Mining Reference Process Models from Large Instance Data
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.
Business Process Management Workshops