Peer-to-Peer Similarity Search Based on M-Tree Indexing
Similarity search in metric spaces has several important applications both in centralized and distributed environments. In centralized applications, such as similarity-based image retrieval, usually a server indexes its data with a state-of-the-art centralized metric indexing technique, such as the M-Tree. In this paper, we propose a framework for distributed similarity search, where each participating peer stores its own data autonomously, under the assumption that data is indexed locally by peers using M-Trees. In order to support scalability and efficiency of search, we adopt a super-peer architecture, where super-peers are responsible for query routing. We propose the construction of metric routing indices suitable for distributed similarity search in metric spaces. We study the performance of the proposed framework using both synthetic and real data.
KeywordsQuery Processing Similarity Search Range Query Representative Object Parent Object
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