Recently, a new generation of adaptive Process-Aware Information Systems (PAIS) has emerged, which allows for dynamic process and service changes (e.g., to insert, delete, and move activities and service executions in a running process). This, in turn, has led to a large number of process variants derived from the same model, but differing in structure due to the applied changes. Generally, such process variants are expensive to configure and difficult to maintain. This paper provides a sophisticated approach which fosters learning from past process changes and allows for mining process variants. As a result we obtain a generic process model for which the average distance between this model and the respective process variants becomes minimal. By adopting this generic model in the PAIS, need for future process configuration and adaptation decreases. We have validated the proposed mining method and implemented it in a powerful proof-of-concept prototype.
|Publisher||IEEE Computer Society Press|