An Execution-Semantic Approach to Inductive Reference Models Development

Jana-Rebecca Rehse, Peter Fettke, Peter Loos

In: 24th European Conference for Information Systems (ECIS). European Conference on Information Systems (ECIS-16) June 12-15 Istanbul Turkey Association for Information Systems (AIS) 2016.


Reference models are a cost- and time-saving approach for the development of new models. As inductive strategies are capable of automatically deriving a potential reference model from a collection of existing process models, they have gained attention in current research. A number of promising approaches can be found in recent publications. However, all existing methods rely on graph-based similarity measures to identify commonalities between input models. Since behaviourally similar process models can have different graphical structures, those approaches are unable to find certain commonalities. To overcome these shortcomings, we propose a new approach to inductive reference model development based on an execution-semantic similarity measure. Since a naïve solution to the intuitive idea does not yield productive results, the proposed approach is rather elaborate. By capturing the commonalities of the input models in a behavioural profile, we are able to derive a reference model subsuming the input models’ semantics instead of their structure. In our contribution, this approach is outlined, implemented and evaluated in three different scenarios. As the evaluations show, it is capable of handling complex process models and overcome most restrictions that structural approaches pose. Thus, it introduces a new level of flexibility and applicability to inductive reference modelling.

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence