Repairing outlier behaviour in event logs

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

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

25 Citaten (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.

Originele taal-2Engels
TitelBusiness Information Systems - 21st International Conference, BIS 2018, Proceedings
RedacteurenW. Abramowicz, A. Paschke
Plaats van productieCham
Aantal pagina's17
ISBN van elektronische versie978-3-319-93931-5
ISBN van geprinte versie978-3-319-93930-8
StatusGepubliceerd - 1 jan. 2018
Evenement21st International Conference on Business Information Systems, (BIS 2018) - Berlin, Duitsland
Duur: 18 jul. 201820 jul. 2018

Publicatie series

NaamLecture Notes in Business Information Processing
ISSN van geprinte versie1865-1348


Congres21st International Conference on Business Information Systems, (BIS 2018)
Verkorte titelBIS2018
Internet adres


Duik in de onderzoeksthema's van 'Repairing outlier behaviour in event logs'. Samen vormen ze een unieke vingerafdruk.

Citeer dit