IEEE Transactions on Big Data最新文献_第9页

Effective and Efficient Conductance-Based Community Search at Billion Scale 十亿规模下基于电导的有效社区搜索

IF 8.1 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2025-12-01 Epub Date: 2025-07-10 DOI: 10.1109/TBDATA.2025.3588028

Longlong Lin;Yue He;Wei Chen;Pingpeng Yuan;Rong-Hua Li;Tao Jia

{"title":"Effective and Efficient Conductance-Based Community Search at Billion Scale","authors":"Longlong Lin;Yue He;Wei Chen;Pingpeng Yuan;Rong-Hua Li;Tao Jia","doi":"10.1109/TBDATA.2025.3588028","DOIUrl":"https://doi.org/10.1109/TBDATA.2025.3588028","url":null,"abstract":"Community search is a widely studied semi-supervised graph clustering problem, retrieving a high-quality connected subgraph containing the user-specified query vertex. However, existing methods primarily focus on cohesiveness within the community but ignore the sparsity outside the community, obtaining sub-par results. Inspired by this, we adopt the well-known <italic>conductance metric to measure the quality of a community and introduce a novel problem of <italic>conductance-based community search (<italic>CCS). <italic>CCS aims at finding a subgraph with the smallest <italic>conductance among all connected subgraphs that contain the query vertex. We prove that the <italic>CCS problem is NP-hard. To efficiently query <italic>CCS, a four-stage <italic>subgraph-conductance-based community search algorithm, <italic>SCCS, is proposed. Specifically, we first greatly reduce the entire graph using local sampling techniques. Then, a three-stage local optimization strategy is employed to continuously refine the community quality. Namely, we first utilize a seeding strategy to obtain an initial community to enhance its internal cohesiveness. Then, we iteratively add qualified vertices in the expansion stage to guarantee the internal cohesiveness and external sparsity of the community. Finally, we gradually remove unqualified vertices during the verification stage. Extensive experiments on real-world datasets containing one billion-scale graph and synthetic datasets show the effectiveness, efficiency, and scalability of our solutions.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 6","pages":"3170-3184"},"PeriodicalIF":8.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148480220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DAG-EnseFL: DAG-Based Asynchronous Federated Learning With Ensemble Distillation DAG-EnseFL：基于dag的集成蒸馏异步联邦学习

IF 8.1 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2025-12-01 Epub Date: 2025-07-30 DOI: 10.1109/TBDATA.2025.3594244

Jiewei Chen;Da Wu;Shaoyong Guo;Feng Qi;Xuesong Qiu

{"title":"DAG-EnseFL: DAG-Based Asynchronous Federated Learning With Ensemble Distillation","authors":"Jiewei Chen;Da Wu;Shaoyong Guo;Feng Qi;Xuesong Qiu","doi":"10.1109/TBDATA.2025.3594244","DOIUrl":"https://doi.org/10.1109/TBDATA.2025.3594244","url":null,"abstract":"In the industrial Internet of Things (IIoT), blockchain technology has been employed to ensure the trustworthiness of federated learning (FL) services. However, the existing framework that combines blockchain and FL suffers from poor training performance and high computational overhead due to the complex consensus mechanism. Although recent studies have explored architectures that integrate Directed Acyclic Graph (DAG) with FL, the aggregation process in DAG-based multi-branch structures still faces significant challenges due to strong statistical heterogeneity across branches. To accommodate the heterogeneity, this paper proposes a DAG-based asynchronous aggregation framework for decentralized FL services. In this framework, the local models are aggregated with global models in the DAG ledger to form a new transaction block (TB). The verified TB becomes the subsequent node of the tail node in the DAG multi-branch structure. Additionally, an FL model delivery mechanism based on improved ensemble distillation is designed. This mechanism merges the models in verified TBs from multiple branches of the DAG, enhancing the accuracy of the final delivery model without compromising system training efficiency. Extensive ablation and comparative experiments demonstrate that our proposed scheme enhances the training efficiency and accuracy of DAG-FL systems while ensuring the security and trustworthiness.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 6","pages":"3342-3355"},"PeriodicalIF":8.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148480772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Pluggable Common Sense-Enhanced Framework for Knowledge Graph Completion 知识图谱补全的可插拔常识增强框架

