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Front. Inform. Technol. Electron. Eng.  2015, Vol. 16 Issue (2): 98-108    DOI: 10.1631/FITEE.1400165
    
不确定空间二维范围查询的Kd-树和四叉树分解
Ahmet Sayar, Süleyman Eken, Okan ?ztürk
Department of Computer Engineering, Kocaeli University, Kocaeli 41380, Turkey
Kd-tree and quad-tree decompositions for declustering of 2D range queries over uncertain space
Ahmet Sayar, Süleyman Eken, Okan ?ztürk
Department of Computer Engineering, Kocaeli University, Kocaeli 41380, Turkey
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摘要: 目的:通过点数据二维范围查询性能测试评价空间划分方法(kd-树和四叉树)的可行性和有效性。
创新:基于不确定空间创建有效索引,将范围查询分解成多个等尺寸子范围求解。
方法:将数据集合定义为二维平面上的点,进行范围查询(窗口查询)。根据数据大小(相对大或相对小)及其分布(随机或偏斜)测试四种方案(图3-8)。相同的测试同时应用于真实数据(Turkey’s points of interest data,图9-11)。
结论:所提算法有助选取由索引表格创建的最佳划分组合,最小化给定查询响应时间。四叉树索引平行度更高,这很大程度上由于四叉树更清晰地揭示数据空间位置。
关键词: Kd-树四叉树空间划分空间索引范围查询查询优化    
Abstract: We present a study to show the possibility of using two well-known space partitioning and indexing techniques, kd trees and quad trees, in declustering applications to increase input/output (I/O) parallelization and reduce spatial data processing times. This parallelization enables time-consuming computational geometry algorithms to be applied efficiently to big spatial data rendering and querying. The key challenge is how to balance the spatial processing load across a large number of worker nodes, given significant performance heterogeneity in nodes and processing skews in the workload.
Key words: Kd tree    Quad tree    Space partitioning    Spatial indexing    Range queries    Query optimization
收稿日期: 2014-05-05 出版日期: 2015-01-29
CLC:  TP391  
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Ahmet Sayar
Süleyman Eken
Okan ?ztürk

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Ahmet Sayar, Süleyman Eken, Okan ?ztürk. Kd-tree and quad-tree decompositions for declustering of 2D range queries over uncertain space. Front. Inform. Technol. Electron. Eng., 2015, 16(2): 98-108.

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http://www.zjujournals.com/xueshu/fitee/CN/10.1631/FITEE.1400165        http://www.zjujournals.com/xueshu/fitee/CN/Y2015/V16/I2/98

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