Automatic Service Derivation from Business Process Model Repositories via Semantic Technology

Although several approaches for service identification have been defined in research and practice, there is a notable lack of fully automated techniques. In this paper, we address the problem of manual work in the context of service derivation and present an approach for automatically deriving service candidates from business process model repositories. Our approach leverages semantic technology in order to derive ranked lists of useful service candidates. An evaluation of the approach with three large process model collection from practice indicates that the approach can effectively identify useful services with hardly any manual effort. The evaluation further demonstrates that our approach can address varying degrees of service cohesion by applying different aggregation mechanisms. Hence, the presented approach represents a useful artifact for enabling business and IT managers to quickly spot reuse potential in their company. In addition, our approach improves the alignment between business and IT. As the ranked service candidates give a good impression on the relative importance of a business operation, they can provide companies with first clues on where IT support is needed and where it could be reduced

Towards the Automated Annotation of Process Models

Many techniques for the advanced analysis of process models build on the annotation of process models with elements from predefined vocabularies such as taxonomies. However, the manual annotation of process models is cumbersome and sometimes even hardly manageable taking the size of taxonomies into account. In this paper, we present the first approach for automatically annotating process models with the concepts of a taxonomy. Our approach builds on the corpus-based method of second-order similarity, different similarity functions, and a Markov Logic formalization. An evaluation with a set of 12 process models consisting of 148 activities and the PCF taxonomy consisting of 1,131 concepts demonstrates that our approach produces satisfying results