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Control-Flow Business Process Summarization via Activity Contraction

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Intelligent Data Engineering and Automated Learning – IDEAL 2019 (IDEAL 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11872))

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Abstract

Organizations collect and store considerable amounts of process data in event logs that are subsequently mined to obtain process models. When the business process involves hundreds of activities, executed according to complex execution patterns, the process model can become too large and complex to identify relevant information by manual and visual inspection only. Summarization techniques can help, by providing concise and meaningful representations of the underling process. This paper describes a business process summarization algorithm based on the hierarchical grouping of activities. In the proposed approach, activity grouping is guided by the existence of some relations, between pairs of activities, mined from the associated event log.

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Notes

  1. 1.

    The reader is pointed to [4] for a comprehensive survey on graph summarization techniques including also other categories.

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Author information

Authors and Affiliations

  1. DeMaCS, University of Calabria, via Pietro Bucci 30B, 87036, Rende, CS, Italy

    Valeria Fionda & Gianluigi Greco

Authors
  1. Valeria Fionda
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  2. Gianluigi Greco
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Corresponding author

Correspondence to Valeria Fionda .

Editor information

Editors and Affiliations

  1. University of Manchester, Manchester, UK

    Hujun Yin

  2. Technical University of Madrid, Madrid, Spain

    David Camacho

  3. University of Birmingham, Birmingham, UK

    Peter Tino

  4. University of Huelva, Huelva, Spain

    Antonio J. Tallón-Ballesteros

  5. University of Exeter, Exeter, UK

    Ronaldo Menezes

  6. University of Manchester, Manchester, UK

    Richard Allmendinger

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Fionda, V., Greco, G. (2019). Control-Flow Business Process Summarization via Activity Contraction. In: Yin, H., Camacho, D., Tino, P., Tallón-Ballesteros, A., Menezes, R., Allmendinger, R. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2019. IDEAL 2019. Lecture Notes in Computer Science(), vol 11872. Springer, Cham. https://doi.org/10.1007/978-3-030-33617-2_25

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  • DOI: https://doi.org/10.1007/978-3-030-33617-2_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33616-5

  • Online ISBN: 978-3-030-33617-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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