Paper Info
Title
KMV-peer: a robust and adaptive peer-selection algorithm
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.
Year
DOI
Venue
2011
10.1145/1935826.1935860
WSDM
Keywords
Field
DocType
central index,various scoring function,proposed solution,decentralized search,kmv synopsis,new peer-selection algorithm,centralized search,scoring function,adaptive peer-selection algorithm,k minimal values,p2p,indexation,score function
Small number,Publication,Data mining,Information retrieval,Computer science,Selection algorithm
Conference
Citations 
PageRank 
References 
1
0.35
24
Authors
3
Name
Order
Citations
PageRank
Yosi Mass157460.91
Yehoshua Sagiv253621575.95
Michal Shmueli-Scheuer38916.11