Detecting deviating behaviors without models

Deviation detection is a set of techniques that identify deviations from normative processes in real process executions. These diagnostics are used to derive recommendations for improving business processes. Existing detection techniques identify deviations either only on the process instance level or rely on a normative process model to locate deviating behavior on the event level. However, when normative models are not available, these techniques detect deviations against a less accurate model discovered from the actual behavior, resulting in incorrect diagnostics. In this paper, we propose a novel approach to detect deviation on the event level by identifying frequent common behavior and uncommon behavior among executed process instances, without discovering any normative model. The approach is implemented in ProM and was evaluated in a controlled setting with artificial logs and real-life logs. We compare our approach to existing approaches to investigate its possibilities and limitations. We show that in some cases, it is possible to detect deviating events without a model as accurately as against a given precise normative model