Pub Date : 2026-01-20DOI: 10.1109/TKDE.2026.3656199
Jun Wang;Miaomiao Li;Zhenglai Li;Hao Yu;Suyuan Liu;Dayu Hu;Chang Tang;Xinwang Liu
Anchor graph learning has become a widely used technique for significantly reducing the computational complexity in existing multi-view clustering methods. However, most existing approaches select anchors independently for each view and then generate the consensus graph by directly fusing all anchor graphs. This process overlooks the correspondence between anchor sets across different views, i.e., the column order correspondence of the anchor graphs. To address this limitation, we propose a novel anchor-based tensor multi-rank constraint multi-view clustering method (TMC). Specifically, TMC captures the high-order structural information of the original data by constructing an anchor graph tensor and enforcing a multi-rank constraint to induce a block-diagonal structure. Additionally, to enhance anchor consistency across all view, we construct the anchor graph of each view into an anchor tensor and impose a low-rank constraint on it. In this way, the block-diagonal structure of each anchor graph maintains an approximate alignment between anchors. Furthermore, we provide theoretical proof that the generated anchor graphs inherently exhibit a block-diagonal structure. Extensive experimental results on six multi-view datasets demonstrate that TMC outperforms existing state-of-the-art methods, highlighting its effectiveness in multi-view clustering task.
{"title":"Tensor Multi-Rank Constraint Guided Anchor-Wise Adaptive Alignment for Multi-View Clustering","authors":"Jun Wang;Miaomiao Li;Zhenglai Li;Hao Yu;Suyuan Liu;Dayu Hu;Chang Tang;Xinwang Liu","doi":"10.1109/TKDE.2026.3656199","DOIUrl":"https://doi.org/10.1109/TKDE.2026.3656199","url":null,"abstract":"Anchor graph learning has become a widely used technique for significantly reducing the computational complexity in existing multi-view clustering methods. However, most existing approaches select anchors independently for each view and then generate the consensus graph by directly fusing all anchor graphs. This process overlooks the correspondence between anchor sets across different views, i.e., the column order correspondence of the anchor graphs. To address this limitation, we propose a novel anchor-based tensor multi-rank constraint multi-view clustering method (TMC). Specifically, TMC captures the high-order structural information of the original data by constructing an anchor graph tensor and enforcing a multi-rank constraint to induce a block-diagonal structure. Additionally, to enhance anchor consistency across all view, we construct the anchor graph of each view into an anchor tensor and impose a low-rank constraint on it. In this way, the block-diagonal structure of each anchor graph maintains an approximate alignment between anchors. Furthermore, we provide theoretical proof that the generated anchor graphs inherently exhibit a block-diagonal structure. Extensive experimental results on six multi-view datasets demonstrate that TMC outperforms existing state-of-the-art methods, highlighting its effectiveness in multi-view clustering task.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"38 3","pages":"2015-2027"},"PeriodicalIF":10.4,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146162179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-19DOI: 10.1109/TKDE.2026.3651564
Zhiwei Li;Cheng Wang
Collaborative allocation effectively integrates multi-party data and improves the quality of collaborative data-driven decisions. Equitable data utility valuation facilitates accurately quantified contributions and stimulates collaborative engagement, which forms the fundamental for collaborative allocation. The Shapley value is the dominant allocation scheme, making it the first choice for data utility valuation. However, calculating the exact Shapley value requires exponential utility function evaluations and factorial marginal contribution calculations, which limits scalability for large datasets. The most mainstream methods use various approximation techniques, including Monte Carlo sampling, lightweight model replacing, and stratified computation, to reduce computational costs. Nonetheless, these methods lack the guaranteed theoretical bounds for approximation errors in reducing computational costs. Finding the optimal trade-off between computational cost and approximation error is essential for practical data valuation. In this paper, we propose a stratified framework named Light Shapley for calculating Shapley values by incorporating quantization-aware training. For scenarios involving more players, we propose a cost-first method that achieves significant computational cost reductions while keeping the error within acceptable ranges. For scenarios with fewer players, we propose an error-first method that reduces the computational cost to less than half of the exact calculation while maintaining accuracy. Theoretical analysis and experimental results provide compelling evidence that Light Shapley balances computational cost and approximation error, enabling efficient and effective data utility valuation.
