LIProMa: Label-Independent Process Matching

Abstract

The identification of best practices is an important methodology to improve the executions of processes. To determine those best practices process mining techniques analyze process entities and model specific views to highlight points for improvements. A major requirement in most approaches is a common activity space so events can be related directly. However there are instances which do provide multiple activity universes and processes from different sources need to be compared. For example in corporate finance, strategic operations like mergers or acquisitions cause processes with similar workflow but different descriptions to be merged. In this work we develop LIProMa, a method to compare processes based on their temporal flow of action sequences by solving the correlated transportation problem. Activity labels are purposely omitted in the comparison. Hence our novel method provides a similarity measure which is capable of comparing processes with diverging labels often caused by distributed executions and varying operators. Therefore it works orthogonal to conventional methods which rely on similarity between activity labels. Instead LIProMa establishes a correspondence between activities of two processes by focusing on temporal patterns.