Abstract

Similarity search has been proved suitable for searching in large collections of unstructured data objects. A number of practical index data structures for this purpose have been proposed. All of them have been devised to process single queries sequentially. However, in large-scale systems such as Web Search Engines indexing multi-media content, it is critical to deal efficiently with streams of queries rather than with single queries. In this paper we show how to achieve efficient and scalable performance in this context. To this end we transform a sequential index based on clustering into a distributed one and devise algorithms and optimizations specially tailored to support high-performance parallel query processing. © 2008 Elsevier Inc. All rights reserved.