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.

Remaining time prediction (RTP) is the problem of predicting the time until a specific process step is reached in a specific process instance. Feature engineering in established RTP techniques assume that cases progress in isolation. Intercase dynamics such as batching violate this assumption, leading to high prediction errors. Yet, existing RTP techniques do not consider the nature of prediction errors to improve quality. We contribute a technique for identifying the location and context of prediction errors by visually comparing prediction and ground truth. For the case of batching, we show how to engineer inter-case features that detail the impact of batching on the remaining time. Our evaluation shows that adding intercase features improves prediction performance across almost all evaluated primary prediction methods on two real-life event logs, with error reductions of up to 37%. We finally advocate for a more thorough and transparent evaluation of prediction errors in RTP research, including our own results.