A Survey on Concept Drift in Process Mining

Concept drift in process mining (PM) is a challenge as classical methods assume processes are in a steady-state, i.e., events share the same process version. We conducted a systematic literature review on the intersection of these areas, and thus, we review concept drift in PM and bring forward a taxonomy of existing techniques for drift detection and online PM for evolving environments. Existing works depict that (i) PM still primarily focuses on offline analysis, and (ii) the assessment of concept drift techniques in processes is cumbersome due to the lack of common evaluation protocol, datasets, and metrics.

• Publication year

  2021

• Venue

  ACM Computing Surveys

• Publication date

  2021-10-07

• Fields of study

  Computer Science, Engineering

• Identifiers
  DOI 10.1145/3472752arXiv 2112.02000
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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