Listen to Me: Improving Process Model Matching through User Feedback

Many use cases in business process management rely on the identification of correspondences between process models. However, the sparse information in process models makes matching a fundamentally hard problem. Consequently, existing approaches yield a matching quality which is too low to be useful in practice. Therefore we propose to investigate user feedback to improve the matching quality. To this end, we analyze which information is suitable for learning. On this basis, we design an approach that performs matching in an iterative, mixedinitiative approach: we determine correspondences between two models automatically, let the user correct them and analyze this input to adapt the matching algorithm. Then, we continue with presenting the results for the next two models. This approach improves the matching quality, as showcased by a comparative evaluation. From this study, we also derive strategies on how to maximize the quality while limiting the workload.