  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 process mining and bring
forward a taxonomy of existing techniques for drift detection and online
process mining 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.
