Text-Based Content Search and Retrieval in Ad-hoc P2P Communities

  • Francisco Matias Cuenca-Acuna
  • Thu D. Nguyen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2376)


We consider the problem of content search and retrieval in peer-to-peer (P2P) communities. P2P computing is a potentially powerful model for information sharing between ad hoc groups of users because of its low cost of entry and natural model for resource scaling. As P2P communities grow, however, locating information distributed across the large number of peers becomes problematic. We address this problem by adapting a state-of-the-art text-based document ranking algorithm, the vector-space model instantiated with the TFxIDF ranking rule, to the P2P environment. We make three contributions: (a) we show how to approximate TFxIDF using compact summaries of individual peers’ inverted indexes rather than the inverted index of the entire communal store; (b) we develop a heuristic for adaptively determining the set of peers that should be contacted for a query; and (c) we show that our algorithm tracks TFxIDF’s performance very closely, giving P2P communities a search and retrieval algorithm as good as that possible assuming a centralized server.


Relevant Document Retrieval Algorithm Community Size Inverted Index Inverse Document Frequency 
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 2002

Authors and Affiliations

  • Francisco Matias Cuenca-Acuna
    • 1
  • Thu D. Nguyen
    • 1
  1. 1.Department of Computer ScienceRutgers UniversityPiscataway

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