:: Talks

Title: Discovering Anomalous Behavior from Process Event Logs
Keynote Speaker: Dr. Domenico Potena

Abstract: 

Nowadays, information systems, while supporting daily activities, typically collect data on executed processes (ranging from production to decision-making ones) in event logs. These data store invaluable information about user (or organization) behavior that can be exploited to monitor and improve performance. In order to derive insights on a process, several techniques have been developed within the Process Mining discipline, whose goal consists in discovering, monitoring and improving a given process exploiting data generated during process execution. Among them, conformance checking allows organizations to compare process executions against a process model representing the normative behavior. Most of the existing techniques, however, are only able to pinpoint where individual process executions deviate from the normative behavior, without considering neither possible correlations among occurred deviations nor their frequency. Moreover, the actual control-flow of the process is not taken into account in the analysis. Neglecting possible parallelisms among process activities can lead to inaccurate diagnostics. In this talk, I will introduce Process Mining techniques and in particular the conformance checking. Then, I will present an approach to extract anomalous frequent patterns from historical logging data. The extracted patterns can exhibit parallel behaviors and correlate recurrent deviations that have occurred in possibly different portions of the process, thus providing analysts with a valuable aid for investigating nonconforming behaviors.