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Mining Reference Process Models from Large Instance Data

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Business Process Management Workshops (BPM 2016)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 281))

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Abstract

Reference models provide generic blueprints of process models that are common in a certain industry. When designing a reference model, stakeholders have to cope with the so-called ‘dilemma of reference modeling’, viz., balancing generality against market specificity. In principle, the more details a reference model contains, the fewer situations it applies to. To overcome this dilemma, the contribution at hand presents a novel approach to mining a reference model hierarchy from large instance-level data such as execution logs. It combines an execution-semantic technique for reference model development with a hierarchical-agglomerative cluster analysis and ideas from Process Mining. The result is a reference model hierarchy, where the lower a model is located, the smaller its scope, and the higher its level of detail. The approach is implemented as proof-of-concept and applied in an extensive case study, using the data from the 2015 BPI Challenge.

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Notes

  1. 1.

    http://refmod-miner.dfki.de/.

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Acknowledgments

The research described in this paper was partly supported by a grant from the German Research Foundation (DFG), project name: Konzeptionelle, methodische und technische Grundlagen zur induktiven Erstellung von Referenzmodellen (Reference Model Mining), support code GZ LO 752/5-1.

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Authors and Affiliations

  1. Institute for Information Systems (IWi), German Research Center for Artificial Intelligence (DFKI GmbH) and Saarland University, Campus D3 2, 66123, Saarbruecken, Germany

    Jana-Rebecca Rehse & Peter Fettke

Authors
  1. Jana-Rebecca Rehse
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  2. Peter Fettke
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Correspondence to Jana-Rebecca Rehse .

Editor information

Editors and Affiliations

  1. Institute of Computer Science, University of Tartu, Tartu, Estonia

    Marlon Dumas

  2. School of Arts, Sciences and Humanities, University of São Paulo, Sao Paulo, São Paulo, Brazil

    Marcelo Fantinato

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Rehse, JR., Fettke, P. (2017). Mining Reference Process Models from Large Instance Data. In: Dumas, M., Fantinato, M. (eds) Business Process Management Workshops. BPM 2016. Lecture Notes in Business Information Processing, vol 281. Springer, Cham. https://doi.org/10.1007/978-3-319-58457-7_1

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  • DOI: https://doi.org/10.1007/978-3-319-58457-7_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-58456-0

  • Online ISBN: 978-3-319-58457-7

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