Pub Date : 2025-12-16DOI: 10.1109/TSC.2025.3644861
Xiwei Zhang;Xianwen Fang;Jianhua Gong;Gubao Mao;Cong Liu
Predictive Process Monitoring (PPM) aims to predict the future states of ongoing process instances. A primary objective is to accurately predict process outcomes while ensuring decision transparency, which is critical for enhancing process efficiency and reducing operational risk. Existing interpretable approaches to process monitoring often struggle with balancing transparency and reliability.Specifically, approaches that prioritize transparency often fall short in predictive accuracy and generalization, while those that achieve higher prediction performance often provide less reliable explanations. To address these limitations, we propose a novel Transparent Process Outcome Prediction framework (TPOP) using a graph neural network with stochastic attention. We begin by applying a SHAP-based feature selection technique to identify and extract the most relevant attributes from the log, thereby improving the quality of graph-based process representations. Next, we introduce a graph stochastic attention mechanism, which helps the model in concentrate on key paths and activities during training, leading to transparent and trustworthy predictions. Experimental evaluations on ten real-life event logs demonstrate that our approach outperforms state-of-the-art approaches in both predictive performance and interpretability. Furthermore, by visualizing how specific activities influence process outcomes across various cases, we confirm the reliability of the explanations generated by our approach.
{"title":"Transparent Business Process Outcome Prediction Using a Graph Stochastic Attention Mechanism","authors":"Xiwei Zhang;Xianwen Fang;Jianhua Gong;Gubao Mao;Cong Liu","doi":"10.1109/TSC.2025.3644861","DOIUrl":"10.1109/TSC.2025.3644861","url":null,"abstract":"Predictive Process Monitoring (PPM) aims to predict the future states of ongoing process instances. A primary objective is to accurately predict process outcomes while ensuring decision transparency, which is critical for enhancing process efficiency and reducing operational risk. Existing interpretable approaches to process monitoring often struggle with balancing transparency and reliability.Specifically, approaches that prioritize transparency often fall short in predictive accuracy and generalization, while those that achieve higher prediction performance often provide less reliable explanations. To address these limitations, we propose a novel Transparent Process Outcome Prediction framework (TPOP) using a graph neural network with stochastic attention. We begin by applying a SHAP-based feature selection technique to identify and extract the most relevant attributes from the log, thereby improving the quality of graph-based process representations. Next, we introduce a graph stochastic attention mechanism, which helps the model in concentrate on key paths and activities during training, leading to transparent and trustworthy predictions. Experimental evaluations on ten real-life event logs demonstrate that our approach outperforms state-of-the-art approaches in both predictive performance and interpretability. Furthermore, by visualizing how specific activities influence process outcomes across various cases, we confirm the reliability of the explanations generated by our approach.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"19 1","pages":"712-725"},"PeriodicalIF":5.8,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145770767","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 : 2025-12-11DOI: 10.1109/tsc.2025.3643326
Guanyu Chen, Weiwei Lin, Fang Shi, Haotong Zhang, Bin Wang
{"title":"PAWSSP: A Two-stage Parallelism-aware Algorithm for Joint Workflow Scheduling and Service Placement in Edge Computing","authors":"Guanyu Chen, Weiwei Lin, Fang Shi, Haotong Zhang, Bin Wang","doi":"10.1109/tsc.2025.3643326","DOIUrl":"https://doi.org/10.1109/tsc.2025.3643326","url":null,"abstract":"","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"6 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145728857","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 : 2025-12-11DOI: 10.1109/tsc.2025.3643482
Yubao Deng, Mande Xie, Houbing Herbert Song, Anfeng Liu, Yuxin Liu
