Repairing outlier behaviour in event logs

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

25 Citations (Scopus)


One of the main challenges in applying process mining on real event data, is the presence of noise and rare behaviour. Applying process mining algorithms directly on raw event data typically results in complex, incomprehensible, and, in some cases, even inaccurate analyses. As a result, correct and/or important behaviour may be concealed. In this paper, we propose an event data repair method, that tries to detect and repair outlier behaviour within the given event data. We propose a probabilistic method that is based on the occurrence frequency of activities in specific contexts. Our approach allows for removal of infrequent behaviour, which enables us to obtain a more global view of the process. The proposed method has been implemented in both the ProM- and the RapidProM framework. Using these implementations, we conduct a collection of experiments that show that we are able to detect and modify most types of outlier behaviour in the event data. Our evaluation clearly demonstrates that we are able to help to improve process mining discovery results by repairing event logs upfront.

Original languageEnglish
Title of host publicationBusiness Information Systems - 21st International Conference, BIS 2018, Proceedings
EditorsW. Abramowicz, A. Paschke
Place of PublicationCham
Number of pages17
ISBN (Electronic)978-3-319-93931-5
ISBN (Print)978-3-319-93930-8
Publication statusPublished - 1 Jan 2018
Event21st International Conference on Business Information Systems, (BIS 2018) - Berlin, Germany
Duration: 18 Jul 201820 Jul 2018

Publication series

NameLecture Notes in Business Information Processing
ISSN (Print)1865-1348


Conference21st International Conference on Business Information Systems, (BIS 2018)
Abbreviated titleBIS2018
Internet address


  • Conditional probability
  • Data cleansing
  • Event log preprocessing
  • Log repair
  • Outlier detection
  • Process mining


Dive into the research topics of 'Repairing outlier behaviour in event logs'. Together they form a unique fingerprint.

Cite this