Mining Business Process Variants: Challenges, Scenarios, Algorithms

During the last decade a new generation of process-aware information systems has emerged, which enables process model configurations at buildtime as well as process instance changes during runtime. Respective adaptations result in a large number of process model variants that were derived from the same process model, but slightly differ in structure. Generally, such model variants are expensive to configure and maintain. This paper introduces two different scenarios for learning from process model adaptations and for discovering a reference model out of which the variants can be configured with minimum efforts. The first scenario presumes a reference process model and a collection of related process model variants. The goal is to evolve the reference process model such that it structurally fits better to the given variant models. The second scenario comprises a collection of process variants, while the original reference model is unknown; i.e., the goal is to "merge" these variants into a reference process model. We suggest two algorithms that are applicable in both scenarios, but which have their pros and cons. We systematically compare the two algorithms and contrast them with conventional process mining techniques. Our comparison results indicate good performance of both algorithms. Further they confirm that specific techniques are needed for learning from past process adaptations. Finally, we present a case study in the automotive industry in which we applied our algorithms