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2017 | OriginalPaper | Chapter

Characterizing Drift from Event Streams of Business Processes

Authors : Alireza Ostovar, Abderrahmane Maaradji, Marcello La Rosa, Arthur H. M. ter Hofstede

Published in: Advanced Information Systems Engineering

Publisher: Springer International Publishing

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Abstract

Early detection of business process drifts from event logs enables analysts to identify changes that may negatively affect process performance. However, detecting a process drift without characterizing its nature is not enough to support analysts in understanding and rectifying process performance issues. We propose a method to characterize process drifts from event streams, in terms of the behavioral relations that are modified by the drift. The method builds upon a technique for online drift detection, and relies on a statistical test to select the behavioral relations extracted from the stream that have the highest explanatory power. The selected relations are then mapped to typical change patterns to explain the detected drifts. An extensive evaluation on synthetic and real-life logs shows that our method is fast and accurate in characterizing process drifts, and performs significantly better than alternative techniques.

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previous chapter Enriching Decision Making with Data-Based Thresholds of Process-Related KPIs

next chapter Massively Distributed Environments and Closed Itemset Mining: The DCIM Approach

Matching a template of k relations to a drift feature set of \(|\mathcal {L}|^2\) relations requires iterating over all possible permutations (

https://static-content.springer.com/image/chp%3A10.1007%2F978-3-319-59536-8_14/450866_1_En_14_IEq230_HTML.gif

). The upper-bound complexity of this operation is \(O(|\mathcal {L}|^{2}!)\). Next, to identify the best-matching template, we iterate over the number of predefined templates m. Finally, we need to match simultaneous changes which in the worse case are \(|\mathcal {L}|^2\) (where each template has only one relation). The upper-bound time complexity of identifying multiple non-overlapping templates is \(O(|\mathcal {L}|^2 \cdot ~m \cdot |\mathcal {L}|^{2}!)\).

Available at http://apromore.org/platform/tools.

http://cpntools.org.

All the CPN models used for this simulation, the resulting synthetic logs, and the detailed evaluation results are available with the software distribution.

http://dx.doi.org/10.4121/uuid:d9769f3d-0ab0-4fb8-803b-0d1120ffcf54.

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Title: Characterizing Drift from Event Streams of Business Processes
Authors: Alireza Ostovar
Abderrahmane Maaradji
Marcello La Rosa
Arthur H. M. ter Hofstede
Publisher: Springer International Publishing
Book: Advanced Information Systems Engineering
Print ISBN: 978-3-319-59535-1

Electronic ISBN: 978-3-319-59536-8

Copyright Year: 2017
DOI: https://doi.org/10.1007/978-3-319-59536-8_14

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