strategy, this framework adopts grid feature vectors to capture the significant changes in streaming data. Furthermore, we propose an optimization that removes sparse grids timely and performs the online clustering adaptively for space and time efficiency. It is proven to be effective both theoretically and experimentally. This strategy enables real-time clustering for dynamic streaming data without degrading the clustering quality or increasing the computation cost. Experiments on real datasets and synthetic datasets verify the accuracy and effectiveness of Ocean compared to the state-of-the-art approaches, as well as the superior ability to perform clustering in a real-time manner.

Sequential Recommendation (SR) has received increasing attention due to its ability to capture user dynamic preferences. Recently, Contrastive Learning (CL) provides an effective approach for sequential recommendation by learning invariance from different views of an input. However, most existing data or model augmentation methods may destroy semantic sequential interaction characteristics and often rely on the hand-crafted property of their contrastive view-generation strategies. In this paper, we propose a Meta-optimized Seq2Seq Generator and Contrastive Learning (Meta-SGCL) for sequential recommendation, which applies the meta-optimized two-step training strategy to adaptive generate contrastive views. Specifically, Meta-SGCL first introduces a simple yet effective augmentation method called Sequence-to-Sequence (Seq2Seq) generator, which treats the Variational AutoEncoders (VAE) as the view generator and can constitute contrastive views while preserving the original sequence's semantics. Next, the model employs a meta-optimized two-step training strategy, which aims to adaptively generate contrastive views without relying on manually designed view-generation techniques. Finally, we evaluate our proposed method Meta-SGCL using three public real-world datasets. Compared with the state-of-the-art methods, our experimental results demonstrate the effectiveness of our model and the code is available.11https.//anonymous.4open.science/status/Meta-SGCL-05B5

Sequential recommendation aims to predict the next item of interest to users based on their historical behavior data. Usually, users’ global and local preferences jointly affect the final recommendation result in different ways. Most existing works use transformers to globally model sequences, which makes them face the dilemma of quadratic computational complexity when dealing with long sequences. Moreover, the scope setting of the user’s local preference is usually static and single, and cannot cover richer multi-level local semantics. To this end, we proposed a parallel architecture for capturing global representation and M ulti-granularity L ocal dependencies with M LP for sequential Rec ommendation ( MLM4Rec ). For global representation, we utilize modified MLP-Mixer to capture global information of user sequences due to its simplicity and efficiency. For local representation, we incorporate convolution into MLP and propose a multi-granularity local awareness mechanism for capturing richer local semantic information. Moreover, we introduced a weight pooling method to adaptively fuse local-global representations instead of directly concatenation. Our model has the advantages of low complexity and high efficiency thanks to its simple MLP structure. Experimental results on three public datasets demonstrate the effectiveness of our proposed model. Our code is available here 1 .

Spatial trajectory prediction is a fundamental problem for diverse location-based applications. However, existing methods fall short in learning and generalization, and cannot sufficiently capture users’ spatiotemporal preferences, especially for cold-start users. Moreover, these methods do not explicitly consider the diversity of moving patterns among users and trajectories, i.e., the learning difficulty of different user and trajectory samples, thus hindering the improvement of prediction accuracy. To solve these problems, we propose a novel Curriculum and Contrastive Learning Enhanced Meta-Learner (CCML) that transfers knowledge from users with rich data to cold-start users. Specifically, a Contrastive-based Trajectory Predictor (CTP) is designed as the base model, which utilizes contrastive learning technique on both user-level and trajectory-level, aiming to facilitate a more profound understanding and differentiation of the varied travel behaviors and preferences exhibited by individuals. Meanwhile, CCML also incorporates the curriculum learning and the hard sample mining strategies. It simultaneously considers the learning difficulty of both user and trajectory samples, and presents the learning tasks by an easy-to-hard curriculum. By learning more challenging combinations of user and trajectory samples in each meta-learning iteration, the meta-learner can converge to a better status. Extensive experiments on two real-world datasets demonstrate the superiority of our models.

