Flexible Process Model Mapping using Relaxation Labeling

Abstract

Computing a mapping between two process models is a crucial technique, since it enables reasoning and operating across processes, like providing a similarity score between two processes, or merging different process variants to generate a consolidated process model. In this paper we present a new flexible technique for process model mapping, based on the relaxation labeling constraint satisfaction algorithm. The technique can be instantiated so that different modes are devised, depending on the context. For instance, it can be adapted to the case where one of the mapped process models is incomplete, or it can be used to ground an adaptable similarity measure between process models. The approach has been implemented inside the open platform NLP4BPM, providing a visualization of the performed mappings and computed similarity scores. The experimental results witness the flexibility and usefulness of the technique proposed.