Mining configurable process models from collections of event logs

Existing process mining techniques are able to discover a specific process model for a given event log. In this paper, we aim to discover a configurable process model from a collection of event logs, i.e., the model should describe a family of process variants rather than one specific process. Consider for example the handling of building permits in different municipalities. Instead of discovering a process model per municipality, we want to discover one configurable process model showing commonalities and differences among the different variants. Although there are various techniques that merge individual process models into a configurable process model, there are no techniques that construct a configurable process model based on a collection of event logs. By extending our ETM genetic algorithm, we propose and compare four novel approaches to learn configurable process models from collections of event logs. We evaluate these four approaches using both a running example and a collection of real event logs.