Similarity assessment and efficient retrieval of semantic workflows

Abstract

In the recent years, the use of workflows has significantly expanded from its original domain of business processes towards new areas. The increasing demand for individual and more flexible workflows asks for new methods that support domain experts to create, monitor, and adapt workflows. The emergent field of process-oriented case-based reasoning addresses this problem by proposing methods for reasoning with workflows based on experience. New workflows can be constructed by reuse of already available similar workflows from a repository. Hence, methods for the similarity assessment of workflows and for the efficient retrieval of similar workflows from a repository are of core importance. To this end, we describe a new generic model for representing workflows as semantically labeled graphs, together with a related model for knowledge intensive similarity measures. Further, new algorithms for workflow similarity computation, based on A⁎ search are described. A new retrieval algorithm is introduced that goes beyond traditional sequential retrieval for graphs, interweaving similarity computation with case selection. We describe the application of this model and several experimental evaluations of the algorithms in the domain of scientific workflows and in the domain of business workflows, thereby showing its broad applicability.