Abstract
We consider the problem of content search and retrieval in peer-to-peer (P2P) communities. P2P computing is a potentially powerful model for information sharing between ad hoc groups of users because of its low cost of entry and natural model for resource scaling with community size. As P2P communities grow in size, however, locating information distributed across the large number of peers becomes problematic. We present a distributed text-based content search and retrieval algorithm to address this problem. Our algorithm is based on a state-of-the-art text-based document ranking algorithm: the vector-space model, instantiated with the TFxIDF ranking rule. A naive application of TFxIDF would require each peer in a community to collect an inverted index of the entire community. This is costly both in terms of bandwidth and storage. Instead, we show how TFxIDF can be approximated given compact summaries of peers’ local inverted indexes. We make three contributions: (a) we show how the TFxIDF rule can be adapted to use the index summaries, (b) we provide a heuristic for adaptively determining the set of peers that should be contacted for a query, and (c) we show that our algorithm tracks TFxIDF’s performance very closely, regardless of how documents are distributed throughout the community. Furthermore, our algorithm preserves the main flavor of TFxIDF by retrieving close to the same set of documents for any given query.