High search performance, small document index: P2P search can have both

One primary goal in P2P networks is to provide high search performance for users to retrieve interested documents distributed over nodes. Document indexing is the key to search performance. However, it is challenging to guarantee high search performance with small document index. In this paper, we present iSearch which aims to build small document index to deliver high search performance on Gnutella-like P2P networks. The number of index terms per document is typically 4, which dramatically reduces associated cost in index storage and dissemination. iSearch explores two options to build index: top term-based indexing (TTI) and query-driven indexing (QDI). TTI bases selection of document index terms on term statistics, while QDI progressively refines document index by past queries. Our simulations show that that TTI and QDI improve search performance over random walk significantly. By dynamically adapting index based on past queries, QDI outperforms TTI greatly, by up to 2× recall improvement.