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Gecko: Efficient Sliding Window Aggregation With Granular-Based Bulk Eviction Over Big Data Streams

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2025年第2期37卷 698-709页

作者： Li, Jianjun Deng, Yuhui Huang, Jiande Yi, Zhou Yang, Qifen Min, Geyong Jinan Univ Dept Comp Sci Guangzhou 510632 Peoples R China Columbus State Univ TSYS Sch Comp Sci Columbus GA 31907 USA Univ Exeter Coll Engn Math & Phys Sci Dept Comp Sci Exeter EX4 4QF England

Sliding window aggregation, which extracts summaries from data streams, is a core operation in streaming analysis. Though existing sliding window algorithms that perform single eviction and insertion operations can achieve a worst-case time complexity of O(1) for in-order streams, real-world data streams often involve out-of-order data and exhibit burst data characteristics, which pose performance challenges to these sliding window algorithms. To address this challenging issue, we propose Gecko - a novel sliding window aggregation algorithm that supports bulk eviction. Gecko leverages a granular-based eviction strategy for various bulk sizes, enabling efficient bulk eviction while maintaining the performance close to that of in-order stream algorithms for single evictions. For large data bulks, Gecko performs coarse-grained eviction at the chunk level, followed by fine-grained eviction using leftward binary tree aggregation (LTA) as a complementary method. Moreover, Gecko partitions data based on chunks to prevent the impacts of out-of-order data on other chunks, thereby enabling efficient handling of out-of-order data streams. We conduct extensive experiments to evaluate the performance of Gecko. Experimental results demonstrate that Gecko exhibits superior performance over other solutions, which is consistent with theoretical expectations. In real-world data scenarios, Gecko improves the average throughput of the state-of-the-art algorithm b_FiBA by 1.7 times, with a maximum improvement of up to 3.5 times. Gecko also demonstrates the best latency performance among all compared schemes.

关键词： Streams Windows Out of order Binary trees Throughput Aggregates partitioning algorithms Time complexity Indexes Computer science Bulk eviction incremental computation out-of-order data streams sliding window aggregation stream algorithm

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Adaptive Task Assignment in Spatial Crowdsourcing: A Human-in-The-Loop Approach

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2025年第4期24卷 2726-2739页

作者： Wu, Qingshun Li, Yafei Yan, Jinxing Zhang, Mei Xu, Jianliang Xu, Mingliang Zhengzhou Univ Sch Comp & Artificial Intelligence Zhengzhou 450001 Peoples R China Hong Kong Baptist Univ Dept Comp Sci Hong Kong Peoples R China

In recent years, adaptive task assignment has been explored in spatial crowdsourcing. The challenge lies in how to adaptively partition the task stream to achieve the best utility for task assignment. A number of existing works have attempted to solve this challenge and achieve better performance by utilizing learning-based methods. Specifically, they mainly employ reinforcement learning to divide the task stream into a series of suitable batches and then perform task assignment in a batch fashion. Drawing inspiration from the effectiveness of human-machine collaborative decision-making, we aim to investigate human-in-the-loop methods to further enhance the performance of adaptive task assignment. In this paper, we propose a novel framework called Human-in-the-Loop Adaptive Partition (HLAP), which consists of two primary modules: Reinforcement Learning Partition Decision (RL-PD) and Human Supervision and Guidance (HSG). In the RL-PD module, we develop an RL agent, referred to as the decision-maker, by integrating the dual attention network into the Deep Q-Network (DQN) algorithm to capture cross-dimensional contextual information and long-range dependencies for a better understanding of the environment. In the HSG module, we design a human-in-the-loop mechanism to optimize the performance of the decision-maker, focusing on addressing two key issues: when and how humans interact with the decision-maker. Furthermore, to alleviate the heavy workload on humans, we construct a supervisor based on RL to oversee the decision-maker's partition process and adaptively determine when human intervention is necessary. We conduct extensive experiments on two real-world datasets, and the results demonstrate the efficiency and effectiveness of the HLAP framework.

