class-based search system in unstructured peer-to-peer networks

Efficient searching is one of the important design issues in peer-to-peer (P2P) networks. Among various searching techniques, semantic-based searching has drawn significant attention recently. Gnutella-like efficient searching system (GES) [29] is such a system. GES derives node vector , a semantic summary of all documents on a node based on vector space model (VSM). The node-based topology adaptation algorithm and search protocol are then discussed. However, when there are many categories of documents at each node, the node vector representation may be inaccurate. We extend the idea of GES and present a class-based search system (CSS). It makes use of a document clustering algorithm: OSKM [27] to cluster all documents on a node into several classes. Each class can be viewed as a virtual node. As a result, class vector replaces node vector and plays an important role in class-based topology adaptation and search process, which makes CSS very efficient. Our simulation demonstrates that CSS outperforms GES.