IF 8.1 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2025-12-01 Epub Date: 2025-07-11 DOI: 10.1109/TBDATA.2025.3588081

Guanglin Niu;Bo Li;Siling Feng

{"title":"A Pluggable Common Sense-Enhanced Framework for Knowledge Graph Completion","authors":"Guanglin Niu;Bo Li;Siling Feng","doi":"10.1109/TBDATA.2025.3588081","DOIUrl":"https://doi.org/10.1109/TBDATA.2025.3588081","url":null,"abstract":"Knowledge graph completion (KGC) tasks aim to infer missing facts in a knowledge graph (KG) for many knowledge-intensive applications. However, existing embedding-based KGC approaches primarily rely on factual triples, potentially leading to outcomes inconsistent with common sense. Besides, generating explicit common sense is often impractical or costly for a KG. To address these challenges, we propose a pluggable common sense-enhanced KGC framework that incorporates both fact and common sense for KGC. This framework is adaptable to different KGs based on their entity concept richness and has the capability to automatically generate explicit or implicit common sense from factual triples. Furthermore, we introduce common sense-guided negative sampling and a coarse-to-fine inference approach for KGs with rich entity concepts. For KGs without concepts, we propose a dual scoring scheme involving a relation-aware concept embedding mechanism. Importantly, our approach can be integrated as a pluggable module for many knowledge graph embedding (KGE) models, facilitating joint common sense and fact-driven training and inference. The experiments illustrate that our framework exhibits good scalability and outperforms existing models across various KGC tasks.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 6","pages":"3282-3299"},"PeriodicalIF":8.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148479349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

PatchAD: A Lightweight Patch-Based MLP-Mixer for Time Series Anomaly Detection PatchAD：用于时间序列异常检测的轻量级基于补丁的mlp混频器

IF 8.1 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2025-12-01 Epub Date: 2025-08-07 DOI: 10.1109/TBDATA.2025.3596745

Zhijie Zhong;Zhiwen Yu;Yiyuan Yang;Weizheng Wang;Kaixiang Yang;C. L. Philip Chen

{"title":"PatchAD: A Lightweight Patch-Based MLP-Mixer for Time Series Anomaly Detection","authors":"Zhijie Zhong;Zhiwen Yu;Yiyuan Yang;Weizheng Wang;Kaixiang Yang;C. L. Philip Chen","doi":"10.1109/TBDATA.2025.3596745","DOIUrl":"https://doi.org/10.1109/TBDATA.2025.3596745","url":null,"abstract":"Time series anomaly detection is a pivotal task in data analysis, yet it poses the challenge of discerning normal and abnormal patterns in label-deficient scenarios. While prior studies have largely employed reconstruction-based approaches, which limit the models’ representational capacities. Moreover, existing deep learning-based methods are not sufficiently lightweight. Addressing these issues, we present PatchAD, our novel, highly efficient multiscale patch-based MLP-Mixer architecture that utilizes contrastive learning for representation extraction and anomaly detection. With its four distinct MLP Mixers and innovative dual project constraint module, PatchAD mitigates potential model degradation and offers a lightweight solution, requiring only <bold>0.403 M parameters. Its efficacy is demonstrated by state-of-the-art results across <bold>8 datasets sourced from different application scenarios, outperforming over <bold>30 comparative algorithms. PatchAD significantly improves the classical F1 score by <bold>6.84%, the Aff-F1 score by <bold>4.27%, and the V-ROC by <bold>2.49%. Simultaneously, an in-depth analysis of the mechanisms underlying PatchAD has been conducted from both theoretical and experimental perspectives, validating the design motivations of the model.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 6","pages":"3460-3473"},"PeriodicalIF":8.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148481124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Global Distance Estimation Hashing for Image Retrieval 用于图像检索的深度全局距离估计哈希

