Novel Discovery Mechanism for Crossing-Workflow Fragments Leveraging Activity Relevance

This paper proposes to discover crossing-workflow fragments with respect to the requirement of scientific experiments. Specifically, functionally-similar activities are clustered by adopting a modularity-based community discovery clustering method, and they are represented as abstract activities. An abstract activity network model is constructed to reflect the invocation relations among abstract activities. Workflow fragments with similar structure and semantics are discovered from the abstract activity network through the sub-graph matching algorithm. These fragments are instantiated by replacing abstract activities by appropriate activities in certain activity clusters. Instantiated workflow fragments are evaluated and recommended for their reuse and repurposing purpose. Experimental evaluation results demonstrate that this technique is accurate and efficient on the discovery and recommendation of appropriate crossing-workflow fragments in comparison with the state of arts.