Mining Based on Learning from Process Change Logs

In today's dynamic business world economic success of an enterprise increasingly depends on its ability to react to internal and external changes in a quick and flexible way. In response to this need, process-aware information systems (PAIS) emerged, which support the modeling, orchestration and monitoring of business processes. Recently, a new generation of flexible PAIS was introduced, which additionally allows for dynamic process changes. This, in turn, leads to a large number of process variants, which are created from the same original model, but might slightly differ from each other. This paper deals with issues related to the mining of such process variant collections. Our overall goal is to learn from process changes and to merge the resulting model variants into a generic process model in the best possible way. By adopting this generic process model in the PAIS, future costs of process change and need for process adaptations will decrease. We compare process variant mining with conventional process mining techniques, and show that it is additionally needed to learn from process changes.