一种基于改进k近邻算法的空间数据库查询方法

IF 0.6 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS
Huili Xia, Feng Xue
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引用次数: 0

摘要

空间数据库是一种空间信息数据库,是地理信息系统的核心组成部分。针对k近邻(kNN)查询算法的时间复杂度与训练样本规模成正比的问题,提出了一种基于Spark框架和反向k近邻(RkNN)的空间数据库查询方法。首先,基于Spark框架,构建了基于网格和Voronoi图的两层索引结构,并提出了一种高效的过滤和细化处理算法;其次,采用所提算法的滤波步骤获得候选点,采用精炼步骤去除候选点;最后,对不同区域的候选集进行合并,得到最终结果。在实际数据集上的实验结果表明,该方法具有更好的查询性能和稳定性,显著提高了处理速度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Novel Query Method for Spatial Database Based on Improved K-Nearest Neighbor Algorithm
Spatial database is a spatial information database and is the core component of geographic information systems (GIS). Aiming at the problem that time complexity of k-nearest neighbor (kNN) querying algorithms are proportionate to scale of training samples, an efficient query method for spatial database based on the Spark framework and the reversed k-nearest neighbor (RkNN) is proposed. Firstly, based on the Spark framework, a two-layer indexing structure based on grid and Voronoi diagram is constructed, and an efficient filtering and a refining processing algorithm are proposed. Secondly, the filtering step of proposed algorithm is used to obtain the candidates, and the refining step is used to remove the candidates. Finally, the candidate sets from different regions are merged to get the final result. Results of experiments on real-world datasets validate that the proposed method has better query performance and better stability and significantly improves the processing speed.
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来源期刊
International Journal of Decision Support System Technology
International Journal of Decision Support System Technology COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
2.20
自引率
18.20%
发文量
40
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