Sequential recommendation (SR) aims to model user preferences by capturing behavior patterns from their item historical interaction data. Most existing methods model user preference in the time domain, omitting the fact that users’ behaviors are also influenced by various frequency patterns that are difficult to separate in the entangled chronological items. However, few attempts have been made to train SR in the frequency domain, and it is still unclear how to use the frequency components to learn an appropriate representation for the user. To solve this problem, we shift the viewpoint to the frequency domain and propose a novel Contrastive Enhanced SLIde Filter MixEr for Sequential Recommendation, named SLIME4Rec. Specifically, we design a frequency ramp structure to allow the learnable filter slide on the frequency spectrums across different layers to capture different frequency patterns. Moreover, a Dynamic Frequency Selection (DFS) and a Static Frequency Split (SFS) module are proposed to replace the self-attention module for effectively extracting frequency information in two ways. DFS is used to select helpful frequency components dynamically, and SFS is combined with the dynamic frequency selection module to provide a more fine-grained frequency division. Finally, contrastive learning is utilized to improve the quality of user embedding learned from the frequency domain. Extensive experiments conducted on five widely used benchmark datasets demonstrate our proposed model performs significantly better than the state-of-the-art approaches. Our code is available at https://github.com/sudaada/SLIME4Rec.

Map matching is a problem to align recorded location data to a digital map. It has been well studied to map GPS data collected from vehicles to paths in a road network. The problem of Cellular Trajectory Map-Matching (CTMM) is a new problem that deals with trajectories of cellular-based positioning data. It has a wide range of applications, for example, for telecommunication companies to understand and predict traffic information based on telecom tokens obtained from vehicles. CTMM is a significantly more challenging task that faces much lower data precision and higher positioning errors. While Hidden Markov Model (HMM) based methods can achieve satisfactory results for GPS-based map matching, we show that they cannot be directly applied to the CTMM problem. In this paper, we aim at reducing the impact of positioning errors by incorporating knowledge obtained by neural networks into learned probabilities. A multi-relational graph learning method is developed to generate meaningful embedding, with multi-relational useful information fully preserved in a shared space. An attentive neural network is then designed as the learner for observation probability, incorporating the knowledge of the dynamic correlation between roads and cell towers under varying trajectory contexts. A transition probability learner is used to capture implicit deep features for enhanced transition probability modeling. Finally, the learned observation and transition probabilities are seamlessly integrated into HMM to guide more accurate path-finding. Extensive experiments on two large-scale cellular datasets reveal that our approach achieves high accuracy and robustness on CTMM.

Contrastive Learning (CL) performances as a rising approach to address the challenge of sparse and noisy recommendation data. Although having achieved promising results, most existing CL methods only perform either hand-crafted data or model augmentation for generating contrastive pairs to find a proper augmentation operation for different datasets, which makes the model hard to generalize. Additionally, since insufficient input data may lead the encoder to learn collapsed embeddings, these CL methods expect a relatively large number of training data (e.g., large batch size or memory bank) to contrast. However, not all contrastive pairs are always informative and discriminative enough for the training processing. Therefore, a more general CL-based recommendation model called Meta-optimized Contrastive Learning for sequential Recommendation (MCLRec) is proposed in this work. By applying both data augmentation and learnable model augmentation operations, this work innovates the standard CL framework by contrasting data and model augmented views for adaptively capturing the informative features hidden in stochastic data augmentation. Moreover, MCLRec utilizes a meta-learning manner to guide the updating of the model augmenters, which helps to improve the quality of contrastive pairs without enlarging the amount of input data. Finally, a contrastive regularization term is considered to encourage the augmentation model to generate more informative augmented views and avoid too similar contrastive pairs within the meta updating. The experimental results on commonly used datasets validate the effectiveness of MCLRec.

LBS-RT (location-based service in a real-time manner) has become popular because it can provide quick and timely services. Range query is often used in LBS-RT, which finds objects in a specified area, and spatial indices are often used to speed up range query. However, in LBS-RT, there are some difficulties. Spatial index was originally designed to index static dataset, but the dataset is dynamic in LBS-RT, which needs lots of insert and delete operations. To meet the gap, this paper proposes a new distributed spatial index called GQ-QBS. It's a two-layer master-slave mode that consists of a global index and multiple local indices. The global index (GQ-tree) is responsible for the dynamic load balancing and auto-scaling, while the local index (QBS-tree) is for quickly updating and querying. Experiments show the index has a significant advantage in LBS-RT.

