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

Ozgur D. Sahin, Fatih Emekci, Divyakant Agrawal, Amr El Abbadi

Research output: Contribution to journalConference article

10 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)61-78
Number of pages18
JournalLecture Notes in Computer Science
Volume3367
Publication statusPublished - 9 Sep 2005
EventSecond International Workshop on Databases, Information Systems, and Peer-to-Peer Computing, DBISP2P 2004 - Toronto, Ont., Canada
Duration: 29 Aug 200430 Aug 2004

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ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Sahin, O. D., Emekci, F., Agrawal, D., & El Abbadi, A. (2005). Content-based similarity search over peer-to-peer systems. Lecture Notes in Computer Science, 3367, 61-78.