IF 8.1 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2025-12-01 Epub Date: 2025-03-02 DOI: 10.1109/TBDATA.2025.3566532

Mengfei Xu;Bowen Luo;Jianfeng Xu;Feng Ding

{"title":"Deep Global Distance Estimation Hashing for Image Retrieval","authors":"Mengfei Xu;Bowen Luo;Jianfeng Xu;Feng Ding","doi":"10.1109/TBDATA.2025.3566532","DOIUrl":"https://doi.org/10.1109/TBDATA.2025.3566532","url":null,"abstract":"Hashing technology has an excellent high dimensional visual information encoding ability. It can map distance information from high dimensional to low dimensional Hamming space, which is widely used in large-scale image nearest neighbor search tasks. Most recent studies, such as pair-wise and triplet-wise, use local positional relations to construct hashing functions. These learning methods, which are only based on local information, will lead to an incoherent feature representation, especially for semantic similarity features. Therefore, we propose a global distance estimation hashing (DEH). The DEH establishes a deep hashing model by constructing global distance relations that can define a distance between every paired category. Thus the distance relations constructed by DEH include much more detailed information. In other words, DEH focuses on global distance relations and makes the division of boundaries have excellent performance, especially for those samples with subtle changes. After the mapping, the model quantization approach effectively decreases the quantization loss caused by transitioning from the real-valued space to the Hamming space. Numerous experiments show that our DEH achieves excellent results on CIFAR-10, NUS-WIDE and ImageNet. These achievements not only validate the effectiveness of DEH in general image hashing retrieval tasks but also demonstrate its significant advantages in handling the large-scale multimedia retrieval systems.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 6","pages":"2896-2913"},"PeriodicalIF":8.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148479606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Document-Image Perceptual Hashing for Content Authentication 文档-图像感知哈希内容认证

IF 8.1 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2025-12-01 Epub Date: 2025-08-07 DOI: 10.1109/TBDATA.2025.3596854

Xiaotong Situ;Tong Liu;Heng Yao;Chuan Qin;Xinpeng Zhang

{"title":"Document-Image Perceptual Hashing for Content Authentication","authors":"Xiaotong Situ;Tong Liu;Heng Yao;Chuan Qin;Xinpeng Zhang","doi":"10.1109/TBDATA.2025.3596854","DOIUrl":"https://doi.org/10.1109/TBDATA.2025.3596854","url":null,"abstract":"This paper proposes an end-to-end two-branch network for a document image perceptual hashing scheme, where the two branches focus on image visual features and text features, respectively. Existing perceptual hashing schemes cannot solve the problem of the tiny proportion of text tampering detection, while simple text detection is unable to solve the problem of background region-aware matching. To address these issues, we extract text information via optical character recognition (OCR) and then generate the text features using the bidirectional encoder representations from Transformers (BERT). Visual features of the image are extracted from the local and global features of the image using ResNet and Vision Transformer cascades, and then fused to generate the final hash sequence through the fully connected layer. The proposed network considers both image visual features and textual information to verify that the document image has not been tampered with. In our network, the OCR module enables accurate and intelligent text detection and recognition, particularly for dealing with text tampering that has only been conducted in tiny portions. It also provides more efficient and robust text recognition services. Experimental results show that the proposed hashing scheme is robust and discriminative in document images.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 6","pages":"3474-3487"},"PeriodicalIF":8.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148481129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-View Spectral Clustering on the Grassmannian Manifold With Hypergraph Representation 超图表示的格拉斯曼流形上的多视点光谱聚类

IF 8.1 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2025-12-01 Epub Date: 2025-07-10 DOI: 10.1109/TBDATA.2025.3588078

