Heuristic mining approaches for high-utility local process models

Local Process Models (LPMs) describe structured fragments of process behavior that occur in the context of business processes. Traditional support-based LPM discovery aims to generate a collection of process models that describe highly frequent behavior, in contrast, in High-Utility Local Process Model (HU-LPM) mining the aim is to generate a collection of process models that provide useful business insights according to a specified utility function. Mining LPMs is computationally expensive as the search space depends combinatorially on the number of activities in the business process. In support-based LPM mining, the search space is constrained by leveraging the anti-monotonic property of support (i.e., the apriori principle). We show that there is no property of monotonicity or anti-monotonicity in HU-LPM mining that allows for lossless pruning of the search space. We propose four heuristic methods to explore the search space only partially. We show on a collection of 57 event logs that these heuristics techniques can reduce the size of the search space of HU-LPM mining without much loss in the mined set of HU-LPMs. Furthermore, we analyze the effect of several properties of the event log on the performance of the heuristics through statistical analysis. Additionally, we use predictive modeling with regression trees to explore the relation between combinations of log properties and the effect of the heuristics on the size of the search space and on the quality of the HU-LPMs, where the statistical analysis focuses on the effect of log properties in isolation.