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Towards Process Mining with Graph Transformation Systems

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Graph Transformation (ICGT 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8571))

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Abstract

This paper is about process mining with graph transformation systems (gtss). Given a set of observed transition sequences, the goal is to find a gts – that is a finite set of graph transformation rules – that models these transition sequences as well as possible. In this paper the focus is on real-word processes such as business processes or (human) problem solving strategies, with the aim of better understanding such processes. The observed behaviour is not assumed to be either complete or error-free and the given model is expected to generalize the observed behaviour and be robust to erroneous input. The paper presents some basic algorithms that obtain gtss from observed transition sequences and gives a method to compare the resulting gtss.

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Author information

Authors and Affiliations

  1. Universität Duisburg-Essen, Germany

    H. J. Sander Bruggink

Authors
  1. H. J. Sander Bruggink
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Editors and Affiliations

  1. Hasso Plattner Institut an der Universität Potsdam, Prof.-Dr.-Helmert-Strasse 2-3, 14482, Potsdam, Germany

    Holger Giese

  2. Fakultät für Ingenieurwissenschaften, Abteilung für Informatik und Angewandte Kognitionswissenscaft, Universität Duisburg-Essen, Lotharstraße 65, 47057, Duisburg, Germany

    Barbara König

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© 2014 Springer International Publishing Switzerland

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Bruggink, H.J.S. (2014). Towards Process Mining with Graph Transformation Systems. In: Giese, H., König, B. (eds) Graph Transformation. ICGT 2014. Lecture Notes in Computer Science, vol 8571. Springer, Cham. https://doi.org/10.1007/978-3-319-09108-2_17

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  • DOI: https://doi.org/10.1007/978-3-319-09108-2_17

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-09107-5

  • Online ISBN: 978-3-319-09108-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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