关键词： Human in the loop Crowdsourcing Roads Reinforcement learning Mobile computing Learning systems partitioning algorithms Costs Symbols Real-time systems Human-in-the-loop location-based services reinforcement learning spatial crowdsourcing task assignment

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Field-Based Security Testing of SDN Configuration Updates

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IEEE TRANSACTIONS ON RELIABILITY 2025年

作者： Malik, Jahanzaib Pastore, Fabrizio Univ Luxembourg SnT Ctr L-4365 Esch Sur Alzette Luxembourg

Software-defined systems revolutionized the management of hardware devices but introduced quality assurance challenges that remain to be tackled. For example, software defined networks (SDNs) became a key technology for the prompt reconfigurations of network services in many sectors including telecommunications, data centers, financial services, cloud providers, and manufacturing industry. Unfortunately, reconfigurations may lead to mistakes that compromise the dependability of the provided services. In this article, we focus on the reconfigurations of network services in the satellite communication sector, and target security requirements, which are often hard to verify;for example, although connectivity may function properly, confidentiality may be broken by packets forwarded to a wrong destination. We propose an approach for FIeld-based Security Testing of SDN Configurations Updates (FISTS). First, it probes the network before and after configuration updates. Then, using the collected data, it relies on unsupervised machine learning algorithms to prioritize the inspection of suspicious node responses, after identifying the network nodes that likely match across the two configurations. Our empirical evaluation has been conducted with network data from simulated and real SDN configuration updates for our industry partner, a world-leading satellite operator. Our results show that, when combined with K-Nearest Neighbor, FISTS leads to best results (up to 0.95 precision and 1.00 recall). Further, we demonstrated its scalability.

关键词： Clustering algorithms Testing Security Software Nearest neighbor methods Anomaly detection partitioning algorithms Inspection Software algorithms Machine learning algorithms Field-based testing software defined network (SDN) security testing upgrades testing

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Handling Multiobjective Optimization Problems With Complex Constraints: A Constraints Grouping-Based Approach

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2025年第6期55卷 3866-3880页

作者： Xu, Biao Zheng, Yiwu Li, Wenji Gao, Xiaozhi Gong, Dunwei He, Jie Fan, Zhun Shantou Univ Dept Elect & Informat Engn Shantou 515063 Peoples R China Wuzhou Univ Guangxi Key Lab Machine Vis & Intelligent Control Wuzhou 543002 Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130012 Peoples R China Univ Eastern Finland Dept Comp Sci Joensuu 80101 Finland Qingdao Univ Sci & Technol Coll Automat & Elect Engn Qingdao 266100 Peoples R China Univ Elect Sci & Technol China Shenzhen Inst Adv Study Shenzhen 518110 Peoples R China

Real-world production scenarios often involve multiobjective optimization problems with intricate constraints. Although there has been a growing interest in multiobjective problems with complex constraints, such as the vehicle routing problem with time windows, existing multiobjective evolutionary optimization techniques still face significant challenges, particularly when addressing the fragmented and narrow feasible regions that arise from these constraints. Our research introduces a refined framework tailored for complex constrained multiobjective evolutionary optimization. The methodology conducts an initial strong-weak analysis to categorize constraints and merges each strong constraint with all weak constraints to form subsets. Each subset, combined with the original objective functions, defines a subproblem. Independent optimization of the original problem and subproblems is carried out by utilizing multiple populations. Information acquired from the subproblems' populations is transferred into the population of the original issue, thereby expediting the detection of the feasible region and simplifying the resolution of the original problem. The efficacy of our innovative algorithm, when benchmarked against traditional constrained multiobjective evolutionary algorithms across 72 test functions, has demonstrated superior convergence, diversity, and competitiveness.

关键词： Optimization Linear programming Search problems Production partitioning algorithms Pareto optimization Intelligent control Fans Convergence Vectors Co-evolutionary complex constraints constraints grouping multiobjective optimization

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New Evaluation Method for Fuzzy Cluster Validity Indices

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IEEE ACCESS 2025年 13卷 22728-22744页

作者： Perez-Sanchez, Ismay Medina-Perez, Miguel Angel Monroy, Raul Loyola-Gonzalez, Octavio Gutierrez-Rodriguez, Andres Eduardo Tecnol Monterrey Sch Engn & Sci Atizapan De Zaragoza 52926 Estado De Mexic Mexico Kayak Analyt Dubai U Arab Emirates NTT DATA Madrid 28050 Spain Tecnol Monterrey Inst Future Educ Monterrey 64849 Mexico MAHLE Shared Serv Monterrey 64650 Mexico

Cluster analysis is the process of associating data objects with classes of similar objects. One key aspect of the clustering problem is to determine the optimal number of groups on a data set to effectively partition it. The process of establishing accurate indicators is called cluster validity, and it evaluates the quality of a clustering, including the optimal number of clusters. A cluster validity index (CVI) is a function that carries out that evaluation based on a series of features like intra-class compactness, inter-class separation, object density, and membership degree of objects to each cluster, among others. Best-c validation protocol has been used to evaluate CVIs previously, but it makes a strong assumption about the correctness of the clustering performed. We propose a new protocol based on mapping the internal CVI values to an external CVI and performing statistical tests to analyze the results. The experiments are conducted evaluating the partitions generated by the fuzzy c-means clustering algorithm over 84 UCI datasets with 31 fuzzy CVIs. Experimental results evidenced the need for bigger experimental setups compared to previous studies. Also, they show that among the CVIs involved, there is none with a significant statistical difference over the other given by a Friedman test. However, the Wilcoxon signed-rank test demonstrates that in 1 vs. 1, the CVI KPBM outperforms the others in most of the fuzzifiers (m). Just except for m = 2.0 which consolidates the results in previous studies where none of the CVIs were able to improve consistently among the selected group of CVIs.

