文档介绍：ARTICLE IN PRESS
Information Systems 30 (2005) 277–298
Exploiting locality for scalable information retrieval in
peer-to-works$
D. Zeinalipour-Yazti, Vana Kalogeraki*, Dimitrios Gunopulos
Department puter Science and Engineering, University of California, Riverside, CA 92521, USA
Received 17 April 2003; received in revised form 1 March 2004; accepted 3 March 2004
Abstract
An important problem in unstructured peer-to-peer (P2P) networks is the efficient content-based retrieval of
documents shared by other peers. However, existing searching mechanisms are not scaling well because they are either
based on the idea of flooding work with queries or because they require some form of global knowledge.
We propose the Intelligent Search Mechanism (ISM) which is an efficient, scalable yet simple mechanism for
improving the information retrieval problem in P2P systems. Our mechanism is efficient since it is bounded by the
number of neighbors and scalable because no global knowledge is required to be maintained.
ISM consists of ponents: A Profiling Structure which logs queryhit ing from neighbors, a
Query Similarity function which calculates the similarity queries to a new query, RelevanceRank which is an online
neighbor ranking function and a Search Mechanism which forwards queries to selected neighbors.
We deploy pare ISM with a number of other distributed search techniques over static and dynamic
environments. Our experiments are performed with real data over Peerware, our middleware simulation infrastructure
which is deployed on 75 workstations. Our results indicate that ISM outperforms petitors and that in some cases
it manages to achieve 100% recall rate while using only half of work resources required by petitors.
Further, its performance is also superior with respect to the total query response time and our algorithm exhibits a
learning behavior as nodes acquire more knowledge. Finally ISM works well in work topologies and in
environments wit