Scientific workflow clustering and recommendation leveraging layer hierarchical analysis
Résumé
This article proposes an approach for identifying and recommending scientific workflows for reuse and repurposing. Specifically, a scientific work- flow is represented as a layer hierarchy, which specifies hierarchical relations between this workflow, its subworkflows, and activities. Semantic similarity is calculated between layer hierarchies of workflows. A graphskeleton based clustering technique is adopted for grouping layer hierarchies into clusters. Barycenters in each cluster are identified, which refer to core workflows in this cluster, for facilitating cluster identification and workflow ranking and recommendation. Experimental evaluation shows that our technique is efficient and accurate on ranking and recommending appropriate clusters and scientific workflows with respect to specific requirements of scientific experiments