Probabilistic optimization of semantic process model matching

  • Authors:
  • Henrik Leopold;Mathias Niepert;Matthias Weidlich;Jan Mendling;Remco Dijkman;Heiner Stuckenschmidt

  • Affiliations:
  • Humboldt-Universität zu Berlin, Berlin, Germany;Universität Mannheim, Mannheim, Germany;Technion - Israel Institute of Technology, Haifa, Israel;Wirtschaftsuniversität Wien, Vienna, Austria;Eindhoven University of Technology, Eindhoven, The Netherlands;Universität Mannheim, Mannheim, Germany

  • Venue:
  • BPM'12 Proceedings of the 10th international conference on Business Process Management
  • Year:
  • 2012

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Abstract

Business process models are increasingly used by companies, often yielding repositories of several thousand models. These models are of great value for business analysis such as service identification or process standardization. A problem is though that many of these analyses require the pairwise comparison of process models, which is hardly feasible to do manually given an extensive number of models. While the computation of similarity between a pair of process models has been intensively studied in recent years, there is a notable gap on automatically matching activities of two process models. In this paper, we develop an approach based on semantic techniques and probabilistic optimization. We evaluate our approach using a sample of admission processes from different universities.