Distributed Ranked Search

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Distributed Ranked Search
P2P deployments are a natural infrastructure for building distributed search networks. Proposed systems support locating and retrieving all results, but lack the information necessary to rank them. Users, however, are primarily interested in the most relevant results, not necessarily all possible results. Using random sampling, we extend a class of well-known information retrieval ranking algorithms such that they can be applied in this decentralized setting. We analyze the overhead of our approach, and quantify how our system scales with increasing number of documents, system size, document to node mapping (uniform versus non-uniform), and types of queries (rare versus popular terms). Our analysis and simulations show that a) these extensions are efficient, and scale with little overhead to large systems, and b) the accuracy of the results obtained using distributed ranking is comparable to that of a centralized implementation.
Vijay Gopalakrishnan, Ruggero Morselli, Bobby Bhat
Added 07 Jun 2010
Updated 07 Jun 2010
Type Conference
Year 2007
Where HIPC
Authors Vijay Gopalakrishnan, Ruggero Morselli, Bobby Bhattacharjee, Peter J. Keleher, Aravind Srinivasan
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