Scalable Peer-to-Peer Web Retrieval with Highly Discriminative Keys (CROSBI ID 528838)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Podnar, Ivana ; Rajman, Martin ; Luu, Toan ; Klemm, Fabius ; Aberer, Karl
engleski
Scalable Peer-to-Peer Web Retrieval with Highly Discriminative Keys
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 present a novel indexing/retrieval model that achieves high performance, cost-efficient retrieval by indexing with \emph{;highly discriminative keys (HDKs)}; stored in a distributed global index maintained in a structured P2P network. HDKs correspond to carefully selected terms and term sets appearing in small numbers 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 retrieval engine. These results show that, despite increased indexing costs, the total traffic generated with the HDK approach is significantly smaller than the one obtained with distributed single-term indexing strategies. Furthermore, our experiments show that the retrieval performance obtained with a random set of real queries is comparable to the one of centralized, single-term solution using the best state-of-the-art BM25 relevance computation scheme. Finally, our scalability analysis demonstrates that the HDK approach can scale to large networks of peers indexing web-size document collections, thus opening the way towards viable, truly-decentralized web retrieval.
information retrieval; P2P
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Podaci o prilogu
1096-1105-x.
2007.
objavljeno
Podaci o matičnoj publikaciji
Proc. IEEE 23rd International Conference on Data Engineering (ICDE 2007)
Chirkova, Rada ; Oria, Vincent
Los Alamitos (CA): Institute of Electrical and Electronics Engineers (IEEE)
1-4244-0802-4
Podaci o skupu
IEEE 23rd International Conference on Data Engineering (ICDE 2007)
predavanje
15.04.2007-20.04.2007
Istanbul, Turska