Abstract
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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Notes
- 1.
The G-test is a non-parametric hypothesis statistical test which assumes no a-priori knowledge of the statistical distributions. The G-test is a better approximation to the theoretical chi-squared distribution than the chi-squared test [12].
- 2.
The typical value of the threshold, i.e. significance level, for the G-test is 0.05 [13].
- 3.
Available at http://apromore.org/platform/tools.
- 4.
- 5.
All the CPN models used for this simulation, the resulting synthetic logs, and the detailed evaluation results are available with the software distribution.
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- 7.
In streaming settings, online noise filters such as the Kalman filter [16] could be used instead.
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This research is partly funded by the Australian Research Council (grant DP150103356).
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Ostovar, A., Maaradji, A., La Rosa, M., ter Hofstede, A.H.M., van Dongen, B.F.V. (2016). Detecting Drift from Event Streams of Unpredictable Business Processes. In: Comyn-Wattiau, I., Tanaka, K., Song, IY., Yamamoto, S., Saeki, M. (eds) Conceptual Modeling. ER 2016. Lecture Notes in Computer Science(), vol 9974. Springer, Cham. https://doi.org/10.1007/978-3-319-46397-1_26
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