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Indulpet Miner: Combining Discovery Algorithms

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On the Move to Meaningful Internet Systems. OTM 2018 Conferences (OTM 2018)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 11229))

Abstract

In this work, we explore an approach to process discovery that is based on combining several existing process discovery algorithms. We focus on algorithms that generate process models in the process tree notation, which are sound by design. The main components of our proposed process discovery approach are the Inductive Miner, the Evolutionary Tree Miner, the Local Process Model Miner and a new bottom-up recursive technique. We conjecture that the combination of these process discovery algorithms can mitigate some of the weaknesses of the individual algorithms. In cases where the Inductive Miner results in overgeneralizing process models, the Evolutionary Tree Miner can often mine much more precise models. At the other hand, while the Evolutionary Tree Miner is computationally expensive, running it only on parts of the log that the Inductive Miner is not able to represent with a precise model fragment can considerably limit the search space size of the Evolutionary Tree Miner. Local Process Models and bottom-up recursion aid the Evolutionary Tree Miner further by instantiating it with frequent process model fragments. We evaluate our approaches on a collection of real-life event logs and find that it does combine the advantages of the miners and in some cases surpasses other discovery techniques.

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Notes

  1. 1.

    Lifecycle information handling capabilities are necessary for Indulpet as the bottom-up recursion might insert such information in the event log, even if it was not present in the input event log.

  2. 2.

    For simplicity, we use only binary partial cuts in this paper. The definitions extend to n-ary partial cuts and this does not change the expressivity of the method [18].

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Acknowledgement

We thank Joos Buijs for his help in integrating the Evolutionary Tree Miner (ETM) with the Indulpet Miner, and Eric Verbeek for coming up with its name.

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Authors and Affiliations

  1. Queensland University of Technology, Brisbane, Australia

    Sander J. J. Leemans & Arthur H. M. ter Hofstede

  2. Eindhoven University of Technology, Eindhoven, The Netherlands

    Niek Tax

Authors
  1. Sander J. J. Leemans
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  2. Niek Tax
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  3. Arthur H. M. ter Hofstede
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Corresponding author

Correspondence to Sander J. J. Leemans .

Editor information

Editors and Affiliations

  1. CNRS, University of Lorraine, Vandoeuvre-les-Nancy, France

    Hervé Panetto

  2. Trinity College Dublin, Dublin, Ireland

    Christophe Debruyne

  3. Luxembourg Institute of Science and Technology, Esch-sur-Alzette, Luxembourg

    Henderik A. Proper

  4. Università degli Studi di Milano, Crema, Italy

    Claudio Agostino Ardagna

  5. SINTEF and University of Oslo, Oslo, Norway

    Dumitru Roman

  6. TU Graz, Graz, Austria

    Robert Meersman

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Leemans, S.J.J., Tax, N., ter Hofstede, A.H.M. (2018). Indulpet Miner: Combining Discovery Algorithms. In: Panetto, H., Debruyne, C., Proper, H., Ardagna, C., Roman, D., Meersman, R. (eds) On the Move to Meaningful Internet Systems. OTM 2018 Conferences. OTM 2018. Lecture Notes in Computer Science(), vol 11229. Springer, Cham. https://doi.org/10.1007/978-3-030-02610-3_6

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  • DOI: https://doi.org/10.1007/978-3-030-02610-3_6

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  • Print ISBN: 978-3-030-02609-7

  • Online ISBN: 978-3-030-02610-3

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