Data is Moody: Discovering Data Modification Rules from Process Event Logs

Marco Bjarne Schuster, Boris Wiegand, Jilles Vreeken

Abstract: Although event logs are a powerful source to gain insight into the behavior of the underlying business process, existing work primarily focuses on finding patterns in the activity sequences of an event log, while ignoring event attribute data. Event attribute data has mostly been used to predict event occurrences and process outcome, but the state of the art neglects to mine succinct and interpretable rules describing how event attribute data changes during process execution. Subgroup discovery and rule-based classification approaches lack the ability to capture the sequential dependencies present in event logs, and thus lead to unsatisfactory results with limited insight into the process behavior.