TY - CHAP T1 - Distributed Ranked Search T2 - High Performance Computing – HiPC 2007High Performance Computing – HiPC 2007 Y1 - 2007 A1 - Gopalakrishnan,Vijay A1 - Morselli,Ruggero A1 - Bhattacharjee, Bobby A1 - Keleher,Pete A1 - Srinivasan, Aravind ED - Aluru,Srinivas ED - Parashar,Manish ED - Badrinath,Ramamurthy ED - Prasanna,Viktor AB - 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. JA - High Performance Computing – HiPC 2007High Performance Computing – HiPC 2007 T3 - Lecture Notes in Computer Science PB - Springer Berlin / Heidelberg VL - 4873 SN - 978-3-540-77219-4 UR - http://dx.doi.org/10.1007/978-3-540-77220-0_6 ER -