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Designing and Evaluating an Interpretable Predictive Modeling Technique for Business Processes

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Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 202))

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Abstract

Process mining is a field traditionally concerned with retrospective analysis of event logs, yet interest in applying it online to running process instances is increasing. In this paper, we design a predictive modeling technique that can be used to quantify probabilities of how a running process instance will behave based on the events that have been observed so far. To this end, we study the field of grammatical inference and identify suitable probabilistic modeling techniques for event log data. After tailoring one of these techniques to the domain of business process management, we derive a learning algorithm. By combining our predictive model with an established process discovery technique, we are able to visualize the significant parts of predictive models in form of Petri nets. A preliminary evaluation demonstrates the effectiveness of our approach.

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Authors and Affiliations

  1. Department for Information Systems, Leonardo-Campus 3, 48149, Muenster, Germany

    Dominic Breuker, Patrick Delfmann, Martin Matzner & Jörg Becker

Authors
  1. Dominic Breuker
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  2. Patrick Delfmann
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  3. Martin Matzner
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  4. Jörg Becker
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Corresponding author

Correspondence to Dominic Breuker .

Editor information

Editors and Affiliations

  1. IBM Research – Haifa, Haifa, Israel

    Fabiana Fournier

  2. Vienna University of Economics and Business, Vienna, Austria

    Jan Mendling

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Breuker, D., Delfmann, P., Matzner, M., Becker, J. (2015). Designing and Evaluating an Interpretable Predictive Modeling Technique for Business Processes. In: Fournier, F., Mendling, J. (eds) Business Process Management Workshops. BPM 2014. Lecture Notes in Business Information Processing, vol 202. Springer, Cham. https://doi.org/10.1007/978-3-319-15895-2_46

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  • DOI: https://doi.org/10.1007/978-3-319-15895-2_46

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-15894-5

  • Online ISBN: 978-3-319-15895-2

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