TADE: Stochastic Conformance Checking Using Temporal Activity Density Estimation

In most processes, we have a strong demand for high conformance. We are interested in processes that work as designed, with as little deviations as possible. To assure this property, conformance checking techniques evaluate process instances by comparing their execution to work-flow models. However, this paradigm is depending on the assumption, that the work-flow perspective contains all necessary information to reveal potential non-conformance. In this work we propose the novel method TADE to check for process conformance with regards to another perspective. While traditional methods like token-based replay and alignments focus on workflow-based deviations, we developed time-sensitive stochastic estimators and prove their superiority over the competitors regarding accuracy and runtime efficiency. TADE is based on the well-known kernel density estimation. The probabilities of event occurrences at certain timestamps are modeled, so the fitness of new cases is computed considering this stochastic model. We evaluate this on a real-world building permit application process, which shows its usage capabilities in industrial scenarios.