Decomposing Process Performance based on Actor Behavior

Process performance analysis based on event logs is a core task of process mining. Typical tools enrich a directly-follows graph with statistics on waiting times between activities. Such projection may reveal process issues that manifest as a high average waiting time between activities. However, the purely control-flow-oriented view disregards the influence of actor behavior on process performance and may lead to a distorted analysis. Typically, projected measures aggregate the waiting time it takes for disparate types of actor behavior to a single measure: a direct continuation of the work by the same actor, a continuation of the work by the same actor after being interrupted by another case, or a handover to another actor. For a handover, the receiving actor may decide to prioritize activities in other cases before starting the work. Hence, two similar waiting time measures may imply very different dynamics of the actors’ behavior. The paper contributes a method to systematically decompose the regular control-flow performance measure into more fine-grained performance measures based on such behavioral mechanisms of actors. We leverage event knowledge graphs as a joint representation of actor and control flow perspectives to derive features for the behavioral mechanisms and systematically analyze them. The evaluation of the features on a loan application process shows that they provide clearly interpretable performance insight compared to the potentially misleading average waiting times.