Comparing concept drift detection with process mining tools

Organisations have seen a rise in the volume of data correspondingto business processes being recorded. Handling process data is ameaningful way to extract relevant information from business processes with impact on the company's values. Nonetheless, businessprocesses are subject to changes during their executions, addingcomplexity to their analysis. This paper aims at evaluating currently available Process Mining tools that handle concept drifts, i.e.changes over time of the statistical properties of the events occurring in a process. We provide an in-depth analysis of these toolsbriefly comparing their differences, advantages, and disadvantages.