Predicting Deadline Transgressions Using Event Logs

  • Anastasiia Pika
  • Wil M. P. van der Aalst
  • Colin J. Fidge
  • Arthur H. M. ter Hofstede
  • Moe T. Wynn
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 132)

Abstract

Effective risk management is crucial for any organisation. One of its key steps is risk identification, but few tools exist to support this process. Here we present a method for the automatic discovery of a particular type of process-related risk, the danger of deadline transgressions or overruns, based on the analysis of event logs. We define a set of time-related process risk indicators, i.e., patterns observable in event logs that highlight the likelihood of an overrun, and then show how instances of these patterns can be identified automatically using statistical principles. To demonstrate its feasibility, the approach has been implemented as a plug-in module to the process mining framework ProM and tested using an event log from a Dutch financial institution.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Anastasiia Pika
    • 1
  • Wil M. P. van der Aalst
    • 2
    • 1
  • Colin J. Fidge
    • 1
  • Arthur H. M. ter Hofstede
    • 1
    • 2
  • Moe T. Wynn
    • 1
  1. 1.Queensland University of TechnologyBrisbaneAustralia
  2. 2.Eindhoven University of TechnologyEindhovenThe Netherlands

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