Artificial intelligence in conformance checking: state of the art and research agenda

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.