Content-based full-text search is a challenging problem in Peer-toPeer (P2P) systems. Traditional approaches have either been centralized or use flooding to ensure accuracy of the results returned. In this paper, we present pSearch, a decentralized non-flooding P2P information retrieval system. pSearch distributes document indices through the P2P network based on document semantics generated by Latent Semantic Indexing (LSI). The search cost (in terms of different nodes searched and data transmitted) for a given query is thereby reduced, since the indices of semantically related documents are likely to be co-located in the network. We also describe techniques that help distribute the indices more evenly across the nodes, and further reduce the number of nodes accessed using appropriate index distribution as well as using index samples and recently processed queries to guide the search. Experiments show that pSearch can achieve performance comparable to centralized information retrieval systems by searching only a small number of nodes. For a system with 128,000 nodes and 528,543 documents (from news, magazines, etc.), pSearch searches only 19 nodes and transmits only 95.5KB data during the search, whereas the top 15 documents returned by pSearch and LSI have a 91.7% intersection.

Content-based full-text search still remains a particularly challenging problem in peer-to-peer (P2P) systems. Traditionally, there have been two index partitioning structures---partitioning based on the document space or partitioning based on keywords. The former requires search of every node in the system to answer a query whereas the latter transmits a large amount of data when processing multi-term queries. In this paper, we propose eSearch---a P2P keyword search system based on a novel hybrid indexing structure. In eSearch, each node is responsible for certain terms. Given a document, eSearch uses a modern information retrieval algorithm to select a small number of top (important) terms in the document and publishes the complete term list for the document to nodes responsible for those top terms. This selective replication of term lists allows a multi-term query to proceed local to the nodes responsible for query terms. We also propose automatic query expansion to alleviate the degradation of quality of search results due to the selective replication, overlay source multicast to reduce the cost of disseminating term lists, and techniques to balance term list distribution across nodes.