Discovery and reuse of composition knowledge for assisted mashup development

Despite the emergence of mashup tools like Yahoo! Pipes or JackBe Presto Wires, developing mashups is still non-trivial and requires intimate knowledge about the functionality of web APIs and services, their interfaces, parameter settings, data mappings, and so on. We aim to assist the mashup process and to turn it into an interactive co-creation process, in which one part of the solution comes from the developer and the other part from reusable composition knowledge that has proven successful in the past. We harvest composition knowledge from a repository of existing mashup models by mining a set of reusable composition patterns, whichwe then use to interactively provide composition recommendations to developers while they model their own mashup. Upon acceptance of a recommendation, the purposeful design of the respective pattern types allows us to automatically weave the chosen pattern into a partial mashup model, in practice performing a set of modeling actions on behalf of the developer. The experimental evaluation of our prototype implementation demonstrates that it is indeed possible to harvest meaningful, reusable knowledge from existing mashups, and that even complex recommendations can be efficiently queried and weaved also inside the client browser. Despite the emergence of mashup tools like Yahoo! Pipes or JackBe Presto Wires, developing mashups is still non-trivial and requires intimate knowledge about the functionality of web APIs and services, their interfaces, parameter settings, data mappings, and so on. We aim to assist the mashup process and to turn it into an interactive co-creation process, in which one part of the solution comes from the developer and the other part from reusable composition knowledge that has proven successful in the past. We harvest composition knowledge from a repository of existing mashup models by mining a set of reusable composition patterns, which we then use to interactively provide composition recommendations to developers while they model their own mashup. Upon acceptance of a recommendation, the purposeful design of the respective pattern types allows us to automatically weave the chosen pattern into a partial mashup model, in practice performing a set of modeling actions on behalf of the developer. The experimental evaluation of our prototype implementation demonstrates that it is indeed possible to harvest meaningful, reusable knowledge from existing mashups, and that even complex recommendations can be efficiently queried and weaved also inside the client browser.