Detecting Drift from Event Streams of Unpredictable Business Processes

  • Alireza OstovarEmail author
  • Abderrahmane Maaradji
  • Marcello La Rosa
  • Arthur H. M. ter Hofstede
  • Boudewijn F. V. van Dongen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9974)


Existing business process drift detection methods do not work with event streams. As such, they are designed to detect inter-trace drifts only, i.e. drifts that occur between complete process executions (traces), as recorded in event logs. However, process drift may also occur during the execution of a process, and may impact ongoing executions. Existing methods either do not detect such intra-trace drifts, or detect them with a long delay. Moreover, they do not perform well with unpredictable processes, i.e. processes whose logs exhibit a high number of distinct executions to the total number of executions. We address these two issues by proposing a fully automated and scalable method for online detection of process drift from event streams. We perform statistical tests over distributions of behavioral relations between events, as observed in two adjacent windows of adaptive size, sliding along with the stream. An extensive evaluation on synthetic and real-life logs shows that our method is fast and accurate in the detection of typical change patterns, and performs significantly better than the state of the art.


Window Size Business Process Change Pattern Concept Drift Event Stream 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This research is partly funded by the Australian Research Council (grant DP150103356).


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Alireza Ostovar
    • 1
    Email author
  • Abderrahmane Maaradji
    • 1
  • Marcello La Rosa
    • 1
  • Arthur H. M. ter Hofstede
    • 1
    • 2
  • Boudewijn F. V. van Dongen
    • 2
  1. 1.Queensland University of TechnologyBrisbaneAustralia
  2. 2.Eindhoven University of TechnologyEindhovenThe Netherlands

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