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Explainable concept drift in process mining
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AI
DOI：10.1016/j.is.2023.102177.png)
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摘要
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The execution of processes leaves trails of event data in information systems. These event data are analyzed to generate insights and improvements for the underlying process. However, companies do not execute these processes in a vacuum. The fast pace of technological development, constantly changing market environments, and fast consumer responses expose companies to high levels of uncertainty. This uncertainty often manifests itself in significant changes in the executed processes. Such significant changes are called concept drifts. Transparency about concept drifts is crucial to respond quickly and adequately, limiting the potentially negative impact of such drifts. Three types of knowledge are of interest to a process owner: When did a drift occur, what happened, and why did it happen. This paper introduces a framework to extract concept drifts and their potential root causes from event data. We extract time series describing process measures, detect concept drifts, and test these drifts for correlation. This framework generalizes existing work such that object-centric event data with multiple case notions, non-linear relationships, and an arbitrary number of process measures are supported. We provide an extendable implementation and evaluate our framework concerning the sensitivity of the time series construction and scalability of cause-effect testing. Furthermore, we provide a case study uncovering an explainable concept drift.
Keyword:
Process mining
Concept drift
Cause-effect
Object-centric process mining
Explainability
期刊
IF:
3.9
论文数:
2.8K
被引数:
1.8K
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