Detecting Temporal Anomaly and Interestingness in Timed Business Process Models

This paper proposes to derive temporal constraints and granularities corresponding to individual activities, collaborative activities and their connecting edges from event logs. Specifically, a timed hierarchical business process model is constructed. Temporal anomalies are measured with time-constrained and granularity-aware bounds according to user's acceptance of deviant executions. Temporal interestingness, as the complement to anomaly detection, is evaluated as the most probable execution times that are partitioned into user-defined granules and ranked by probability. Experimental evaluations upon public event logs demonstrate the effectiveness and applicability of our proposed model for temporal anomaly and interestingness detection in terms of accuracy and recall, in comparison with the state-of-art's techniques.