KMV-peer: A robust and adaptive peer-selection algorithm

Yosi Mass*, Yehoshua Sagiv, Michal Shmueli-Scheuer

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

The problem of fully decentralized search over many collections is considered. The objective is to approximate the results of centralized search (namely, using a central index) while controlling the communication cost and involving only a small number of collections. The proposed solution is couched in a peer-to-peer (P2P) network, but can also be applied in other setups. Peers publish per-term summaries of their collections. Specifically, for each term, the range of document scores is divided into intervals; and for each interval, a KMV (K Minimal Values) synopsis of its documents is created. A new peer-selection algorithm uses the KMV synopses and two scoring functions in order to adaptively rank the peers, according to the relevance of their documents to a given query. The proposed method achieves high-quality results while meeting the above criteria of efficiency. In particular, experiments are done on two large, real-world datasets; one is blogs and the other is web data. These experiments show that the algorithm outperforms the state-of-the-art approaches and is robust over different collections, various scoring functions and multi-term queries.

Original languageEnglish
Title of host publicationProceedings of the 4th ACM International Conference on Web Search and Data Mining, WSDM 2011
Pages157-166
Number of pages10
DOIs
StatePublished - 2011
Event4th ACM International Conference on Web Search and Data Mining, WSDM 2011 - Hong Kong, China
Duration: 9 Feb 201112 Feb 2011

Publication series

NameProceedings of the 4th ACM International Conference on Web Search and Data Mining, WSDM 2011

Conference

Conference4th ACM International Conference on Web Search and Data Mining, WSDM 2011
Country/TerritoryChina
CityHong Kong
Period9/02/1112/02/11

Keywords

  • Algorithms
  • Experimentation
  • Performance

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