{"title":"Light Shapley: Improving the Scalability of Equitable Data Utility Valuation","authors":"Zhiwei Li;Cheng Wang","doi":"10.1109/TKDE.2026.3651564","DOIUrl":"https://doi.org/10.1109/TKDE.2026.3651564","url":null,"abstract":"Collaborative allocation effectively integrates multi-party data and improves the quality of collaborative data-driven decisions. Equitable data utility valuation facilitates accurately quantified contributions and stimulates collaborative engagement, which forms the fundamental for collaborative allocation. The Shapley value is the dominant allocation scheme, making it the first choice for data utility valuation. However, calculating the exact Shapley value requires exponential utility function evaluations and factorial marginal contribution calculations, which limits scalability for large datasets. The most mainstream methods use various approximation techniques, including Monte Carlo sampling, lightweight model replacing, and stratified computation, to reduce computational costs. Nonetheless, these methods lack the guaranteed theoretical bounds for approximation errors in reducing computational costs. Finding the optimal trade-off between computational cost and approximation error is essential for practical data valuation. In this paper, we propose a stratified framework named Light Shapley for calculating Shapley values by incorporating quantization-aware training. For scenarios involving more players, we propose a cost-first method that achieves significant computational cost reductions while keeping the error within acceptable ranges. For scenarios with fewer players, we propose an error-first method that reduces the computational cost to less than half of the exact calculation while maintaining accuracy. Theoretical analysis and experimental results provide compelling evidence that Light Shapley balances computational cost and approximation error, enabling efficient and effective data utility valuation.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"38 3","pages":"1826-1842"},"PeriodicalIF":10.4,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146162190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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, previous one-to-one deep 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-many spatio-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.
{"title":"Unraveling Spatio-Temporal Foundation Models via the Pipeline Lens: A Comprehensive Review","authors":"Yuchen Fang;Hao Miao;Yuxuan Liang;Liwei Deng;Yue Cui;Ximu Zeng;Yuyang Xia;Yan Zhao;Torben Bach Pedersen;Christian S. Jensen;Xiaofang Zhou;Kai Zheng","doi":"10.1109/TKDE.2026.3651536","DOIUrl":"https://doi.org/10.1109/TKDE.2026.3651536","url":null,"abstract":"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, previous <italic>one-to-one</i> deep 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, <italic>one-to-many</i> spatio-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.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"38 3","pages":"2040-2063"},"PeriodicalIF":10.4,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146162227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-12DOI: 10.1109/TKDE.2026.3652729
Ruohan Yang;Muhammad Asif Ali;Huan Wang;Zhongfei Zhang;Junyang Chen;Di Wang
Link prediction for attributed graphs has garnered significant attention due to its ability to enhance predictive performance by leveraging multi-modal node attributes. However, real-world challenges such as privacy concerns, content restrictions, and attribute constraints often result in nodes facing varying degrees of missing modalities in their attributes, significantly limiting the effectiveness of existing approaches. Building on this fact, we propose a model for link PRediction in attrIbuted networkS with uncertain Modalities (PRISM), which learns the shared representations across various scenarios of missing modalities through dual-level adversarial training. PRISM comprises four modules, i.e., a GCN extractor, an adversarial extractor, an attentive fusion, and an adaptive aggregator. The GCN extractor leverages graph convolutional networks (GCN) to extract fundamental representations from the network topology. The adversarial extractor employs dual-level adversarial training to acquire the shared representations across various multi-modal scenarios at the node-level and link-level, respectively. The attentive fusion applies the multi-head attention mechanism to integrate the shared representations and the fundamental representations. The adaptive aggregator comprehensively considers both node-level and link-level representations to predict the existence of links. Experimental evaluation using real-world datasets demonstrates that PRISM significantly outperforms existing state-of-the-art link prediction methods for multi-modal attributed graphs under missing modalities by improving the Recall@50 metric (R@50) by up to 38.79%.