关键词： Clustering algorithms partitioning algorithms Indexes Protocols Prediction algorithms Hands Weather forecasting Technological innovation Organizations Object recognition Fuzzy cluster validation index evaluation method fuzzy clustering fuzzy sets

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FWCEC: An Enhanced Feature Weighting Method via Causal Effect for Clustering

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2025年第2期37卷 685-697页

作者： Cao, Fuyuan Jing, Xuechun Yu, Kui Liang, Jiye Shanxi Univ Sch Comp & Informat Technol Key Lab Computat Intelligence & Chinese Informat P Minist Educ Taiyuan 030006 Shanxi Peoples R China Hefei Univ Technol Sch Comp & Informat Hefei 230009 Peoples R China

Feature weighting aims to assign different weights to features based on their importance in machine learning tasks. In clustering tasks, the existing methods learn feature importance based on the clustering results derived from the collaborative contribution of all features, which overlooks the independent effect of each feature. In fact, there are underlying causal relationships between features and the clustering results, and the features with high causal effects are always more crucial for clustering. Therefore, we propose an enhanced Feature Weighting method via Causal Effect for Clustering, calculating the causal effect of each feature on the clustering results for obtaining the independent contribution of each feature. Specifically, we start by identifying the causal relationships among the features and utilizing the causal relationships to generate a reasonable treatment group. Next, we compare the changes in the data distribution between the treatment and control groups to determine the causal effect of each feature. Finally, the causal effects of features are used for enhancing the clustering-driven weight learning. Moreover, we present a theory of relative order consistency in causal effect. Experimental results demonstrate that utilizing causal effect in weight learning facilitates efficient convergence and achieves superior accuracy compared to state-of-the-art clustering algorithms.

关键词： Clustering algorithms Machine learning algorithms Machine learning partitioning algorithms Noise measurement Collaboration Linear programming Convergence Technological innovation Reviews Feature weights clustering causal effect feature importance

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-Graph: A Graph Embedding for Interpretable Time Series Clustering

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2025年第5期37卷 2680-2694页

作者： Boniol, Paul Tiano, Donato Bonifati, Angela Palpanas, Themis Inria PSL CNRS ENS F-75005 Paris France Univ Modena I-42121 Modena Italy Lyon 1 Univ IUF LIRIS CNRS Villeurbanne F-69622 Villeurbanne France Univ Paris Cite IUF F-75006 Paris France

Time series clustering poses a significant challenge with diverse applications across domains. A prominent drawback of existing solutions lies in their limited interpretability, often confined to presenting users with centroids. In addressing this gap, our work presents k-Graph, an unsupervised method explicitly crafted to augment interpretability in time series clustering. Leveraging a graph representation of time series subsequences, k-Graph constructs multiple graph representations based on different subsequence lengths. This feature accommodates variable-length time series without requiring users to predetermine subsequence lengths. Our experimental results reveal that k-Graph outperforms current state-of-the-art time series clustering algorithms in accuracy, while providing users with meaningful explanations and interpretations of the clustering outcomes.

关键词： Time series analysis Feature extraction Clustering algorithms Accuracy Heuristic algorithms Clustering methods Training Shape partitioning algorithms Directed graphs Time series clustering interpretability

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High-Precision Deblending of 3-D Simultaneous Source Data Based on Prior Information Constraint

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2025年 22卷

作者： Zhang, Hua Bao, Xingyue Zhao, Hu Hao, Yaju Huang, Hongwei Dai, Mengxue Liu, Weihua East China Univ Technol Natl Key Lab Uranium Resources Explorat Min & Nucl Nanchang 330013 Peoples R China SINOPEC Key Lab Geophys Nanjing 211103 Peoples R China Southwest Petr Univ Sch Geosci & Technol Chengdu 610500 Peoples R China

