IEEE Transactions on Services Computing最新文献

{"title":"Efficient Online Computation of Business Process State From Trace Prefixes via N-Gram Indexing","authors":"David Chapela-Campa;Marlon Dumas","doi":"10.1109/TSC.2025.3547235","DOIUrl":"10.1109/TSC.2025.3547235","url":null,"abstract":"This paper addresses the following problem: Given a process model and an event log containing trace prefixes of ongoing cases of a process, map each case to its corresponding state (i.e., marking) in the model. This state computation operation is a building block of other process mining operations, such as log animation and short-term simulation. An approach to this state computation problem is to perform a token-based replay of each trace prefix against the model. However, when a trace prefix does not strictly follow the behavior of the model, token replay may produce a state that is not reachable from the initial state of the process. An alternative approach is to first compute an alignment between the trace prefix of each ongoing case and the model, and then replay the aligned trace prefix. However, (prefix-)alignment is computationally expensive. This paper proposes a method that, given a trace prefix of an ongoing case, computes its state in constant time on the length of the trace using an index that represents states as <inline-formula><tex-math>$n$</tex-math></inline-formula>-grams. An empirical evaluation shows that the proposed approach has an accuracy comparable to that of the prefix-alignment approach, while achieving a throughput of hundreds of thousands of traces per second.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 2","pages":"770-783"},"PeriodicalIF":5.5,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143538764","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}

{"title":"BlockEmulator: An Emulator Enabling to Test Blockchain Sharding Protocols","authors":"Huawei Huang;Guang Ye;Qinglin Yang;Qinde Chen;Zhaokang Yin;Xiaofei Luo;Jianru Lin;Jian Zheng;Taotao Li;Zibin Zheng","doi":"10.1109/TSC.2025.3547222","DOIUrl":"10.1109/TSC.2025.3547222","url":null,"abstract":"Numerous blockchain simulators have been proposed to allow researchers to simulate mainstream blockchains. However, we have not yet found a testbed that enables researchers to develop and evaluate their new consensus algorithms or new protocols for blockchain sharding systems. To fill this gap, we developed BlockEmulator, which is designed as an experimental platform, particularly for emulating blockchain sharding mechanisms. BlockEmulator adopts a lightweight blockchain architecture so developers can only focus on implementing their new protocols or mechanisms. Using layered modules and useful programming interfaces offered by BlockEmulator, researchers can implement a new protocol with minimum effort. Through experiments, we test various functionalities of BlockEmulator in two steps. First, we prove the correctness of the emulation results yielded by BlockEmulator by comparing the theoretical analysis with the observed experiment results. Second, other experimental results demonstrate that BlockEmulator can facilitate measuring a series of metrics, including throughput, transaction confirmation latency, cross-shard transaction ratio, the queuing status of transaction pools, workload distribution across blockchain shards, etc. We have made BlockEmulator open-source in Github.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 2","pages":"690-703"},"PeriodicalIF":5.5,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143538765","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}

{"title":"Service Migration for Delay-Sensitive IoT Applications in Edge Networks","authors":"Xiaocui Li, Zhangbing Zhou, Yasha Wang, Shuiguang Deng, Patrick C. K. Hung","doi":"10.1109/tsc.2025.3547221","DOIUrl":"https://doi.org/10.1109/tsc.2025.3547221","url":null,"abstract":"","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"49 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143538766","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}

{"title":"Li-MSA: Power Consumption Prediction of Servers Based on Few-Shot Learning","authors":"Saiqin Long;Yuan Li;Zhetao Li;Guoqi Xie;Weiwei Lin;Kenli Li","doi":"10.1109/TSC.2025.3541555","DOIUrl":"10.1109/TSC.2025.3541555","url":null,"abstract":"Power consumption prediction is one of the keys to optimize the energy consumption of servers. Existing traditional regression-based methods are too simple and poorly generalized, while popular deep learning methods require too much data. Therefore, they are difficult to be widely generalized. In this study, we propose a framework of linear interpolation multi-head sparse temporal pattern attention (Li-MSA) based on few-shot learning for power consumption prediction of servers with small-scale datasets in environments such as cloud data centers or edge computing. First, the interpolation reconstruction module extends and smooths the data. Then, the embedding learning module is used to narrow the scope of the hypothesis space. Finally, the multi-head sparse temporal pattern attention module emphasizes features and predicts power consumption. The results of the experiments show that Li-MSA outperforms the best results among the other methods for two datasets with different time steps in the RMSE metric by 15.34%, 17.35%, 18.18%, 6.28%, 4.05%, 7.73%.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 2","pages":"926-939"},"PeriodicalIF":5.5,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143518792","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}

