Mining Frequent Infix Patterns from Concurrency-Aware Process Execution Variants

Abstract

Event logs, as considered in process mining, document a large number of individual process executions. Moreover, each process execution consists of various executed activities. To cope with the vast amount of process executions in event logs, the concept of variants exists that group process executions with identical ordering relations among their executed activities. Variants are an integral concept of process mining and help process analysts explore, filter, and manage large amounts of event data. In this paper, we consider concurrency-aware variants that allow activities within a process execution to be partially ordered---the execution of individual activities can overlap in time. However, the number of variants is often vast, making it challenging for process analysts to explore event data. Therefore, we present a novel approach to frequent pattern mining from concurrency-aware variants. We show that mining frequent patterns from concurrency-aware variants can be reduced to the frequent subtree mining problem. Further, we compare our proposed algorithm to a state-of-the-art frequent subtree mining algorithm exhibiting improved performance on real-life event logs.