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Process Model Discovery: A Method Based on Transition System Decomposition

  • Anna A. Kalenkova
  • Irina A. Lomazova
  • Wil M. P. van der Aalst
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8489)

Abstract

Process mining aims to discover and analyze processes by extracting information from event logs. Process mining discovery algorithms deal with large data sets to learn automatically process models. As more event data become available there is the desire to learn larger and more complex process models. To tackle problems related to the readability of the resulting model and to ensure tractability, various decomposition methods have been proposed. This paper presents a novel decomposition approach for discovering more readable models from event logs on the basis of a priori knowledge about the event log structure: regular and special cases of the process execution are treated separately. The transition system, corresponding to a given event log, is decomposed into a regular part and a specific part. Then one of the known discovery algorithms is applied to both parts, and finally these models are combined into a single process model. It is proven, that the structural and behavioral properties of submodels are inherited by the unified process model. The proposed discovery algorithm is illustrated using a running example.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Anna A. Kalenkova
    • 1
  • Irina A. Lomazova
    • 1
  • Wil M. P. van der Aalst
    • 1
    • 2
  1. 1.National Research University Higher School of Economics (HSE)MoscowRussia
  2. 2.Eindhoven University of TechnologyEindhovenThe Netherlands

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