A Global Knowledge for Information Retrieval in P2P Networks

Abstract

In traditional P2P networks, such as Gnutella, peers propagate query messages towards the resource holders by flooding them through the network. However, it is a costly operation since it consumes node and link resources excessively, which are often unnecessarily. There is no reason, for example, for a peer to receive a query message if the peer has no matching resource or is not on the path to a peer holding a matching resource. However, how to quickly discover the right resource in a large-scale P2P network without generating too much network traffic and with minimum possible time remain highly challenging. In this paper, we propose a new peer-to-peer (P2P) search method aiming at exploiting data mining concepts (Decision Tree) to improve search performance for information retrieval in P2P network. We use a PDMS system, which aims to combine a Super-Peer (SP) based network with the capability of managing a data model attached to the peers in the form of relational, xml, or object schemes. Each SP is connected to a Global-Knowledge-Super-Peer (GKSP) that operates with an index (decision tree), to predict the relevant domains (super-peers), to answer a given query. Compared with a super peer-based approach, our proposal architectures show the effect of the data mining with better performance with respect to response time, number of messages, precision and recall.