{"title":"PRISM: Link Prediction in Attributed Networks With Uncertain Modalities","authors":"Ruohan Yang;Muhammad Asif Ali;Huan Wang;Zhongfei Zhang;Junyang Chen;Di Wang","doi":"10.1109/TKDE.2026.3652729","DOIUrl":"https://doi.org/10.1109/TKDE.2026.3652729","url":null,"abstract":"Link prediction for attributed graphs has garnered significant attention due to its ability to enhance predictive performance by leveraging multi-modal node attributes. However, real-world challenges such as privacy concerns, content restrictions, and attribute constraints often result in nodes facing varying degrees of missing modalities in their attributes, significantly limiting the effectiveness of existing approaches. Building on this fact, we propose a model for link <underline><small><b>PR</b></small></u>ediction in attr<underline><small><b>I</b></small></u>buted network<underline><small><b>S</b></small></u> with uncertain <underline><small><b>M</b></small></u>odalities (<sc>PRISM</small>), which learns the shared representations across various scenarios of missing modalities through dual-level adversarial training. <sc>PRISM</small> comprises four modules, i.e., a GCN extractor, an adversarial extractor, an attentive fusion, and an adaptive aggregator. The GCN extractor leverages graph convolutional networks (GCN) to extract fundamental representations from the network topology. The adversarial extractor employs dual-level adversarial training to acquire the shared representations across various multi-modal scenarios at the node-level and link-level, respectively. The attentive fusion applies the multi-head attention mechanism to integrate the shared representations and the fundamental representations. The adaptive aggregator comprehensively considers both node-level and link-level representations to predict the existence of links. Experimental evaluation using real-world datasets demonstrates that <sc>PRISM</small> significantly outperforms existing state-of-the-art link prediction methods for multi-modal attributed graphs under missing modalities by improving the Recall@50 metric (R@50) by up to 38.79%.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"38 3","pages":"1919-1931"},"PeriodicalIF":10.4,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146162239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-12DOI: 10.1109/TKDE.2026.3651427
Min-Seon Kim;Ling Liu;Hyuk-Yoon Kwon
The dynamic nature of streaming data often introduces distribution shifts that challenge typical text classification models. This paper proposes an online learning framework tailored for streaming text classification under distribution shifts. First, we decompose a neural network-based text classification model into distinct modules and analyze the varying impact of updating these modules under different types of shifts. Based on this insight, we define three novel indicators to efficiently measure the extent of distribution shifts without evaluating the entire model. These indicators enable the development of predictive models that dynamically optimize module update strategies, balancing learning efficiency and accuracy in real-time. To the best of our knowledge, this is the first approach to systematically adapt model updates according to a trade-off between efficiency and accuracy in online text classification. Extensive experiments on real-world streaming datasets demonstrate the effectiveness of our method, which consistently outperforms both static update strategies and state-of-the-art online text classification models.
