Assisted Mashup Development: On the Discovery and Recommendation of Mashup Composition Knowledge

  • Carlos Rodríguez
  • Soudip Roy Chowdhury
  • Florian Daniel
  • Hamid R. Motahari Nezhad
  • Fabio Casati
Chapter

Abstract

Over the past few years, mashup development has been made more accessible with tools such as Yahoo! Pipes that help in making the development task simpler through simplifying technologies. However, mashup development is still a difficult task that requires knowledge about the functionality of web APIs, parameter settings, data mappings, among other development efforts. In this work, we aim at assisting users in the mashup process by recommending development knowledge that comes in the form of reusable composition knowledge. This composition knowledge is harvested from a repository of existing mashup models by mining a set of composition patterns, which are then used for interactively providing composition recommendations while developing the mashup. When the user accepts a recommendation, it is automatically woven into the partial mashup model by applying modeling actions as if they were performed by the user. In order to demonstrate our approach we have implemented Baya, a Firefox plugin for Yahoo! Pipes that shows that it is indeed possible to harvest useful composition patterns from existing mashups, and that we are able to provide complex recommendations that can be automatically woven inside Yahoo! Pipes’ web-based mashup editor.

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Carlos Rodríguez
    • 1
  • Soudip Roy Chowdhury
    • 1
  • Florian Daniel
    • 1
    • 2
  • Hamid R. Motahari Nezhad
    • 3
  • Fabio Casati
    • 1
  1. 1.University of Trento PovoItaly
  2. 2.Dipartimento di Ingegneria e Scienza dell’InformazioneUniversità di TrentoPovoItaly
  3. 3.Hewlett Packard LabsPalo AltoUSA

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