Detecting Role Inconsistencies in Process Models

Banu Aysolmaz, Deniz Iren, Hajo A. Reijers

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

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Abstract

Business process models capture crucial information about business operations. To overcome the challenge of maintaining process definitions in large process repositories, researchers have suggested methods to discover errors in the functional and the behavioral perspectives of process models. However, there is a gap in the literature on the detection of problems on the organizational perspective of process models, which is critical to manage the resources and the responsibilities within organizations. In this paper, we introduce an approach to automatically detect inconsistencies between activities and roles in process models. Our approach implements natural language processing techniques and enterprise semantics to identify ambiguous, redundant, and missing roles in textual descriptions. We applied our approach on the process model repository of a major telecommunication company. A quantitative evaluation of our approach with 282 real-life activities displayed that this approach can accurately discover role inconsistencies. Practitioners can achieve significant quality improvements in their process model repositories by applying the approach on process models complemented with textual descriptions.
Original languageEnglish
Title of host publication Proceedings of the 27th European Conference on Information Systems (ECIS), Stockholm & Uppsala, Sweden
Place of PublicationSweden
Number of pages16
ISBN (Electronic)978-1-7336325-0-8
Publication statusPublished - 15 May 2019

Keywords

  • business process modeling
  • inconsistency detection
  • organizational perspective
  • natural language processing

Cite this

Aysolmaz, B., Iren, D., & Reijers, H. A. (2019). Detecting Role Inconsistencies in Process Models. In Proceedings of the 27th European Conference on Information Systems (ECIS), Stockholm & Uppsala, Sweden [27]. https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1026&context=ecis2019_rp