peer-to-peer similarity search over widely distributed document collections
This paper addresses the challenging problem of similarity search over widely distributed ultra-high dimensional data. Such an application is retrieval of the top- k most similar documents in a widely distributed document collection, as in the case of digital libraries. Peer-to-peer (P2P) systems emerge as a promising solution to delve with content management in cases of highly distributed data collections. We propose a self-organizing P2P approach in which an unstructured P2P network evolves into a super-peer architecture, with super-peers responsible for peers with similar content. Our approach is based on distributed clustering of peer contents, thus managing to create high quality clusters that span the entire network. More importantly, we show how to efficiently process similarity queries capitalizing on the newly constructed, clustered super-peer network. During query processing, the query is propagated only to few carefully selected super-peers that are able to return results of high quality. We evaluate the performance of our approach and demonstrate its advantages through simulation experiments on two document collections.
semantic overlay networks distributed and peer-to-peer search