Mining Reference Process Models from Large Instance Data

Jana-Rebecca Rehse, Peter Fettke

In: Marlon Dumas , Marcelo Fantinato (Hrsg.). Proceedings of the 12th International Workshop on Business Process Intelligence. International Workshop on Business Process Intelligence (BPI-2016) befindet sich International Conference on Business Process Management September 18-22 Rio de Janeiro Brazil Springer 2016.


Reference models provide generic blueprints of process models that are common in a certain industry. When designing a reference model, stakeholders have to cope with the so-called `dilemma of reference modeling', viz., balancing generality against market specificity. In principle, the more details a reference model contains, the fewer situations it applies to. To overcome this dilemma, the contribution at hand presents a novel approach to mining a reference model hierarchy from large instance-level data such as execution logs. It combines an execution-semantic technique for reference model development with a hierarchical-agglomerative cluster analysis and ideas from Process Mining. The result is a reference model hierarchy, where the lower a model is located, the smaller its scope, and the higher its level of detail. The approach is implemented as proof-of-concept and applied in an extensive case study, using the data from the 2015 BPI Challenge.

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence