Supporting Approximate Similarity Queries with Quality Guarantees in P2P Systems

In this paper we study how to support similarity queries in peer-to-peer (P2P) systems. Such queries ask for the most relevant objects in a P2P network, where the relevance is based on a predefined similarity function; the user is interested in obtaining objects with the highest relevance. Retrieving all objects and computing the exact answer over a large-scale network is impractical. We propose a novel approximate answering framework which computes an answer by visiting only a subset of network peers. Users are presented with progressively refined answers consisting of the best objects seen so far, together with continuously improving quality guarantees providing feedback about the progress of the search. We develop statistical techniques to determine quality guarantees in this framework. We propose mechanisms to incorporate quality estimators into the search process. Our work makes it possible to implement similarity search as a new method of accessing data from a P2P network, and shows how this can be achieved efficiently