A Graph Processing Based Approach for Automatic Detection of Semantic Inconsistency Between BPMN Process Model and SBVR Rules

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• 关键词：Business Process Modeling ; Business rule modeling ; Inconsistency detection ; Business process intelligence ; Graph matching algorithms
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9468
• 期：1
• 页码：115-129
• 全文大小：3,991 KB
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• 作者单位：Akanksha Mishra (16)
  Ashish Sureka (17)
  
  16. Indraprastha Institute of Information Technology-Delhi (IIIT-D), New Delhi, India
  17. Software Analytics Research Lab (SARL), New Delhi, India
  
• 丛书名：Mining Intelligence and Knowledge Exploration
• ISBN：978-3-319-26832-3
• 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

文摘

Business Process Modeling Notation (BPMN) is a technique for graphically drawing and illustrating business processes in diagramtic form. Semantic of Business Vocabulary and Business Rules (SBVR) is a declarative language used to define business vocabulary, rules and policy. Several times inconsistencies occur between BPMN and SBVR as they are independently maintained. Our aim is to investigate techniques for automatically detecting inconsistencies between business process and rules. We present a method for inconsistency detection (between BPMN and SBVR) based on converting SBVR rules to graphical representation and apply sub graph-isomorphism to detect instances of inconsistencies between BPMN and SBVR models. We propose a multi-step process framework for identification of instances of inconsistencies between the two models. We first generate an XML of BPMN diagram and apply parsing and tag extraction. We then apply Stanford NLP Parser to generate parse tree of rules. The detailed information about the parse tree is stored in the form of Typed Dependency which represent grammatical relation between words of a sentence. We utilize the grammatical relation extract triplet (actor-action-object) of a sentence. We find node-induced sub-graph of all possible length of nodes of a graph and apply VF2 Algorithm to detect instances of inconsistency between sub graphs. Finally, we evaluate the proposed research framework by conducting experiments on synthetic dataset to validate the accuracy and effectiveness of our approach.