An index data structure for searching in metric space databases

被引：0

作者：

Uribe, Roberto ^{[1
]}

Navarro, Gonzalo ^{[1
]}

Barrientos, Ricardo J. ^{[1
]}

Marin, Mauricio ^{[1
]}

机构：

[1] Univ Magallanes, Dept Comp Engn, Punta Arenas, Chile

来源：

COMPUTATIONAL SCIENCE - ICCS 2006, PT 1, PROCEEDINGS | 2006年 / 3991卷

关键词：

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

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.

引用

页码：611 / 617

页数：7