Detecting drift from event streams of unpredictable business processes

Ostovar, Alireza, Maaradji, Abderrahmane, La Rosa, Marcello, ter Hofstede, Arthur H. M., & van Dongen, Boudewijn (2016) Detecting drift from event streams of unpredictable business processes.



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.

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25 since deposited on 03 May 2016
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ID Code: 95322
Item Type: Report
Refereed: No
Keywords: concept drift, process drift, process mining, business process management
Divisions: Current > Schools > School of Information Systems
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Facilities: Science and Engineering Centre
Copyright Owner: Copyright 2016 The Authors
Deposited On: 03 May 2016 03:46
Last Modified: 03 May 2016 16:31

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