Content-based similarity search over peer-to-peer systems

Authors:
Ozgur D. Sahin;Fatih Emekci;Divyakant Agrawal;Amr El Abbadi
Affiliations:
Department of Computer Science, University of California Santa Barbara, Santa Barbara, CA;Department of Computer Science, University of California Santa Barbara, Santa Barbara, CA;Department of Computer Science, University of California Santa Barbara, Santa Barbara, CA;Department of Computer Science, University of California Santa Barbara, Santa Barbara, CA
Venue:
DBISP2P'04 Proceedings of the Second international conference on Databases, Information Systems, and Peer-to-Peer Computing
Year:
2004

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Abstract

Peer-to-peer applications are used to share large volumes of data. An important requirement of these systems is efficient methods for locating the data of interest in a large collection of data. Unfortunately current peer-to-peer systems either offer exact keyword match functionality or provide inefficient text search methods through centralized indexing or flooding. In this paper we propose a method based on popular Information Retrieval techniques to facilitate content-based searches in peer-to-peer systems. A simulation of the proposed design was implemented and its performance was evaluated using some commonly used test collections, including Ohsumed which was used for the TREC-9 Filtering Track. The experiments demonstrate that our approach is scalable as it achieves high recall by visiting only a small subset of the peers.