Efficient, Interactive Recommendation of Mashup Composition Knowledge

  • Soudip Roy Chowdhury
  • Florian Daniel
  • Fabio Casati
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7084)

Abstract

In this paper, we approach the problem of interactively querying and recommending composition knowledge in the form of re-usable composition patterns. The goal is that of aiding developers in their composition task. We specifically focus on mashups and browser-based modeling tools, a domain that increasingly targets also people without profound programming experience. The problem is generally complex, in that we may need to match possibly complex patterns on-the-fly and in an approximate fashion. We describe an architecture and a pattern knowledge base that are distributed over client and server and a set of client-side search algorithms for the retrieval of step-by-step recommendations. The performance evaluation of our prototype implementation demonstrates that - if sensibly structured - even complex recommendations can be efficiently computed inside the client browser.

Keywords

Service Composition Business Process Management Retrieval Time Query Object Composition Pattern 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Soudip Roy Chowdhury
    • 1
  • Florian Daniel
    • 1
  • Fabio Casati
    • 1
  1. 1.University of TrentoPovoItaly

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