University of Udine
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.