{"title":"Modular Model Adaptation for Online Learning in Streaming Text Classification","authors":"Min-Seon Kim;Ling Liu;Hyuk-Yoon Kwon","doi":"10.1109/TKDE.2026.3651427","DOIUrl":"https://doi.org/10.1109/TKDE.2026.3651427","url":null,"abstract":"The dynamic nature of streaming data often introduces distribution shifts that challenge typical text classification models. This paper proposes an online learning framework tailored for streaming text classification under distribution shifts. First, we decompose a neural network-based text classification model into distinct modules and analyze the varying impact of updating these modules under different types of shifts. Based on this insight, we define three novel indicators to efficiently measure the extent of distribution shifts without evaluating the entire model. These indicators enable the development of predictive models that dynamically optimize module update strategies, balancing learning efficiency and accuracy in real-time. To the best of our knowledge, this is the first approach to systematically adapt model updates according to a trade-off between efficiency and accuracy in online text classification. Extensive experiments on real-world streaming datasets demonstrate the effectiveness of our method, which consistently outperforms both static update strategies and state-of-the-art online text classification models.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"38 3","pages":"1843-1856"},"PeriodicalIF":10.4,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146162204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-05DOI: 10.1109/TKDE.2026.3650857
Yang Guo;Tianyu Wang;Zizhan Chen;Zili Shao
With the rapid growth of mobile devices and applications, a prodigious number of spatio-temporal data are generated constantly. To process these data for applications like traffic forecasting, existing spatio-temporal systems rely on the move-data-to-computation paradigm. However, this approach incurs significant data movement overhead between hosts and storage devices, particularly when a spatio-temporal query is executed on a non-preferred data layout or when the query has a small result size due to its inherent nature. To address this issue, this work introduces Groundhog, an efficient in-storage computing technique designed specifically for spatio-temporal queries, aimed at reducing unnecessary data movement and computations. Groundhog introduces three key designs for efficient in-storage computing: (i) a self-contained and segment-based storage model, which is lightweight for in-storage computing and enables fine-grained pruning for spatio-temporal queries; (ii) a set of fine-grained techniques to optimize query processing inside storage devices for spatio-temporal queries; and (iii) an in-storage-computing-aware query planner, which offloads spatio-temporal queries in a fine-grained manner using a cost-based approach. We implemented Groundhog on real hardware and demonstrated how to apply fine-grained techniques to accelerate various spatio-temporal queries. Extensive experiments conducted on real-world datasets demonstrate that Groundhog achieves significant performance improvements, with latency reductions of up to 81% for widely used spatio-temporal queries compared to host computing solutions.
{"title":"Groundhog: Accelerating Spatio-Temporal Data Analytics With Fine-Grained In-Storage Processing","authors":"Yang Guo;Tianyu Wang;Zizhan Chen;Zili Shao","doi":"10.1109/TKDE.2026.3650857","DOIUrl":"https://doi.org/10.1109/TKDE.2026.3650857","url":null,"abstract":"With the rapid growth of mobile devices and applications, a prodigious number of spatio-temporal data are generated constantly. To process these data for applications like traffic forecasting, existing spatio-temporal systems rely on the move-data-to-computation paradigm. However, this approach incurs significant data movement overhead between hosts and storage devices, particularly when a spatio-temporal query is executed on a non-preferred data layout or when the query has a small result size due to its inherent nature. To address this issue, this work introduces Groundhog, an efficient in-storage computing technique designed specifically for spatio-temporal queries, aimed at reducing unnecessary data movement and computations. Groundhog introduces three key designs for efficient in-storage computing: (i) a self-contained and segment-based storage model, which is lightweight for in-storage computing and enables fine-grained pruning for spatio-temporal queries; (ii) a set of fine-grained techniques to optimize query processing inside storage devices for spatio-temporal queries; and (iii) an in-storage-computing-aware query planner, which offloads spatio-temporal queries in a fine-grained manner using a cost-based approach. We implemented Groundhog on real hardware and demonstrated how to apply fine-grained techniques to accelerate various spatio-temporal queries. Extensive experiments conducted on real-world datasets demonstrate that Groundhog achieves significant performance improvements, with latency reductions of up to 81% for widely used spatio-temporal queries compared to host computing solutions.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"38 3","pages":"1798-1812"},"PeriodicalIF":10.4,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146162206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wildcard Keyword Searchable Encryption (WKSE) has grown into a ubiquitous tool. It enables clients to search desired files with wildcard expressions. Although promising, previous schemes confront three barriers: (1) An adversary can launch a correlation attack to acquire the similarity between keywords. (2) The WKSE schemes exhibit false positives which can lead to wrong search results. (3) Existing feature extraction strategies limit the flexibility of search expressions. In this paper, we propose a Multi-Character Searchable Encryption scheme (MCSE) that overcomes the aforementioned barriers. To resist correlation attacks, we design the randomize-pad model to encrypt the vector. To eradicate false positives, we apply the vector space model and complete feature extraction strategies so that a feature set uniquely identifies a keyword or expression. To enhance search flexibility, we introduce three distinct feature extraction strategies for keyword expressions, wildcard expressions, and logical expressions, enabling effective multi-character search. These strategies enable indexes to accommodate the search of diverse expressions. Finally, we prove that MCSE is indistinguishable against chosen-feature attacks and implement MCSE on two real datasets. Compared with state-of-the-art schemes, the experiment results show that MCSE achieves good performance.