How to effectively remove blending noise is the key to the success of simultaneous source acquisition. The traditional deblending of 3-D simultaneous source data is based on the energy difference between blending noise and useful signals in the sparse domain and removes blending noise independently of each 2-D partition of 3-D simultaneous source data using a sparse inversion algorithm. However, these 3-D deblending techniques do not fully utilize a priori information derived from the locations of the spare coefficients from different partitions, especially when highly redundant curvelet domain sparsity is used. In this letter, we propose to use the priori support information to improve the deblending performance of 3-D simultaneous source data by using a 2-D curvelet transform. First, based on the correlations between the support sets of the significant curvelet coefficients of adjacent partitions, a weighted operator is constructed in the curvelet domain using the prior support sets provided by the previous partition that has removed blending noise. Then, incorporate it into the iterative shrinkage thresholding algorithm (ISTA) to delineate the main energy distribution range of the useful signals, and define a sequence of 2-D curvelet-based deblending problems that exploit 3-D correlations exhibited by seismic wavefields without using the highly redundant 3-D curvelet transform. To test the performance of our proposed method, we compared deblending from synthetic and field artificially blended data. These examples demonstrated that our method is superior to the traditional iterative algorithm in terms of deblending performance, and computational memory requirements.

关键词： Noise Three-dimensional displays Geoscience and remote sensing partitioning algorithms Accuracy Iterative algorithms Frequency-domain analysis Correlation Uranium Transforms Deblending iterative shrinkage thresholding algorithm (ISTA) prior information weighted operator

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Partitioned Scheduling With Shared Resources on Imprecise Mixed-Criticality Multiprocessor Systems

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IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 2025年第1期44卷 65-76页

作者： Zhang, Yi-Wen Ma, Jin-Peng Gu, Zonghua Huaqiao Univ Coll Comp Sci & Technol Xiamen 361021 Peoples R China Hofstra Univ Dept Comp Sci Hempstead NY 11549 USA Umea Univ Dept Appl Phys & Elect S-90187 Umea Sweden

Both resource access protocols and real-time scheduling algorithms have been extensively studied in classic embedded real-time systems. However, there has been relatively little attention given to the resource access protocol and real-time scheduling algorithms in mixed-criticality systems. In this article, we pay attention to the problem of scheduling an imprecise mixed-criticality (IMC) taskset on a multiprocessor platform with shared resources. First, we propose an IMC with MSRP (IMC-MSRP) resource access protocol, which ensures mutually exclusive access to the shared resources for the tasks. Second, we propose the schedulability test based on the IMC-multiprocessor stack resource policy (MSRP) for a given task-to-processor mapping method. Third, we propose a feasible task-to-processor mapping algorithm called resource-aware criticality-unaware worst-fit decreasing (RA-CU-WFD), which first assigns tasks sharing the same resources to the same processor to reduce the global waiting time of the tasks and thus improve the schedulability ratio of the system. And then assigns tasks based on the criticality-unaware worst-fit decreasing (CU-WFD) algorithm. Finally, we conduct experiments using the synthetic tasksets, and the experimental results show that the RA-CU-WFD outperforms the other approaches in terms of the schedulability ratio.

关键词： Task analysis Program processors Job shop scheduling Access protocols Scheduling algorithms Quality of service partitioning algorithms Imprecise mixed-criticality (IMC) multicore partitioned scheduling shared resource

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Fast Domain Decomposition Algorithm Using Barycentric Interpolation and Overlapping Subdomains for 3D Multiscale Problems

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IEEE JOURNAL ON MULTISCALE AND MULTIPHYSICS COMPUTATIONAL TECHNIQUES 2025年 10卷 198-208页

作者： Kielian, Nils Stiemer, Marcus Univ Fed Armed Forces Hamburg Helmut Schmidt Univ Dept Theory Elect Engn & Numer Field Computat D-22043 Hamburg Germany

A robust data-transfer process for balanced domain decomposition is presented. This algorithm reduces the number of mesh elements required to solve certain 3D multiscale problems with the finite element method. In some examples, a reduction factor of up to 5 has been observed. The reduction is achieved by introducing an overlapping auxiliary domain to an originally non-overlapping domain decomposition scheme, allowing for individual meshing of the subdomains. The data transfer to couple the individually meshed subdomains is performed with the help of barycentric interpolation. Hence, the advantages of parallel solution of subdomain problems is combined with a stable inter-subdomain data transfer. The developed algorithm can be applied on problems with a scalar valued second order spatial elliptic differential operator in various fields of engineering, such as semiconductors, huge and complex biological cell clusters, heat conducting and pressure problems on multiple scales.

关键词： Data transfer Couplings Finite element analysis Interpolation Mortar Convergence Differential operators Boundary conditions partitioning algorithms Iterative methods Domain decomposition finite element method interface problems electro-quasistatic problems

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