Conformance checking allows organizations to compare process executions recorded by the IT system 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; it also poses some challenges in interpreting the results, since deviations occurring in parallel behaviors are often instantiated in different sequential behaviors in different traces. In this work, we 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. Our approach has been implemented as a plug-in of the ESub tool and evaluated using both synthetic and real-life logs.

Artificial intelligence methods have gained increasing importance in business process management in recent years. In particular, artificial intelligence boosted the development of process mining techniques, with predictive process monitoring being one prominent example. However, for some branches of process mining, little attention has been paid in the literature to understanding potential opportunities offered by artificial intelligence. In this work, we focus on conformance checking. This discipline is gaining traction in both research and practical applications due to its capability of revealing inconsistencies between models and event logs. Though state-of-the-art conformance checking has been investigated recently, there exists a lack of insights on whether conformance checking can benefit from recent developments in artificial intelligence. This paper addresses this research gap through a systematic literature review of conformance checking approaches contrasted against trends in artificial intelligence research extracted from papers published in core artificial intelligence venues. This comparative analysis extracts prominent trends in both disciplines, highlighting potential overlaps and topics of common interests. Elaborating upon such overlaps, we identify promising research avenues leveraging AI to address open challenges in conformance checking, as well as recent AI trends which may lead to interesting developments for the conformance checking community.