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Process Model Discovery: A Method Based on Transition System Decomposition

  • Conference paper
Application and Theory of Petri Nets and Concurrency (PETRI NETS 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,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.

This work is supported by the Basic Research Program of the National Research University Higher School of Economics.

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Author information

Authors and Affiliations

  1. National Research University Higher School of Economics (HSE), Moscow, 101000, Russia

    Anna A. Kalenkova, Irina A. Lomazova & Wil M. P. van der Aalst

  2. Eindhoven University of Technology, P.O. Box 513, NL-5600 MB, Eindhoven, The Netherlands

    Wil M. P. van der Aalst

Authors
  1. Anna A. Kalenkova
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  2. Irina A. Lomazova
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  3. Wil M. P. van der Aalst
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Editor information

Editors and Affiliations

  1. Department of Computer Science, Iowa State University, 226 Atanasoff Hall, 50011, Ames, IA, USA

    Gianfranco Ciardo

  2. DTU Compute, Technical University of Denmark, Richard Pedersens Plads, 2800 Kgs., Lyngby, Denmark

    Ekkart Kindler

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Kalenkova, A.A., Lomazova, I.A., van der Aalst, W.M.P. (2014). Process Model Discovery: A Method Based on Transition System Decomposition. In: Ciardo, G., Kindler, E. (eds) Application and Theory of Petri Nets and Concurrency. PETRI NETS 2014. Lecture Notes in Computer Science, vol 8489. Springer, Cham. https://doi.org/10.1007/978-3-319-07734-5_5

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  • DOI: https://doi.org/10.1007/978-3-319-07734-5_5

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07733-8

  • Online ISBN: 978-3-319-07734-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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