Podnar, IvanaRajman, MartinLuu, ToanKlemm, FabiusAberer, Karl2006-07-172006-07-172006-07-172006https://infoscience.epfl.ch/handle/20.500.14299/232618WOS:000249779900108The 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.peer-to-peer information systemsdistributed information retrievalscalabilitytext::report