From fine-grained to abstract process models: a semantic approach

Sergey Smirnov, Hajo A. Reijers, Mathias Weske

Research output: Contribution to journalArticleAcademicpeer-review

18 Citations (Scopus)

Abstract

Organizations actively managing their business processes face a rapid growth of the number of process models that they maintain. Business process model abstraction has proven to be an effective means to generate readable, high-level views on business process models by showing coarse-grained activities and leaving out irrelevant details. In this way, abstraction facilitates a more efficient management of process models, as a single model can provide for many relevant views. Yet, it is an open question how to perform abstraction in the same skillful way as experienced modelers combine activities into more abstract tasks. This paper presents an approach that uses semantic information of a process model to decide on which activities belong together, which extends beyond existing approaches that merely exploit model structural characteristics. The contribution of this paper is twofold: we propose a novel activity aggregation method and suggest how to discover the activity aggregation habits of human modelers. In an experimental validation, we use an industrial process model repository to compare the developed activity aggregation method with actual modeling decisions, and observe a strong correlation between the two. The presented work is expected to contribute to the development of modeling support for the effective process model abstraction.

Original languageEnglish
Pages (from-to)784-797
Number of pages14
JournalInformation Systems
Volume37
Issue number8
DOIs
Publication statusPublished - 1 Jan 2012

Keywords

  • Business process model abstraction
  • Business process modeling
  • Process model management

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