{"title":"A Malicious Information Popularity Prediction Model Based on User Influence","authors":"Tun Li;Yan Tang;Rong Xie;Yuqi Weng;Qian Li;Rong Wang;Chaolong Jia;Yunpeng Xiao","doi":"10.1109/TSC.2025.3544122","DOIUrl":"10.1109/TSC.2025.3544122","url":null,"abstract":"In social networks, studying methods for predicting the popularity of malicious information can help improve the ability to predict online public opinion. This paper proposes a malicious information popularity prediction model based on user influence, targeting the cooperative adversarial nature of malicious information propagation, the problem of assessing user influence in malicious information propagation space, and the complexity of malicious information propagation space. First, regarding the cooperative adversarial nature of the malicious information propagation process, considering that user behavior is influenced by both malicious and positive information during the propagation process, evolutionary game theory and multiple linear regression are introduced, and internal and external behavioral factors of the user are synthesized to construct influential functions that quantify malicious information and positive information. Meanwhile, the influence matrix is introduced when quantifying information to construct a weighted malicious information propagation network further. Second, regarding the problem of assessing user influence in the malicious information propagation space, considering the advantages of PageRank in measuring the importance of web pages and combining the timeliness of malicious information propagation, an improved algorithm T-PageRank (Timeliness-PageRank) based on timeliness is proposed. Introducing the time decay factor into the PageRank algorithm effectively enhances the accuracy and timeliness of the influence assessment of malicious information propagation. Finally, regarding the complexity of the propagation space of malicious information and considering that Graph Attention Network (GAT) can effectively capture complex relationships between nodes, combined with user influence, a malicious information popularity prediction model based on GAT is constructed. The model learns the complex interaction between users by using GAT and updates the feature representation of users so that it can be used for subsequent malicious information popularity prediction tasks. The experiment shows that the model can not only accurately assess the influence of users but also effectively predict the popularity of malicious information propagation.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 2","pages":"543-556"},"PeriodicalIF":5.5,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143462245","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}

{"title":"Leveraging Consortium Blockchain for Secure Cross-Domain Data Sharing in Supply Chain Networks","authors":"Runqun Xiong;Jing Cheng;Xirui Dong;Jiahang Pu;Feng Shan","doi":"10.1109/TSC.2025.3544130","DOIUrl":"10.1109/TSC.2025.3544130","url":null,"abstract":"Supply Chain Networks (SCNs) play a vital role in achieving strategic decision-making for production and distribution facilities, aiming to meet market demands and gain competitive advantages. With the application of new-generation information technology in the supply chain, enterprises within SCNs generate a substantial volume of relevant business data. Sharing this data among SCN enterprises can effectively reduce operating costs, optimize business processes, and enhance the overall efficiency of the supply chain. However, effective data sharing among SCN participants faces challenges, such as data leakage, data quality assurance, and fair data value allocation. To address these challenges, this paper proposes a secure cross-domain data sharing model in SCNs (named SCN-CDSM) based on consortium blockchain technology. The model introduces trust, enables cross-domain data exchange, and promotes cooperation among supply chain enterprises. To ensure privacy, group signatures and access control smart contracts are designed, along with an approach to reduce blockchain throughput limitations. Furthermore, a sharing incentive mechanism utilizing the Stackelberg game model based on data value is designed to foster fairness and collaboration. Extensive numerical simulations are conducted to demonstrate the effectiveness of the proposed schemes, achieving both security and efficiency in data sharing within SCNs.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 2","pages":"897-911"},"PeriodicalIF":5.5,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143462250","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}

{"title":"An Adaptively Bias-Extended Non-Negative Latent Factorization of Tensors Model for Accurately Representing the Dynamic QoS Data","authors":"Xiuqin Xu;Mingwei Lin;Xin Luo;Zeshui Xu","doi":"10.1109/TSC.2025.3544123","DOIUrl":"10.1109/TSC.2025.3544123","url":null,"abstract":"Time-varying quality-of-service (QoS) data are usually utilized for Web service evaluation and selection. To accurately estimate the unknown information in time-varying QoS data, it is crucial to capture the temporal patterns hidden in the known data. The Non-negative Latent Factorization of Tensors (NLFT) model has performed well in describing the temporal patterns in time-varying QoS data. However, it assigns a single bias to each dimension of the target QoS tensor, making it suffer from estimation accuracy loss when describing the fluctuations of time-varying QoS data. To address this vital issue, this paper proposes an Adaptively Bias-extended NLFT (ABNT) model based on the fuzzy logic with two-fold ideas: a) extending the linear biases on each dimension of tensor for describing the complex fluctuations of QoS data precisely, b) building a fuzzy logic-incorporated particle swarm optimization algorithm to establish a self-adaptation mechanism for the count of extended linear biases and regularization coefficients. Detailed algorithms and analyses are provided for the proposed ABNT model. Empirical studies on two practical time-varying QoS datasets indicate that the estimation accuracy of the ABNT model outperforms that of state-of-the-art QoS data estimation models (with an average 23.94% improvement in MAE).","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 2","pages":"603-617"},"PeriodicalIF":5.5,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143462456","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}

