On Measuring Process Model Similarity Based on High-Level Change Operations
ER '08 Proceedings of the 27th International Conference on Conceptual Modeling
Flexibility in Process-Aware Information Systems
Transactions on Petri Nets and Other Models of Concurrency II
Discovering Reference Models by Mining Process Variants Using a Heuristic Approach
BPM '09 Proceedings of the 7th International Conference on Business Process Management
Survey paper: Refactoring large process model repositories
Computers in Industry
Comparison and retrieval of process models using related cluster pairs
Computers in Industry
A process distance metric based on alignment of process structure trees
APWeb'12 Proceedings of the 14th international conference on Web Technologies and Applications
On Utilizing Web Service Equivalence for Supporting the Composition Life Cycle
International Journal of Web Services Research
Multidimensional process mining: a flexible analysis approach for health services research
Proceedings of the Joint EDBT/ICDT 2013 Workshops
| Hi-index | 0.00 |
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.