A content–addressable network for similarity search in metric spaces

  • Falchi F
  • Gennaro C
  • Zezula P
19Citations
Citations of this article
14Readers
Mendeley users who have this article in their library.
Get full text

Abstract

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 (DHT) abstraction over a Cartesian space. We have extended the CAN structure to support storage and retrieval of 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 the computing 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. © Springer-Verlag Berlin Heidelberg 2007.

Cite

CITATION STYLE

APA

Falchi, F., Gennaro, C., & Zezula, P. (2006). A content–addressable network for similarity search in metric spaces. In International Workshops, DBISP2P 2005/2006 (Vol. 6, pp. 98–110). https://doi.org/10.1007/978-3-540-71661-7_9

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free