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DBISP2P
2005
Springer

A Content-Addressable Network for Similarity Search in Metric Spaces

10 years 5 months ago
A Content-Addressable Network for Similarity Search in Metric Spaces
In this paper we present a scalable and distributed access structure for similarity search in metric spaces. The approach is based on the Content– addressable Network (CAN) paradigm, which provides a Distributed Hash Table straction over a Cartesian space. We have extended the CAN structure to support storage and retrieval of more generic metric space objects. We use pivots for projecting objects of the metric space in an N-dimensional vector space, and exploit the CAN organization for distributing the objects among computer nodes of the structure. We obtain a Peer–to–Peer network, called the MCAN, which is able to search metric space objects by means of the similarity range queries. Experiments conducted on our prototype system confirm full scalability of the approach.
Fabrizio Falchi, Claudio Gennaro, Pavel Zezula
Added 26 Jun 2010
Updated 26 Jun 2010
Type Conference
Year 2005
Where DBISP2P
Authors Fabrizio Falchi, Claudio Gennaro, Pavel Zezula
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