Developing an extensible framework for content based searching in super peer p2p network

Abstract

Searching is an important factor in p2p network for content retrieval. Most of the searches in p2p system are title-based with their limited functionality. Without knowing the unique filename we canpsilat retrieve the content of the file in title based search. Here super peer p2p network is designed that supports content-based search for relevant documents. At the beginning, a general and extensible framework is proposed which is based on hierarchical summary structure for searching similar documents in p2p network. The summary structure is formed by Vector Space Model (VSM), Latent Semantic Indexing (LSI) and Singular Value Decomposition (SVD) techniques. Than an effective document searching is developed by summarizing and maintaining all documents within the network with different factors. Finally at the end, the experimental result is verified on a real p2p prototype and large scale network. The results show the effectiveness, efficiency and scalability of the proposed system.