Complementary assistance mechanisms for end user mashup composition

Despite several efforts for simplifying the composition process, learning efforts required for using existing mashup editors to develop mashups remain still high. In this paper, we describe how this barrier can be lowered by means of an assisted development approach that seamlessly integrates automatic composition and interactive pattern recommendation techniques into existing mashup platforms for supporting easy mashup development by end users. We showcase the use of such an assisted development environment in the context of an open-source mashup platform Apache Rave. Results of our user studies demonstrate the benefits of our approach for end user mashup development.