Selectivity Estimation of Reverse k-Nearest Neighbor Queries

被引：0

作者：

Steinke, Michael ^{[1
]}

Niedermayer, Johannes ^{[1
]}

Kroeger, Peer ^{[1
]}

机构：

[1] Univ Munich, Inst Comp Sci, D-81377 Munich, Germany

来源：

DATABASE SYSTEMS FOR ADVANCED APPLICATIONS, DASFAA 2014, PT II | 2014年 / 8422卷

关键词：

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper explores different heuristics to estimate the selectivity of a reverse k-nearest neighbor query. The proposed methods approximate the number of results for a given RkNN query, providing a key ingredient of common (relational) query optimizers. A range of experiments evaluate the quality of these estimates compared to the true number of results on both real and synthetic data, analyzing the potentials of the proposed approximations to make accurate predictions that can be used to generate efficient query execution plans.

引用

页码：108 / 123

页数：16

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