Intelligent content-based retrieval for P2P networks

Abstract

Currently, most peer-to-peer (P2P) systems are designed for file sharing by network participants. Simple meta-data search mechanism will be sufficient to support searching and retrieving shared files over P2P networks. However, to share document information such as news articles, scientific publications, company reports, etc., a content-based search mechanism is needed to provide efficient content-based retrieval. In this paper, we propose an intelligent P2P content-based document retrieval system known as iSearch-P2P. In iSearch-P2P, we have incorporated an intelligent technique based on the Fuzzy Adaptive Resonance Theory (Fuzzy ART) neural network to perform document clustering in order to support content-based publishing and retrieval over P2P networks. With intelligent content-based search, the iSearch-P2P system supports scalability and avoids indexing and query flooding problems of most existing P2P systems. In this paper, we describe the architecture, the publishing and retrieval processes, implementation and performance evaluation of the proposed iSearch-P2P system.