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Mining Reference Process Models from Large Instance Data

  • Jana-Rebecca Rehse
  • Peter Fettke
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 281)

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.

Keywords

Reference Model Mining Dilemma of reference modeling Reference model hierarchy Inductive reference model development Trace clustering 

Notes

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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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Institute for Information Systems (IWi)German Research Center for Artificial Intelligence (DFKI GmbH) and Saarland UniversitySaarbrueckenGermany

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