A Framework for Explainable Concept Drift Detection in Process Mining
  Rapidly changing business environments expose companies to high levels of
uncertainty. This uncertainty manifests itself in significant changes that tend
to occur over the lifetime of a process and possibly affect its performance. It
is important to understand the root causes of such changes since this allows us
to react to change or anticipate future changes. Research in process mining has
so far only focused on detecting, locating and characterizing significant
changes in a process and not on finding root causes of such changes. In this
paper, we aim to close this gap. We propose a framework that adds an
explainability level onto concept drift detection in process mining and
provides insights into the cause-effect relationships behind significant
changes. We define different perspectives of a process, detect concept drifts
in these perspectives and plug the perspectives into a causality check that
determines whether these concept drifts can be causal to each other. We
showcase the effectiveness of our framework by evaluating it on both synthetic
and real event data. Our experiments show that our approach unravels
cause-effect relationships and provides novel insights into executed processes.
