An index data structure for searching in metric space databases

Authors:
Roberto Uribe;Gonzalo Navarro;Ricardo J. Barrientos;Mauricio Marín
Affiliations:
Computer Engineering Department, University of Magallanes, Chile;Computer Science Department, University of Chile;Computer Engineering Department, University of Magallanes, Chile;Computer Engineering Department, University of Magallanes, Chile
Venue:
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part I
Year:
2006

Citing 10
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Abstract

This paper presents the Evolutionary Geometric Near-neighbor Access Tree (EGNAT) which is a new data structure devised for searching in metric space databases. The EGNAT is fully dynamic, i.e., it allows combinations of insert and delete operations, and has been optimized for secondary memory. Empirical results on different databases show that this tree achieves good performance for high-dimensional metric spaces. We also show that this data structure allows efficient parallelization on distributed memory parallel architectures. All this indicates that the EGNAT is suitable for conducting similarity searches on very large metric space databases.