Dimensionality Reduction in a P2P System

Peers and data objects in the hybrid overlay network (HON) are organized in a n-dimensional feature space. As the dimensionality increases, peers and data objects become sparse and the distance measures become increasingly meaningless which leads to serious problems affecting HON performance. In thi...

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Bibliographic Details
Main Authors: Kacimi, M., Yetongnon, K.
Format: Conference Proceeding
Language:English
Subjects:
Content based retrieval
Data analysis
Data mining
Distributed computing
Expert systems
Information retrieval
Network servers
Peer to peer computing
Spatial databases
Online Access:Request full text
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Summary:Peers and data objects in the hybrid overlay network (HON) are organized in a n-dimensional feature space. As the dimensionality increases, peers and data objects become sparse and the distance measures become increasingly meaningless which leads to serious problems affecting HON performance. In this paper we propose a distributed feature selection technique reduce the dimensionality in HON. We study in our simulations the impact of the proposed feature selection technique on query results quality and show that it achieves high recall and precision.
ISSN:1529-4188
2378-3915
DOI:10.1109/DEXA.2007.85