Process mining techniques enable the analysis of a wide variety of processes using event data. For example, event logs can be used to automatically learn a process model (e.g., a Petri net or BPMN model). Next to the automated discovery of the real underlying process, there are process mining techniques to analyze bottlenecks, uncover hidden inefficiencies, check compliance, explain deviations, predict performance, and guide users towards “better” processes. However, existing techniques focus on the analysis of a single process rather than the comparison of different processes. Using process cubes, comparative process mining can be applied to analyze multiple processes at the same time.
Given a process cube with suitably chosen dimensions, it is possible to compare process mining results generated for an array of cells. Such a procedure is called comparative process mining. The goal is to highlight differences between cells. This includes comparisons such as cross-checking conformance or comparing models visually or overlay the models as is supported by several process mining tools.