{"title":"Secure Multi-Character Searchable Encryption Supporting Rich Search Functionalities","authors":"Qing Wang;Donghui Hu;Meng Li;Yan Qiao;Guomin Yang;Mauro Conti","doi":"10.1109/TKDE.2025.3650082","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3650082","url":null,"abstract":"Wildcard Keyword Searchable Encryption (WKSE) has grown into a ubiquitous tool. It enables clients to search desired files with wildcard expressions. Although promising, previous schemes confront three barriers: (1) An adversary can launch a correlation attack to acquire the similarity between keywords. (2) The WKSE schemes exhibit false positives which can lead to wrong search results. (3) Existing feature extraction strategies limit the flexibility of search expressions. In this paper, we propose a Multi-Character Searchable Encryption scheme (MCSE) that overcomes the aforementioned barriers. To resist correlation attacks, we design the randomize-pad model to encrypt the vector. To eradicate false positives, we apply the vector space model and complete feature extraction strategies so that a feature set uniquely identifies a keyword or expression. To enhance search flexibility, we introduce three distinct feature extraction strategies for keyword expressions, wildcard expressions, and logical expressions, enabling effective multi-character search. These strategies enable indexes to accommodate the search of diverse expressions. Finally, we prove that MCSE is indistinguishable against chosen-feature attacks and implement MCSE on two real datasets. Compared with state-of-the-art schemes, the experiment results show that MCSE achieves good performance.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"38 3","pages":"1958-1972"},"PeriodicalIF":10.4,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146162182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1109/TKDE.2025.3650227
Jialiang Li;Hua Lu;Cyrus Shahabi
Trajectory similarity in road networks is pivotal for numerous applications in transportation, urban planning, and ridesharing. However, due to the varying lengths of trajectories, employing similarity metrics directly on raw trajectory data (e.g., DTW (Yi et al., 1998)) becomes impractical at scale. Therefore, current research primarily revolves around applying deep learning to embed trajectories into vector representations, i.e., embeddings, enabling the application of simpler (and indexable) similarity metrics such as Euclidean distance. Existing research either involves embedding trajectories independent of the downstream tasks, or tailors the embedding specifically for a designated similarity metric. While the former offers versatility and allows for easy fine-tuning to accommodate various metrics, the latter typically yields more effective results but necessitates reconfiguration for different, yet similar metrics. Moreover, both approaches neglect the intrinsic spatiotemporal continuity in trajectory data, resulting in suboptimal trajectory modeling. Our objective is to address the limitations in modeling and have the best of the two worlds. Initially, we generate an embedding through pre-training, decoupled from any particular similarity metric. Subsequently, through a meticulous yet less complex fine-tuning process, we enhance the embedding to encapsulate the nuances of a designated similarity metric. Moreover, a significant aspect of our approach lies in our trajectory modeling that captures spatiotemporal continuity, which mainly consists of a trajectory-oriented road segment embedding and a Transformer encoder enhanced by spatiotemporal semantics inherent in road network-constrained trajectories. Our experimental results demonstrate the superiority of our approach in approximating multiple trajectory similarity metrics over existing state-of-the-art models from both categories of approaches.
