{"title":"Supervisory control of business processes with resources, parallel and mutually exclusive branches, loops, and uncertainty","authors":"Davide Bresolin, Matteo Zavatteri","doi":"10.1016/j.is.2023.102288","DOIUrl":null,"url":null,"abstract":"<div><p>A recent direction in <em>Business Process Management</em> studied methodologies to control the execution of <em>Business Processes</em> under several sources of uncertainty in order to always get to the end by satisfying all constraints. Current approaches encode business processes into temporal constraint networks or timed game automata in order to exploit their related strategy synthesis algorithms. However, the proposed encodings can only synthesize <em>single</em>-strategies and fail to handle loops. To overcome these limits we propose an approach based on <em>supervisory control</em>. We consider <em>structured business processes</em> with resources, parallel and mutually exclusive branches, loops, and uncertainty. We provide an encoding into finite state automata and prove that their concurrent behavior models exactly all possible executions of the process. After that, we introduce <em>tentative commitment constraints</em> as a new class of constraints restricting the executions of a process. We define a tree decomposition of the process that plays a central role in modular supervisory control, and we prove that this modular approach is equivalent to the monolithic one. We provide an algorithm to compute the <em>finest tree decomposition</em> to reduce the computational effort of synthesizing supervisors.</p></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":null,"pages":null},"PeriodicalIF":3.0000,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Systems","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0306437923001242","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
A recent direction in Business Process Management studied methodologies to control the execution of Business Processes under several sources of uncertainty in order to always get to the end by satisfying all constraints. Current approaches encode business processes into temporal constraint networks or timed game automata in order to exploit their related strategy synthesis algorithms. However, the proposed encodings can only synthesize single-strategies and fail to handle loops. To overcome these limits we propose an approach based on supervisory control. We consider structured business processes with resources, parallel and mutually exclusive branches, loops, and uncertainty. We provide an encoding into finite state automata and prove that their concurrent behavior models exactly all possible executions of the process. After that, we introduce tentative commitment constraints as a new class of constraints restricting the executions of a process. We define a tree decomposition of the process that plays a central role in modular supervisory control, and we prove that this modular approach is equivalent to the monolithic one. We provide an algorithm to compute the finest tree decomposition to reduce the computational effort of synthesizing supervisors.
期刊介绍:
Information systems are the software and hardware systems that support data-intensive applications. The journal Information Systems publishes articles concerning the design and implementation of languages, data models, process models, algorithms, software and hardware for information systems.
Subject areas include data management issues as presented in the principal international database conferences (e.g., ACM SIGMOD/PODS, VLDB, ICDE and ICDT/EDBT) as well as data-related issues from the fields of data mining/machine learning, information retrieval coordinated with structured data, internet and cloud data management, business process management, web semantics, visual and audio information systems, scientific computing, and data science. Implementation papers having to do with massively parallel data management, fault tolerance in practice, and special purpose hardware for data-intensive systems are also welcome. Manuscripts from application domains, such as urban informatics, social and natural science, and Internet of Things, are also welcome. All papers should highlight innovative solutions to data management problems such as new data models, performance enhancements, and show how those innovations contribute to the goals of the application.