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Filtering Spurious Events from Event Streams of Business Processes

  • Sebastiaan J. van ZelstEmail author
  • Mohammadreza Fani Sani
  • Alireza Ostovar
  • Raffaele Conforti
  • Marcello La Rosa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10816)

Abstract

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.

Keywords

Process mining Event stream Filtering Anomaly detection 

Notes

Acknowledgments

This research is funded by the Australian Research Council (grant DP150103356), and the DELIBIDA research program supported by NWO.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Sebastiaan J. van Zelst
    • 1
    Email author
  • Mohammadreza Fani Sani
    • 2
  • Alireza Ostovar
    • 3
  • Raffaele Conforti
    • 4
  • Marcello La Rosa
    • 4
  1. 1.Eindhoven University of TechnologyEindhovenThe Netherlands
  2. 2.RWTH Aachen UniversityAachenGermany
  3. 3.Queensland University of TechnologyBrisbaneAustralia
  4. 4.University of MelbourneMelbourneAustralia

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