道路网络的轨迹相似性对于交通、城市规划和拼车的众多应用至关重要。然而，由于轨迹的长度不同，直接在原始轨迹数据（例如，DTW (Yi et al., 1998)）上使用相似性度量在规模上变得不切实际。因此，目前的研究主要围绕着应用深度学习将轨迹嵌入到向量表示中，即嵌入，从而能够应用更简单（且可索引）的相似性度量，如欧几里得距离。现有的研究要么涉及独立于下游任务的嵌入轨迹，要么专门针对指定的相似性度量来定制嵌入。前者提供了多功能性，并允许轻松微调以适应各种指标，而后者通常会产生更有效的结果，但需要针对不同但相似的指标进行重新配置。此外，这两种方法都忽略了轨迹数据固有的时空连续性，导致轨迹建模不够理想。我们的目标是解决建模中的局限性，并获得两者的最佳效果。首先，我们通过预训练生成嵌入，从任何特定的相似度度量解耦。随后，通过细致但不太复杂的微调过程，我们增强了嵌入，以封装指定相似度度量的细微差别。此外，我们的方法的一个重要方面在于我们的轨迹建模，捕捉时空连续性，主要包括一个面向轨迹的道路段嵌入和一个由路网约束轨迹中固有的时空语义增强的Transformer编码器。我们的实验结果表明，我们的方法在近似多轨迹相似度量方面优于现有的两类方法的最先进模型。
{"title":"STORM: Exploiting Spatiotemporal Continuity for Trajectory Similarity Learning in Road Networks","authors":"Jialiang Li;Hua Lu;Cyrus Shahabi","doi":"10.1109/TKDE.2025.3650227","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3650227","url":null,"abstract":"Trajectory similarity in road networks is pivotal for numerous applications in transportation, urban planning, and ridesharing. However, due to the varying lengths of trajectories, employing similarity metrics directly on raw trajectory data (e.g., DTW (Yi et al., 1998)) becomes impractical at scale. Therefore, current research primarily revolves around applying deep learning to embed trajectories into vector representations, i.e., embeddings, enabling the application of simpler (and indexable) similarity metrics such as Euclidean distance. Existing research either involves embedding trajectories independent of the downstream tasks, or tailors the embedding specifically for a designated similarity metric. While the former offers versatility and allows for easy fine-tuning to accommodate various metrics, the latter typically yields more effective results but necessitates reconfiguration for different, yet similar metrics. Moreover, both approaches neglect the intrinsic spatiotemporal continuity in trajectory data, resulting in suboptimal trajectory modeling. Our objective is to address the limitations in modeling and have the best of the two worlds. Initially, we generate an embedding through pre-training, decoupled from any particular similarity metric. Subsequently, through a meticulous yet less complex fine-tuning process, we enhance the embedding to encapsulate the nuances of a designated similarity metric. Moreover, a significant aspect of our approach lies in our trajectory modeling that captures spatiotemporal continuity, which mainly consists of a trajectory-oriented road segment embedding and a Transformer encoder enhanced by spatiotemporal semantics inherent in road network-constrained trajectories. Our experimental results demonstrate the superiority of our approach in approximating multiple trajectory similarity metrics over existing state-of-the-art models from both categories of approaches.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"38 3","pages":"1986-2000"},"PeriodicalIF":10.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146162200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-31DOI: 10.1109/TKDE.2025.3649808
Yu Zhou;Feng Wang;Yongning Tang
Order-preserving pattern matching (OPPM) is a specialized area within the domain of pattern recognition and string matching. This specialized area is dedicated to identifying patterns in sequences where the intrinsic order of elements is crucially important. This comprehensive review provides an in-depth analysis of diverse order-preserving pattern matching techniques, focusing on their algorithms and methodologies. Particular attention is paid to the challenges researchers face in preserving order during pattern matching. The review also evaluates the performance and scalability of various techniques to handle large-scale datasets. By discussing the current state of OPPM research, we identify gaps, opportunities, and potential avenues for future exploration. Through this exploration, we aim to contribute valuable insights that will guide researchers and practitioners in advancing the frontiers of OPPM research, shaping the trajectory of this field in the coming years.