Murong Yang;Shihui Ying;Xin-Jian Xu;Yue Gao

{"title":"Multi-View Spectral Clustering on the Grassmannian Manifold With Hypergraph Representation","authors":"Murong Yang;Shihui Ying;Xin-Jian Xu;Yue Gao","doi":"10.1109/TBDATA.2025.3588078","DOIUrl":"https://doi.org/10.1109/TBDATA.2025.3588078","url":null,"abstract":"Graph-based multi-view spectral clustering methods have achieved notable progress recently, yet they often fall short in either oversimplifying pairwise relationships or struggling with inefficient spectral decompositions in high-dimensional euclidean spaces. In this paper, we introduce a novel approach that begins to generate hypergraphs by leveraging sparse representation learning from data points. Based on the generated hypergraph, we propose an optimization function with orthogonality constraints for multi-view hypergraph spectral clustering, which incorporates spectral clustering for each view and ensures consistency across different views. In euclidean space, solving the orthogonality-constrained optimization problem may yield local maxima and approximation errors. Innovately, we transform this problem into an unconstrained form on the Grassmannian manifold. Finally, we devise an alternating iterative Riemannian optimization algorithm to solve the problem. To validate the effectiveness of the proposed algorithm, we test it on four real-world multi-view datasets and compare its performance with six state-of-the-art multi-view clustering algorithms. The experimental results demonstrate that our method outperforms the baselines in terms of clustering performance due to its superior low-dimensional and resilient feature representation.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 6","pages":"3185-3196"},"PeriodicalIF":8.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148479756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prediction of Multivariate Spatial-Temporal Series Data Based on Adaptive Spatial-Temporal Information 基于自适应时空信息的多元时空序列数据预测

IF 8.1 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2025-12-01 Epub Date: 2025-07-10 DOI: 10.1109/TBDATA.2025.3588086

Chen An;Zibao Lu;Yuru Ma;Weiwei Zhao;Xinyi Chen

{"title":"Prediction of Multivariate Spatial-Temporal Series Data Based on Adaptive Spatial-Temporal Information","authors":"Chen An;Zibao Lu;Yuru Ma;Weiwei Zhao;Xinyi Chen","doi":"10.1109/TBDATA.2025.3588086","DOIUrl":"https://doi.org/10.1109/TBDATA.2025.3588086","url":null,"abstract":"This paper proposes an Prediction of Multivariate Spatial-Temporal Series Data Based on Adaptive Spatial-Temporal Information(ASTCN), which learns complex spatio-temporal information from multivariate spatio-temporal series through trainable temporal embeddings and graph adjacency matrices. A gated fusion mechanism is employed to control the proportion of different temporal embeddings to improve prediction accuracy. Visualization of the temporal embeddings reveals that weekly temporal embeddings have the greatest impact on prediction accuracy, followed by daily temporal embeddings, while monthly temporal embeddings have the least impact. Additionally, a novel method for constructing graph adjacency matrices is introduced. Ablation experiments demonstrate that the two types of graph adjacency matrices proposed in this method have varying degrees of influence on improving the prediction accuracy of the dataset. Consequently, this paper integrates the two graph adjacency matrices, enabling ASTCN to achieve superior prediction accuracy on traffic speed, traffic flow, and air quality datasets compared to when either matrix is used alone. In comparative experiments, ASTCN ultimately achieves excellent prediction performance with relatively low training costs.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 6","pages":"3212-3224"},"PeriodicalIF":8.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148480822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Collaborative Framework for circRNA-Disease Associations Prediction Using Dual Variational Graph 基于双变分图的circrna -疾病关联预测协同框架

IF 8.1 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2025-12-01 Epub Date: 2025-03-02 DOI: 10.1109/TBDATA.2025.3566581

Changchun Liu;Lei Wang;Bowei Zhao;Mengmeng Wei;Yang Li;Mianshuo Lu;Yu Wei;Sizhe Liang

