Nearest neighbor search in metric spaces through Content-Addressable Networks
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摘要
Most of the Peer-to-Peer search techniques proposed in the recent years have focused on the single-key retrieval. However, similarity search in metric spaces represents an important paradigm for content-based retrieval in many applications. In this paper we introduce an extension of the well-known Content-Addressable Network paradigm to support storage and retrieval of more generic metric space objects. In particular we address the problem of executing the nearest neighbors queries, and propose three different algorithms of query execution. An extensive experimental study on real-life data sets explores the performance characteristics of the proposed algorithms by showing their advantages and disadvantages.
论文关键词:Content-Addressable Network,Peer-to-Peer,Metric space,Similarity search,Nearest neighbor search
论文评审过程:Available online 1 May 2007.
论文官网地址:https://doi.org/10.1016/j.ipm.2007.03.002