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Scalable Peer-to-Peer Web Retrieval with Highly Discriminative Keys Ivana Podnar, Martin Rajman, Toan Luu, Fabius Klemm, Karl Aberer
 

Summary: Scalable Peer-to-Peer Web Retrieval with Highly Discriminative Keys
Ivana Podnar, Martin Rajman, Toan Luu, Fabius Klemm, Karl Aberer
School of Computer and Communication Sciences
Ecole Polytechnique F´ed´erale de Lausanne (EPFL)
Lausanne, Switzerland
firstname.lastname@epfl.ch
Abstract
The suitability of Peer-to-Peer (P2P) approaches for full-
text web retrieval has recently been questioned because of
the claimed unacceptable bandwidth consumption induced
by retrieval from very large document collections.
In this contribution we formalize a novel index-
ing/retrieval model that achieves high performance, cost-
efficient retrieval by indexing with highly discriminative
keys (HDKs) stored in a distributed global index main-
tained in a structured P2P network. HDKs correspond to
carefully selected terms and term sets appearing in a small
number of collection documents. We provide a theoretical
analysis of the scalability of our retrieval model and report
experimental results obtained with our HDK-based P2P re-

  

Source: Aberer, Karl - Faculté Informatique et Communications, Ecole Polytechnique Fédérale de Lausanne

 

Collections: Computer Technologies and Information Sciences