Recommend-As-You-Go: A Novel Approach Supporting Services-Oriented Scientific Workflow Reuse

Abstract

Services computing technology enables scientists to expose data and computational resources wrapped as publicly accessible Web services. However, our study indicates that scientific services are currently poorly reused in an ad hoc style. This project aims to help domain scientists find interested services and reuse successful processes to attain their research purposes in the form of workflows. In contrast to existing interface-based services discovery approaches, this paper proposes a novel approach of proactively recommending services in a workflow composition process, based on service usage history. The underpinning is a People-Service-Workflow (PSW) network that models existing scientific artifacts, services and workflows, and their past usage relationships into a social network. Various social network analysis techniques are applied to discover hidden knowledge accrued. A prototyping search engine has been developed as a proof of concept, and is seamlessly integrated as a plug-in into the Tavern a workbench, a widely used scientific workflow management tool.