{"title":"Collaborative Framework for circRNA-Disease Associations Prediction Using Dual Variational Graph","authors":"Changchun Liu;Lei Wang;Bowei Zhao;Mengmeng Wei;Yang Li;Mianshuo Lu;Yu Wei;Sizhe Liang","doi":"10.1109/TBDATA.2025.3566581","DOIUrl":"https://doi.org/10.1109/TBDATA.2025.3566581","url":null,"abstract":"Many experiments have shown that circular RNA (circRNA) can act as biomarkers for complex diseases and play significant regulatory roles in multiple pathological processes. However, most circRNA-disease associations remain unknown, and discovering these associations through biological experimental approach is expensive and time-consuming. Taking into account the shortcomings of current methods, we introduce a new collaborative framework that utilizes multi-heterogeneous graphs, along with variational graph auto-encoders (VGAE) to predict associations between circRNA and diseases. First, we build multi-similarity networks using various biological attributes of circRNA and diseases, and integrate these similarity networks. Two subnetworks are constructed from association matrix and combined similarity network, which included a circRNA-based network and a disease-based network. We then employ random walk with restart and Singular Value Decomposition, for feature extraction from the similarity matrix. Finally, we use collaborative framework to predict the circRNA-disease association scores based on the two subnetworks. We integrate the two score matrices to obtain a final prediction scoring matrix. Using 5-fold cross-validation on the CircR2Disease dataset, our model achieved an AUC score of 0.9828 and an AUPR score of 0.9820. Additionally, among the top 30 highest-scoring circRNA-disease association pairs, 26 associations have already been validated. Our model shows strong performance and can accurately predict associations between circRNA and diseases, according to experimental results.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 6","pages":"2884-2895"},"PeriodicalIF":8.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148480885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

FIFA: A Forest-Based Sliding Window Aggregation Scheme for Out-of-Order Data Streams FIFA：一种基于森林的无序数据流滑动窗口聚合方案

IF 8.1 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2025-12-01 Epub Date: 2025-04-08 DOI: 10.1109/TBDATA.2025.3558844

Jiande Huang;Yuhui Deng;Jianjun Li;Lijuan Lu;Qifen Yang;Geyong Min

{"title":"FIFA: A Forest-Based Sliding Window Aggregation Scheme for Out-of-Order Data Streams","authors":"Jiande Huang;Yuhui Deng;Jianjun Li;Lijuan Lu;Qifen Yang;Geyong Min","doi":"10.1109/TBDATA.2025.3558844","DOIUrl":"https://doi.org/10.1109/TBDATA.2025.3558844","url":null,"abstract":"Sliding window aggregation is a core operation in data stream analysis that extracts summaries from the most recent data stream. An evict or insert of the window can be handled in <inline-formula><tex-math>$O(1)$</tex-math></inline-formula> for in-order data streams. However, real-world data streams are typically disordered due to network delays. To process out-of-order data streams, existing methods primarily use a tree to maintain the sliding windows. Since the complexity of the tree is related to the window size, the performance of these methods will drop sharply or become unavailable when facing a big window. To overcome the limitation of existing methods, this paper presents Finger B-Trees Forest Aggregation (FIFA). This novel forest-based sliding window aggregation scheme optimally handles out-of-order data streams. At its heart, FIFA uses aggregation forest to extend Finger B-Trees. Specifically, FIFA evenly divides a window into several chunks and constructs a separate tree to maintain each chunk. When an out-of-order item arrives, FIFA first locates the corresponding chunk of the item and then uses Finger B-Trees to insert it into the window efficiently. Chunking reduces the complexity of the tree and the coupling of aggregation results by isolating items within windows. Thus, an insert or evict takes amortized <inline-formula><tex-math>$O(c)$</tex-math></inline-formula> in the worst case, where <inline-formula><tex-math>$c$</tex-math></inline-formula> is the size of each chunk. Finally, extensive experiments based on real-world data demonstrate that FIFA achieves an average 2-fold throughput improvement on out-of-order data streams compared with the state-of-the-art (SOTA) aggregation schemes.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 6","pages":"2827-2840"},"PeriodicalIF":8.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148479038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0