Assessing the effectiveness of automated service composition
With the increasing popularity of scientific workflow management systems (SWfMS), more and more workflow specifications are becoming available. Such specifications contain precious knowledge that can be reused to produce new workflows. It is a fact that provenance data can help reusing third party code. However, finding the dependencies among programs without the support of a tool is not a trivial activity and, in many cases, becomes a barrier to build more sophisticated models and analysis. Due to the huge number of task versions available and their configuration parameters, this activity is highly error prone and counterproductive. In this work, we propose workflow recommender (WR), a recommendation service that aims at suggesting frequent combinations of workflow tasks for reuse. It works similarly to an e-commerce application that applies data mining techniques to help users find items they would like to purchase, predicting a list based on other user’s choices. Our experiments show that our approach is effective both in terms of performance and precision of the results. The approach is general in the sense that it can be coupled to any SWfMS.