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International Conference on Mining Intelligence and Knowledge Exploration

MIKE 2016: Mining Intelligence and Knowledge Exploration pp 164–173Cite as

Inferring the Repetitive Behaviour from Event Logs for Process Mining Discovery

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10089))

Abstract

This paper addresses the problem of discovering a sound Workflow net (WFN) from event traces representing the behavior of a discrete event process. A novel and efficient method for inferring the repetitive behaviour in a workflow log is proposed. It is based on an iterative search and filtering of cycles computed in each trace; a graph of causal relations is built for each cycle, which helps to find the supports of the t-invariants of an extended WFN. The t-invariants are used for determining causal and concurrent relations between events, allowing building the WFN efficiently in a complete discovery technique.

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Authors and Affiliations

  1. CINVESTAV Unidad Guadalajara, Av. del Bosque 1145, Col. El Bajio, 45015, Zapopan, Mexico

    Tonatiuh Tapia-Flores & Ernesto López-Mellado

Authors
  1. Tonatiuh Tapia-Flores
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  2. Ernesto López-Mellado
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Corresponding author

Correspondence to Tonatiuh Tapia-Flores .

Editor information

Editors and Affiliations

  1. Norwegian University of Science and Technology, Trondheim, Norway

    Rajendra Prasath

  2. Center for Computing Research, CIC, National Polytechnic Institute, IPN, Mexico City, Distrito Federal, Mexico

    Alexander Gelbukh

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Tapia-Flores, T., López-Mellado, E. (2017). Inferring the Repetitive Behaviour from Event Logs for Process Mining Discovery. In: Prasath, R., Gelbukh, A. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2016. Lecture Notes in Computer Science(), vol 10089. Springer, Cham. https://doi.org/10.1007/978-3-319-58130-9_16

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  • DOI: https://doi.org/10.1007/978-3-319-58130-9_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-58129-3

  • Online ISBN: 978-3-319-58130-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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