A Global Knowledge for Information Retrieval in P2P Networks
Anis Ismail, Mohamed Quafafou, Gilles Nachouki, Mohammad Hajjar · 2010
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.