With the prevalence of location-based services (LBS), trajectories are being generated rapidly. As is widely used in LBS, top-k trajectory similarity query serves as a key operation, deeply empowering applications such as travel route recommendation and carpooling. Given the rise of deep learning, trajectory representation has been well-proven to speed up this operator. However, existing representation-based computing modes remain two major problems understudied: the low quality of trajectory representation and insufficient support for various trajectory similarity metrics, which make them difficult to apply in practice. Therefore, we propose an Accurate metric-based representation learning approach for fast top-k trajectory similarity query, named Aries. Specifically, Aries has two sophisticated modules: (1) An novel trajectory embedding strategy enhanced by the bidirectional LSTM encoder and spatial attention mechanism, which can extract more precise and comprehensive knowledge. (2) A deep metric learning network aggregating multiple measures for better top-k query. Extensive experiments conducted on real trajectory dataset show that Aries achieves both impressive accuracy and lower training time compared with state-of-the-art solutions. In particular, it achieves 5x-10x speedup and 10%-20% accuracy improvement over Euclidean, Hausdorff, DTW, and EDR measures. Besides, our method can maintain stable performance when handling various scenarios, without repeated training in order to adapt to diverse similarity metrics.

As an essential branch of web service applications, the location-based service (LBS) plays an irreplaceable role in our daily lives. Usually, the LBS is time-sensitive, which requires the system to process trajectory data in a real-time manner. Due to the sensitivity of trajectory data, LBS services may violate users’ privacy. Furthermore, trajectory compression plays a crucial auxiliary role in analyzing and mining massive trajectory raw data such as trajectory clustering and trajectory similarity calculation and can help keep users’ privacy. In other words, trajectory compression serves as the prerequisite for privacy-preserved trajectory data mining, which retains points with high-information content and removes redundant approximate points with low information value under the premise of protecting users’ privacy. We can speed up the applications’ response speed and save computing resources if we take advantage of trajectory compression and provide lightweight data support for big-data-driven web page extraction, convenient for fast and accurate response. Unfortunately, trajectory compression’s current real-time processing capacity is still not big enough and not cost-effective. In terms of the implementation principle, most of the existing works are micro-batch processing. Consequently, the system will overly consume resources and respond with a high latency with the trajectory data inputting. In addition, it is difficult for users to understand and set compression parameters correctly. In this context, we propose an algorithm to incrementally compress the trajectory in real-time based on the azimuth change, and two kinds of user-perceivable parameters are proposed to facilitate real-time specific compression. For verification, our study uses real-world data sets, such as GeoLife Trajectory data. We also found that compared with the current OPW-TR algorithm with better all-around performance, our algorithm dramatically improves the processing speed with a minimal loss of accuracy. Furthermore, thanks to the maintenance of incremental stateful computation, our memory consumption was reduced by 28.5% when processing about 400k records. The memory advantage will become more pronounced as the number of data increases.

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.

Knowledge graph (KG) reasoning is a significant method for KG completion. To enhance the explainability of KG reasoning, some studies adopt reinforcement learning (RL) to complete the multi-hop reasoning. However, RL-based reasoning methods are severely limited by few-shot relations (only contain few triplets). To tackle the problem, recent studies introduce meta-learning into RL-based methods to improve reasoning performance. However, the generalization abilities of their models are limited due to the problem of low reasoning accuracies over hard relations (e.g., language and title). To overcome this problem, we propose a novel model called THML (Two-level Hardness-aware Meta-reinforcement Learning). Specifically, the model contains the following two components: (1) A hardness-aware meta-reinforcement learning method is proposed to predict the missing element by training hardness-aware batches. (2) A two-level hardness-aware sampling is proposed to effectively generate new hardness-aware batches from relation level and relation-cluster level. The generalization ability of our model is significantly improved by repeating the process of these two components in an alternate way. The experimental results demonstrate that THML notably outperforms the state-of-the-art approaches in few-shot scenarios.

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.

Matrix-based scheme (Join-Matrix) can prefectly support distributed stream joins, especially for arbitrary join predicates, because it guarantees any tuples from two streams to meet with each other. However,the dynamics and unpredictability features of stream require quick actions on scheme changing. Otherwise, they may lead to degradation of system throughputs and increament of processing latency with the waste of system resources, such as CPUs and Memories. Since Join-Matrix model has the fixed processing architecture with replicated data, these kinds of adverseness will be magnified. Therefore, it is urgent to find a solution that preserves advantages of Join-Matrix model and promises a good usage to computation resources when it meets scheme changing. In this paper, we propose a cost-effective stream join algorithm, which ensures the adaptability of Join-Matrix but with lower resources consumption. Specifically, a varietal matrix generation algorithm is proposed to generate an irregular matrix scheme for assigning the minimal number of tasks; a lightweight migration algorithm is designed to ensure state migration at a low cost; a complete load balance process framework is described to guarantee the correctness during the scheme changing. We conduct extensive experiments to compare our method with baseline systems on both benchmarks and real-workloads, and explain the results in detail.