In the context of company mergers and restructurings, it often occurs that multiple alternative processes, previously belonging to different companies or units, need to be consolidated into a single one in order to eliminate redundancies and create synergies. To this end, teams of business analysts need to compare similar process models so as to identify commonalities and differences, and to create integrated process models that can be used to drive the process consolidation effort. This process model merging effort is tedious, time-consuming and error-prone. This presentation describes an algorithm that produces a single configurable process model from a collection of (similar) process models. The algorithm works by extracting the common parts of the input process models, creating a single copy of them, and appending the differences as branches of configurable connectors. This way, the merged process model is kept as small as possible, while still capturing all the behavior of the input models. Moreover, analysts are able to trace back which model(s) a given element in the merged model originates from. The algorithm has been prototyped and tested against process models taken from several application domains.
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