{"title":"Joint Trajectory Optimization and Resource Allocation in UAV-MEC Systems: A Lyapunov-Assisted DRL Approach","authors":"Ying Chen;Yaozong Yang;Yuan Wu;Jiwei Huang;Lian Zhao","doi":"10.1109/TSC.2025.3544124","DOIUrl":"10.1109/TSC.2025.3544124","url":null,"abstract":"Mobile Edge Computing (MEC), as a highly promising technology, effectively processes computation-intensive tasks by offloading them to edge servers. Utilizing the advantages of Unmanned Aerial Vehicles (UAVs) in deployment flexibility and broad coverage, UAV-assisted edge computing can significantly enhance system efficiency. This paper studies a scenario where a UAV-MEC system serves multiple Mobile Users (MUs) with random task arrivals and movements. We minimize the energy consumption of MUs by jointly optimizing UAV trajectory and resource allocation for MUs subjected to the UAV energy limit. The problem is formulated as a multi-stage Mixed-Integer Nonlinear Programming (MINLP) problem. To address this, we propose an algorithm called JTORA integrated Deep Reinforcement Learning (DRL) and Lyapunov optimization techniques. Specifically, we initially transform the multi-stage MINLP problem into a deterministic optimization problem utilizing Lyapunov techniques and decompose the original problem into two sub-problems in parallel. Through DRL, we solve the first sub-problem of trajectory and communication resources optimization. For the second sub-problem involving computing resource allocation, convex optimization is employed to get the optimal solution. Theoretical analysis and experimental results demonstrate that the JTORA algorithm can effectively reduce the energy consumption of MUs while ensuring UAV endurance.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 2","pages":"854-867"},"PeriodicalIF":5.5,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143462246","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}

{"title":"Learning Schema Embeddings for Service Link Prediction: A Coupled Matrix-Tensor Factorization Approach","authors":"Jing Yang;Xiaofen Wang;Laurence T. Yang;Yuan Gao;Shundong Yang;Xiaokang Wang","doi":"10.1109/TSC.2025.3541552","DOIUrl":"10.1109/TSC.2025.3541552","url":null,"abstract":"Schema information is increasingly crucial to improve service discovery, recommendation, and composition, addressing link sparsity and lack of explainability inherent in methods relying solely on triples. However, existing approaches predominantly utilize schema information as a rigid filtering mechanism, equivalent to fixed conditions that lack the capability to adaptively adjust based on model learning. This paper introduces a novel learnable schema-aware knowledge embedding framework that enhances service link prediction by synergizing entity, relation, and type embeddings through a coupled matrix-tensor factorization model. To our knowledge, this is the first approach that couples entity and relation embeddings to enable adaptive learning of <bold><u>Schema</u></b> <bold><u>E</u></b>mbeddings (<bold>SchemaE</b>). Our framework is both expressive and easy to use, with the capability to generalize to existing bilinear models. Within this framework, we further propose the schema prompt method for embedding isolated nodes, which typically suffer from sparse relations or the absence of neighbors, leading to biased representation often overlooked in existing works. Despite embedding schema information, our model remains lightweight due to the introduction of a parameter-efficient strategy via type assists. We conduct extensive experiments on four public datasets, including comparisons with existing SOTA models, parameter analysis, performance validation on extended models, and visualization. The experimental results confirm the effectiveness and efficiency of the proposed model.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 2","pages":"883-896"},"PeriodicalIF":5.5,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143443459","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}

{"title":"User Preference Oriented Service Caching and Task Offloading for UAV-Assisted MEC Networks","authors":"Ruiting Zhou;Yifeng Huang;Yufeng Wang;Lei Jiao;Haisheng Tan;Renli Zhang;Libing Wu","doi":"10.1109/TSC.2025.3536319","DOIUrl":"10.1109/TSC.2025.3536319","url":null,"abstract":"Unmanned aerial vehicles (UAVs) have emerged as a new and flexible paradigm to offer low-latency and diverse mobile edge computing (MEC) services for user equipment (UE). To minimize the service delay, caching is introduced in UAV-assisted MEC networks to bring service contents closer to UEs. However, UAV-assisted MEC is challenged by the heavy communication overhead introduced by service caching and UAV’s limited energy capacity. In this article, we propose an online algorithm, <italic>OOA</i>, that jointly optimizes caching and offloading decisions for UAV-assisted MEC networks, to minimize the overall service delay. Specifically, to improve the caching effectiveness and reduce the caching overhead, <italic>OOA</i> employs a greedy algorithm to dynamically make caching decisions based on UEs’ preferences on services and UAVs’ historical trajectories, with the goal of maximizing the probability of successful offloading. To realize the rational utilization of energy from a long-term perspective, <italic>OOA</i> decomposes the online problem into a series of single-slot problems by scaling the UAV’s energy constraint into the objective, and iteratively optimizes UAV trajectory and task offloading at each time slot. Theoretical analysis proves that <italic>OOA</i> converges to a suboptimal solution with polynomial time complexity. Extensive simulations based on real world data further show that <italic>OOA</i> can reduce the service delay by up to 33% while satisfying the UAV’s energy constraint, compared to three state-of-the-art algorithms.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 2","pages":"1097-1109"},"PeriodicalIF":5.5,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143417811","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}