A Formal Framework for Diagnostic Analysis for Errors of Business Processes

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• 关键词：Verification ; Formal methods ; Processes ; BPM Notation ; Errors ; Soundness ; Petri nets ; Workflow nets ; Woflan ; Diagnosis ; Metrics
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9930
• 期：1
• 页码：226-261
• 全文大小：3,025 KB
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• 作者单位：Suman Roy (16)
  A. S. M. Sajeev (17)
  
  16. Infosys Ltd., #44 Electronics City, Hosur Road, Bangalore, 560 100, India
  17. Melbourne Institute of Technology, Sydney, NSW, 2000, Australia
  
• 丛书名：Transactions on Petri Nets and Other Models of Concurrency XI
• ISBN：978-3-662-53401-4
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9930

文摘

Business process models expressed in languages such as BPMN (Business Process Model and Notation), play a critical role in implementing the workflows in modern enterprises. However, control flow errors such as deadlocks and lack of synchronization, and syntactic errors arising out of poor modeling practices often occur in industrial process models. A major challenge is to provide the means and methods to detect such errors and more importantly, to identify the location of each error. In this work, we develop a formal framework of diagnosing errors by locating their occurrence nodes in business process models at the level of sub-processes and swim-lanes. We use graph-theoretic techniques and Petri net-based analyses to detect syntactic and control flow-related errors respectively. While syntactic errors can be easily located on the processes themselves, we project control-related errors on processes using a mapping from Petri nets to processes. We use this framework to analyze a sample of 174 industrial BPMN process models having 1262 sub-processes in which we identify more than 2000 errors. We are further able to discover how error frequencies change with error depth, how they correlate with the size of the sub-processes and swim-lane interactions in the models, and how they can be predicted in terms of process metrics like sub-process size, coefficient of connectivity, sequentiality and structuredness.