{"title":"Order-Preserving Pattern Matching: Review","authors":"Yu Zhou;Feng Wang;Yongning Tang","doi":"10.1109/TKDE.2025.3649808","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3649808","url":null,"abstract":"Order-preserving pattern matching (OPPM) is a specialized area within the domain of pattern recognition and string matching. This specialized area is dedicated to identifying patterns in sequences where the intrinsic order of elements is crucially important. This comprehensive review provides an in-depth analysis of diverse order-preserving pattern matching techniques, focusing on their algorithms and methodologies. Particular attention is paid to the challenges researchers face in preserving order during pattern matching. The review also evaluates the performance and scalability of various techniques to handle large-scale datasets. By discussing the current state of OPPM research, we identify gaps, opportunities, and potential avenues for future exploration. Through this exploration, we aim to contribute valuable insights that will guide researchers and practitioners in advancing the frontiers of OPPM research, shaping the trajectory of this field in the coming years.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"38 3","pages":"1885-1904"},"PeriodicalIF":10.4,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146162217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Semi-supervised learning (SSL) problems are challenging, appear in many domains, and are particularly relevant to streaming applications, where data are abundant but labels are not. The problem tackled here is classification over an evolving data stream where labels are rare and distributed randomly. We propose SLEADE (Stream LEArning by Disagreement Ensemble), a novel method that exploits disagreement-based learning and unsupervised drift detection to leverage unlabeled data during training. SLEADE uses pseudo-labeled instances to augment the training set of each member of an ensemble using a majority trains the minority scheme. The pseudo-labeled data impact is controlled by a weighting function that considers the confidence in the prediction attributed by the ensemble members. SLEADE exploits unsupervised drift detection, which allows the ensemble to respond to changes. We present several experiments using real and synthetic data to illustrate the benefits and limitations of SLEADE compared to existing algorithms.
半监督学习（SSL）问题具有挑战性，出现在许多领域，并且与流媒体应用程序特别相关，其中数据丰富但标签不足。这里要解决的问题是对不断发展的数据流进行分类，其中标签很少且随机分布。我们提出了SLEADE (Stream LEArning by disagree Ensemble)，这是一种利用基于分歧的学习和无监督漂移检测来利用训练过程中未标记数据的新方法。SLEADE使用伪标记实例来增强集合中每个成员的训练集，使用多数训练少数方案。伪标记数据影响由一个加权函数控制，该函数考虑了集成成员对预测的置信度。SLEADE利用无监督漂移检测，允许集成响应变化。我们提出了几个使用真实和合成数据的实验，以说明与现有算法相比，SLEADE的优点和局限性。
{"title":"SLEADE: Disagreement-Based Semi-Supervised Learning for Sparsely Labeled Evolving Data Streams","authors":"Heitor Murilo Gomes;Jesse Read;Maciej Grzenda;Bernhard Pfahringer;Albert Bifet","doi":"10.1109/TKDE.2025.3647050","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3647050","url":null,"abstract":"Semi-supervised learning (SSL) problems are challenging, appear in many domains, and are particularly relevant to streaming applications, where data are abundant but labels are not. The problem tackled here is classification over an evolving data stream where labels are rare and distributed randomly. We propose SLEADE (Stream LEArning by Disagreement Ensemble), a novel method that exploits disagreement-based learning and unsupervised drift detection to leverage unlabeled data during training. SLEADE uses pseudo-labeled instances to augment the training set of each member of an ensemble using a <italic>majority trains the minority</i> scheme. The pseudo-labeled data impact is controlled by a weighting function that considers the confidence in the prediction attributed by the ensemble members. SLEADE exploits unsupervised drift detection, which allows the ensemble to respond to changes. We present several experiments using real and synthetic data to illustrate the benefits and limitations of SLEADE compared to existing algorithms.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"38 3","pages":"1973-1985"},"PeriodicalIF":10.4,"publicationDate":"2025-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146162223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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