Impact-Aware Conformance Checking

Abstract

Alignment-based conformance checking techniques detect and quantify deviations of process execution from expected behavior as depicted in process models. However, often when deviations occur, additional actions are needed to remedy and restore the process state. These would seem as further reducing conformance according to existing measures. This paper proposes a conformance checking approach which considers the response to unexpected deviations during process execution, by analyzing the data updates involved and their impact on the expected behavior. We evaluated our approach in an experimental study, whose results show that our approach better captures adapted behavior in response to deviations, as compared to standard fitness measurement.