Improving the Performance of Process Discovery Algorithms by Instance Selection

Mohammadreza Fani Sani1, Sebastiaan J. van Zelst1, 2 and Wil van der Aalst1, 2

  1. Process and Data Science Chair, RWTH Aachen University
    Aachen, Germany
    {fanisani, s.j.v.zelst, wvdaalst}@pads.rwth-aachen.de
  2. Fraunhofer FIT, Birlinghoven Castle
    Sankt Augustin, Germany

Abstract

Process discovery algorithms automatically discover process models based on event data that is 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 considerably speed up discovery using instance selection strategies. Furthermore, results show that applying biased selection of the process instances compared to random sampling will result in simpler process models with higher quality.

Key words

Process Mining, Process Discovery, Subset Selection, Event Log Preprocessing, Performance Enhancement

Digital Object Identifier (DOI)

https://doi.org/https://doi.org/10.2298/CSIS200127028S

Publication information

Volume 17, Issue 3 (October 2020)
Year of Publication: 2020
ISSN: 1820-0214 (Print) 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Sani, M. F., Zelst, S. J. v., Aalst, W. v. d.: Improving the Performance of Process Discovery Algorithms by Instance Selection. Computer Science and Information Systems, Vol. 17, No. 3, 927–958. (2020), https://doi.org/https://doi.org/10.2298/CSIS200127028S