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 formalize a novel indexing/retrieval model that achieves high performance, cost-efficient retrieval by indexing with 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 a small number 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.


Published in:
Proceedings of the 23rd International Conference on Data Engineering,, 1096-1105
Presented at:
IEEE 23rd International Conference on Data Engineering (ICDE 2007) , Istanbul, Turkey, April 15-20, 2007
Year:
2007
Publisher:
IEEE
Laboratories:




 Record created 2011-11-18, last modified 2018-09-13

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