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European Conference on Advances in Databases and Information Systems

ADBIS 2019: New Trends in Databases and Information Systems pp 391–404Cite as

The Impact of Event Log Subset Selection on the Performance of Process Discovery Algorithms

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1064))

Abstract

Process discovery algorithms automatically discover process models on the basis of event data, captured during the execution of business processes. These algorithms tend to use all of the event data to discover a process model. When dealing with large event logs, it is no longer feasible using standard hardware in limited time. A straightforward approach to overcome this problem is to down-size the event data by means of sampling. However, little research has been conducted on selecting the right sample, given the available time and characteristics of event data. This paper evaluates various subset selection methods and evaluates their performance on real event data. The proposed methods have been implemented in both the ProM and the RapidProM platforms. Our experiments show that it is possible to speed up discovery considerably using ranking-based strategies. Furthermore, results show that biased selection of the process instances compared to random selection of them will result in process models with higher quality.

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Notes

  1. 1.

    Here, we select only one trace for each variant.

  2. 2.

    Sample Variant plug-in in: https://svn.win.tue.nl/repos/prom/Packages/LogFiltering.

  3. 3.

    https://data.4tu.nl/repository/collection:event_logs_real.

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Author information

Authors and Affiliations

  1. Process and Data Science Chair, RWTH Aachen University, Aachen, Germany

    Mohammadreza Fani Sani, Sebastiaan J. van Zelst & Wil M. P. van der Aalst

  2. Fraunhofer FIT, Birlinghoven Castle, Sankt Augustin, Germany

    Sebastiaan J. van Zelst & Wil M. P. van der Aalst

Authors
  1. Mohammadreza Fani Sani
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  2. Sebastiaan J. van Zelst
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  3. Wil M. P. van der Aalst
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Corresponding author

Correspondence to Mohammadreza Fani Sani .

Editor information

Editors and Affiliations

  1. University of Maribor, Maribor, Slovenia

    Tatjana Welzer

  2. Alpen Adria University Klagenfurt, Klagenfurt am Wörthersee, Austria

    Johann Eder

  3. University of Maribor, Maribor, Slovenia

    Vili Podgorelec

  4. Poznan University of Technology, Poznan, Poland

    Robert Wrembel

  5. University of Novi Sad, Novi Sad, Serbia

    Mirjana Ivanović

  6. Free University of Bozen-Bolzano, Bolzano, Italy

    Johann Gamper

  7. Poznań University of Technology, Poznan, Poland

    Mikoƚaj Morzy

  8. University of Thessaly, Lamia, Greece

    Theodoros Tzouramanis

  9. Université Lumière Lyon 2, Lyon, France

    Jérôme Darmont

  10. University of Maribor, Maribor, Slovenia

    Aida Kamišalić Latifić

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Fani Sani, M., van Zelst, S.J., van der Aalst, W.M.P. (2019). The Impact of Event Log Subset Selection on the Performance of Process Discovery Algorithms. In: Welzer, T., et al. New Trends in Databases and Information Systems. ADBIS 2019. Communications in Computer and Information Science, vol 1064. Springer, Cham. https://doi.org/10.1007/978-3-030-30278-8_39

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  • DOI: https://doi.org/10.1007/978-3-030-30278-8_39

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-30277-1

  • Online ISBN: 978-3-030-30278-8

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