Discovery of Fuzzy DMN Decision Models from Event Logs

  • Ekaterina Bazhenova
  • Stephan Haarmann
  • Sven Ihde
  • Andreas Solti
  • Mathias Weske
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10253)

Abstract

Successful business process management is highly dependent on effective decision making. The recent Decision Model and Notation (DMN) standard prescribes decisions to be documented and executed complementary to processes. However, the decision logic is often implicitly contained in event logs, and “as-is” decision knowledge needs to be retrieved. Commonly, decision logic is represented by rules based on Boolean algebra. The formal nature of such decisions is often hard for interpretation and utilization in practice, because imprecision is intrinsic to real-life decisions. Operations research considers fuzzy logic, based on fuzzy algebra, as a tool dealing with partial knowledge. In this paper, we explore the possibility of incorporating fuzziness into DMN decision models. Further, we propose a methodology for discovering fuzzy DMN decision models from event logs. The evaluation of our approach on a use case from the banking domain shows high comprehensibility and accuracy of the output decision model.

Keywords

Fuzzy logic Decision models Decision mining DMN Event logs 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Ekaterina Bazhenova
    • 1
  • Stephan Haarmann
    • 1
  • Sven Ihde
    • 1
  • Andreas Solti
    • 2
  • Mathias Weske
    • 1
  1. 1.Hasso Plattner Institute at the University of PotsdamPotsdamGermany
  2. 2.Vienna University of Economics and BusinessViennaAustria

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