During the last years a new generation of adaptive Process-Aware Information Systems (PAIS) has emerged, which enables dynamic process changes at runtime, while preserving PAIS robustness and consistency. Such adaptive PAIS allow authorized users to add new process activities, to delete existing activities, or to change pre-defined activity sequences during runtime. Both this runtime flexibility and process configurations at build-time, lead to a large number of process variants being derived from the same process model, but slightly differing in structure due to the applied changes. Generally, process variants are expensive to configure and difficult to maintain. This paper presents selected results from our MinAdept project. In particular, we provide a clustering algorithm that fosters learning from past process changes by mining a collection of process variants. As mining result we obtain a process model for which average distance to the process variant models becomes minimal. By adopting this process model as reference model in the PAIS, need for future process configuration and adaptation decreases. We have validated our clustering algorithm by means of a case study as well as comprehensive simulations. Altogether, our vision is to enable full process lifecycle support in adaptive PAIS.
In REF an approach is described to synthesize the most representative process model out of a collection of variants.
THE MINADEPT CLUSTERING APPROACH FOR DISCOVERING REFERENCE PROCESS MODELS OUT OF PROCESS VARIANTS
Int. J. Cooperative Inf. Syst.
Int. J. Cooperative Inf. Syst.
Computer Science