Filtering spurious events from event streams of business processes

Sebastiaan J. van Zelst, Mohammadreza Fani Sani, Alireza Ostovar, Raffaele Conforti, Marcello La Rosa

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

23 Citations (Scopus)


Process mining aims at gaining insights into business processes by analysing event data recorded during process execution. The majority of existing process mining techniques works offline, i.e. using static, historical data stored in event logs. Recently, the notion of online process mining has emerged, whereby techniques are applied on live event streams, as process executions unfold. Analysing event streams allows us to gain instant insights into business processes. However, current techniques assume the input stream to be completely free of noise and other anomalous behaviours. Hence, applying these techniques to real data leads to results of inferior quality. In this paper, we propose an event processor that enables us to filter out spurious events from a live event stream. Our experiments show that we are able to effectively filter out spurious events from the input stream and, as such, enhance online process mining results.

Original languageEnglish
Title of host publicationAdvanced Information Systems Engineering - 30th International Conference, CAiSE 2018, Proceedings
Number of pages18
ISBN (Print)9783319915623
Publication statusPublished - 1 Jan 2018
Event30th International Conference on Advanced Information Systems Engineering, CAiSE 2018 - Tallinn, Estonia
Duration: 11 Jun 201815 Jun 2018
Conference number: 30

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10816 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference30th International Conference on Advanced Information Systems Engineering, CAiSE 2018
Abbreviated titleCAiSE 2018
Internet address


  • Anomaly detection
  • Event stream
  • Filtering
  • Process mining


Dive into the research topics of 'Filtering spurious events from event streams of business processes'. Together they form a unique fingerprint.

Cite this