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International Conference on Enterprise Information Systems

ICEIS 2018: Enterprise Information Systems pp 396–423Cite as

Big Data Meets Process Science: Distributed Mining of MP-Declare Process Models

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Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 363))

Abstract

Process mining techniques allow the user to build a process model representing the process behavior as recorded in the logs. Standard process discovery techniques produce as output a procedural process model. Recently, several approaches have been developed to extract declarative process models from logs and have been proven to be more suitable to analyze flexible processes, which frequently depend on human decisions and are less predictable. However, when analyzing declarative constraints from other perspective than the control flow, such as data and resources, existing process mining techniques turned out to be inefficient. Thus, computational performance remains a key challenge of declarative process discovery. In this paper, we present a high-performance approach for the discovery of multi-perspective declarative process models that is built upon the distributed big data processing method MapReduce. Compared to recent work we provide an in-depth analysis of an implementation approach based on Hadoop, a powerful BigData-Framework, and describe detailed information on the implemented prototype. We evaluated effectiveness and efficiency of the approach on real-life event logs.

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Notes

  1. 1.

    Round up for simplicity.

  2. 2.

    https://doi.org/10.4121/uuid:d9769f3d-0ab0-4fb8-803b-0d1120ffcf54.

  3. 3.

    https://doi.org/10.4121/uuid:270fd440-1057-4fb9-89a9-b699b47990f5.

  4. 4.

    https://doi.org/10.4121/uuid:31a308ef-c844-48da-948c-305d167a0ec1.

  5. 5.

    https://doi.org/10.4121/uuid:5f3067df-f10b-45da-b98b-86ae4c7a310b.

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Authors and Affiliations

  1. University of Bayreuth, Bayreuth, Germany

    Christian Sturm & Stefan Schönig

Authors
  1. Christian Sturm
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  2. Stefan Schönig
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Corresponding author

Correspondence to Christian Sturm .

Editor information

Editors and Affiliations

  1. MODESTE/ESEO, Angers, France

    Slimane Hammoudi

  2. Warsaw University of Technology, Warsaw, Poland

    Michał Śmiałek

  3. MODESTE/ESEO, Angers, France

    Olivier Camp

  4. INSTICC and Polytechnic Institute of Setúbal, Setúbal, Portugal

    Joaquim Filipe

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Sturm, C., Schönig, S. (2019). Big Data Meets Process Science: Distributed Mining of MP-Declare Process Models. In: Hammoudi, S., Śmiałek, M., Camp, O., Filipe, J. (eds) Enterprise Information Systems. ICEIS 2018. Lecture Notes in Business Information Processing, vol 363. Springer, Cham. https://doi.org/10.1007/978-3-030-26169-6_19

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  • DOI: https://doi.org/10.1007/978-3-030-26169-6_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-26168-9

  • Online ISBN: 978-3-030-26169-6

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