Improving the Performance of Process Discovery Algorithms by Instance Selection

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

Research output: Contribution to journalArticleAcademicpeer-review

11 Citations (Scopus)


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.

Keywords: process mining, process discovery, subset selection, event log preprocessing, performance enhancement
Original languageEnglish
Pages (from-to)927-958
Number of pages32
JournalComputer Science and Information Systems
Issue number3
Publication statusPublished - Oct 2020


FundersFunder number
Alexander von Humboldt-Stiftung


    • Event Log Pre-processing
    • Performance Enhancement
    • Process Discovery
    • Process Mining
    • Subset Selection


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