Discovering Reference Models by Mining Process Variants Using a Heuristic Approach
BPM '09 Proceedings of the 7th International Conference on Business Process Management
Editorial: Mining business process variants: Challenges, scenarios, algorithms
Data & Knowledge Engineering
On Utilizing Web Service Equivalence for Supporting the Composition Life Cycle
International Journal of Web Services Research
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Recently, a new generation of adaptive process management technology has emerged, which enables dynamic changes of composite services and process models respectively. This, in turn, results in a large number of process variants derived from the same process model, but differing in structure due to the applied changes. Since such process variants are expensive to maintain, the process model should be evolved accordingly. In this context, we need to know which activities have been more often involved in process adaptations than others, such that we can focus on them when reconfiguring the process model. This paper provides two approaches for ranking activities according to their involvement in process adaptations. The first one allows to precisely rank the activities, but is expensive to perform since the algorithm is at NP level. We therefore provide as alternative an approximation ranking algorithm which computes in polynomial time. The performance of the approximation algorithm is evaluated and compared through a simulation of 3600 process models. Statistical significance tests indicate that the performance of the approximation ranking algorithm does not depend on the size of process models, i.e., our algorithm can scale up.