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A Method to Improve Workflow Net Decomposition for Process Model Repair

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Analysis of Images, Social Networks and Texts (AIST 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11832))

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Abstract

Creating a process model (PM) is a convenient means to depict the behavior of a particular information system. However, user behavior is not static and tends to change over time. In order for them to sustain relevant, PMs have to be adjusted to ever-changing behavior. Sometimes the existing PM may be of high value (e.g. it is well-structured, or has been developed continuously by experts to later work with), which makes the approach to create a brand-new model using discovery algorithms less preferable. In this case, a different and better suitable approach to adjust PM to new behavior is to work with an existing model through repairing only such PM fragments that do not fit the actual behavior stated in sub-log. This article is to present a method for efficient decomposition of PMs for their future repair. It aims to improve the accuracy of model repair. Unlike the ones introduced earlier, this algorithm suggests finding the minimum spanning tree of undirected graph’s vertices subset. It helps to reduce the size of a fragment to be repaired in a model and enhances the quality of a repaired model according to various conformance metrics.

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

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Authors and Affiliations

  1. National Research University Higher School of Economics, 20 Myasnitskaya St., 101000, Moscow, Russia

    Semyon E. Tikhonov & Alexey A. Mitsyuk

Authors
  1. Semyon E. Tikhonov
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  2. Alexey A. Mitsyuk
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Corresponding author

Correspondence to Semyon E. Tikhonov .

Editor information

Editors and Affiliations

  1. RWTH Aachen University, Aachen, Germany

    Wil M. P. van der Aalst

  2. University of Ljubljana, Ljubljana, Slovenia

    Vladimir Batagelj

  3. National Research University Higher School of Economics, Moscow, Russia

    Dmitry I. Ignatov

  4. Krasovskii Institute of Mathematics and Mechanics, Yekaterinburg, Russia

    Michael Khachay

  5. National Research University Higher School of Economics, Moscow, Russia

    Valentina Kuskova

  6. University of Oslo, Oslo, Norway

    Andrey Kutuzov

  7. National Research University Higher School of Economics, Moscow, Russia

    Sergei O. Kuznetsov

  8. National Research University Higher School of Economics, Moscow, Russia

    Irina A. Lomazova

  9. Lomonosov Moscow State University, Moscow, Russia

    Natalia Loukachevitch

  10. LORIA, Vandœuvre-lès-Nancy, France

    Amedeo Napoli

  11. University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

  12. Ca Foscari University of Venice, Venice, Italy

    Marcello Pelillo

  13. National Research University Higher School of Economics, Nizhny Novgorod, Russia

    Andrey V. Savchenko

  14. Kazan Federal University, Kazan, Russia

    Elena Tutubalina

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Tikhonov, S.E., Mitsyuk, A.A. (2019). A Method to Improve Workflow Net Decomposition for Process Model Repair. In: van der Aalst, W., et al. Analysis of Images, Social Networks and Texts. AIST 2019. Lecture Notes in Computer Science(), vol 11832. Springer, Cham. https://doi.org/10.1007/978-3-030-37334-4_37

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  • DOI: https://doi.org/10.1007/978-3-030-37334-4_37

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  • Online ISBN: 978-3-030-37334-4

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