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A local search mechanism for peer-to-peer networks

Kalogeraki, Vana, Gunopulos, Dimitrios, Zeinalipour-Yazti, D.·2002·Proceedings of the eleventh international conference on Information and knowledge management

Abstract

One important problem in peer-to-peer (P2P) networks is searching and retrieving the correct information. However, existing searching mechanisms in pure peer-to-peer networks are inefficient due to the decentralized nature of such networks. We propose two mechanisms for information retrieval in pure peer-to-peer networks. The first, the modified Breadth-First Search (BFS) mechanism, is an extension of the current Gnuttela protocol, allows searching with keywords, and is designed to minimize the number of messages that are needed to search the network. The second, the Intelligent Search mechanism, uses the past behavior of the P2P network to further improve the scalability of the search procedure. In this algorithm, each peer autonomously decides which of its peers are most likely to answer a given query. The algorithm is entirely distributed, and therefore scales well with the size of the network. We implemented our mechanisms as middleware platforms. To show the advantages of our mechanisms we present experimental results using the middleware implementation.

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Metadata

Authors
Kalogeraki, V., Gunopulos, D., & Zeinalipour-Yazti, D.
Venue
Proceedings of the eleventh international conference on Information and knowledge management
Publisher
ACM
Published
2002-11-04
Pages
300-307
OpenAlex
W2098125775
Cited by
369
References
0
FWCI
10.01
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: A local search mechanism for peer-to-peer networks. https://app.cochat.ai/papers/10.1145/584792.584842
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Kalogeraki, V., Gunopulos, D., & Zeinalipour-Yazti, D. (2002). A local search mechanism for peer-to-peer networks. Proceedings of the eleventh international conference on Information and knowledge management. https://doi.org/10.1145/584792.584842

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.