{"title":"DQO-P5PI: A Preservation DRIBL Privacy and Data Quality Scheme using Fuzzy Sets for MCS Service System","authors":"Yubao Deng, Mande Xie, Houbing Herbert Song, Anfeng Liu, Yuxin Liu","doi":"10.1109/tsc.2025.3643482","DOIUrl":"https://doi.org/10.1109/tsc.2025.3643482","url":null,"abstract":"","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"15 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145728855","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 : 2025-12-09DOI: 10.1109/tsc.2025.3642033
Yu Song, Yinlin Ren, Xuesong Qiu, Ao Xiong, Shaoyong Guo
{"title":"Trusted Lifecycle Management for AIGC Services in Metaverse: a Blockchain-Empowered Collaborative Service Framework","authors":"Yu Song, Yinlin Ren, Xuesong Qiu, Ao Xiong, Shaoyong Guo","doi":"10.1109/tsc.2025.3642033","DOIUrl":"https://doi.org/10.1109/tsc.2025.3642033","url":null,"abstract":"","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"46 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145718349","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 : 2025-12-09DOI: 10.1109/TSC.2025.3641367
Jie Zhang;Xiaohong Li;Man Zheng;Ruitao Feng;Shanshan Xu;Zhe Hou;Guangdong Bai
Enabling search over encrypted cloud data is essential for privacy-preserving data outsourcing. While searchable encryption has evolved to support individual requirements like fuzzy matching (tolerance to typos and variants in query keywords), dynamic updates, and result verification, designing a service that supports Dynamic Verifiable Fuzzy Search (DVFS) over encrypted cloud data remains a fundamental challenge due to inherent conflicts between underlying technologies. Existing approaches struggle with simultaneously achieving efficiency, functionality, and security, often forcing impractical trade-offs. This paper presents VeriFuzzy, a novel DVFS service framework that cohesively integrates three innovations: an Enhanced Virtual Binary Tree (EVBTree) that decouples fuzzy semantics from index logic to support $O(log n)$ search/updates; a blockchain-reconstructed verification mechanism that ensures result integrity with logarithmic complexity; and a dual-repository state management scheme that achieves IND-CKA2 security by neutralizing branch leakage. Extensive evaluation on 3,500+ documents shows VeriFuzzy achieves 41% faster search, $5times$ more efficient verification, and constant-time index updates compared to state-of-the-art alternatives. Our code and dataset are now open source, hoping to inspire future DVFS research.
{"title":"VeriFuzzy: A Dynamic Verifiable Fuzzy Search Service Framework for Encrypted Cloud Data","authors":"Jie Zhang;Xiaohong Li;Man Zheng;Ruitao Feng;Shanshan Xu;Zhe Hou;Guangdong Bai","doi":"10.1109/TSC.2025.3641367","DOIUrl":"10.1109/TSC.2025.3641367","url":null,"abstract":"Enabling search over encrypted cloud data is essential for privacy-preserving data outsourcing. While searchable encryption has evolved to support individual requirements like fuzzy matching (tolerance to typos and variants in query keywords), dynamic updates, and result verification, designing a service that supports Dynamic Verifiable Fuzzy Search (DVFS) over encrypted cloud data remains a fundamental challenge due to inherent conflicts between underlying technologies. Existing approaches struggle with simultaneously achieving efficiency, functionality, and security, often forcing impractical trade-offs. This paper presents <b>VeriFuzzy</b>, a novel DVFS service framework that cohesively integrates three innovations: an <i>Enhanced Virtual Binary Tree (EVBTree)</i> that decouples fuzzy semantics from index logic to support <inline-formula><tex-math>$O(log n)$</tex-math></inline-formula> search/updates; a <i>blockchain-reconstructed verification</i> mechanism that ensures result integrity with logarithmic complexity; and a <i>dual-repository state management</i> scheme that achieves IND-CKA2 security by neutralizing branch leakage. Extensive evaluation on 3,500+ documents shows VeriFuzzy achieves 41% faster search, <inline-formula><tex-math>$5times$</tex-math></inline-formula> more efficient verification, and constant-time index updates compared to state-of-the-art alternatives. Our code and dataset are now open source, hoping to inspire future DVFS research.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"19 1","pages":"780-793"},"PeriodicalIF":5.8,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145717958","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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