Tailoring Machine Learning for Process Mining

Process Mining (PM) is a consolidated discipline grounded on data mining and business process management. The exploitation of traditional PM tasks (discovery, conformance checking, and enhancement) is today a reality in many organizations [1, 2]. In the last decade, a wave of new results in artificial intelligence has triggered the interest of the PM research community in using supervised or unsupervised Machine Learning (ML) techniques for gaining insight into business processes and providing advice on how to improve their inefficiencies. In today's practice, ML models are routinely integrated into PM data pipelines [3] to carry out tasks like data transformation, noise reduction, anomaly detection, classification, and prediction. For example, ML is playing a key role in the interface between PM and sensor platforms. Advances in sensing technologies have made it possible to deploy distributed monitoring platforms capable of detecting fine-grained events. The granularity gap between these events and the activities considered by classic PM analysis has often been bridged using ML models [4, 5] that compute virtual activity logs, a problem which is also known as log lifting [6].