Similarity Search in Metric Spaces

Abstract

Similarity search in metric spaces has attracted much attention recently due to numerous applications, including multimedia retrieval and scientific data management. Several centralized indexing methods have been proposed to support efficient similarity search in metric spaces. In this chapter, a distributed framework termed SIMPEER [49] is presented for efficient similarity search in P2P systems that extends the basic concepts of an efficient approach that has been previously proposed for centralized systems. SIMPEER dynamically clusters peer data, in order to build distributed routing information at super-peer level. The usage of carefully designed distributed data summaries guarantees that all similar objects to the query are retrieved, without necessarily flooding the network during query processing. With SIMPEER, the targeted query types (range and nearest neighbor queries) can be efficiently evaluated, thus reducing communication cost, network latency, bandwidth consumption and computational overhead at each individual peer.