摘要： 研究预定数据链规模的单纯型连续近邻链(SCNNC)查询问题，基于Hilbert曲线，提出SCNNC_H_SS算法，将已处理过的数据点从数据集中进行剔除，可减少大量冗余计算。为对SCNNC进行动态维护和更新，提出SCNNC_H_CS算法。理论分析和实验结果表明，在数据集和待查近邻链的规模较大时，相比基于传统树索引结构的方法，该算法具有更高的查询效率。
关键词:
空间数据库,
空间数据挖掘,
最近邻查询,
连续近邻链,
R树,
Hilbert曲线
Abstract: This paper researches the Simple Continues Near Neighbor Chain(SCNNC) query with predestination data chain size, based on Hilbert curve, the SCNNC_H_SS algorithm is proposed. The redundant data information can be duly deleted and the number of the effective data point is decreased with operation of the algorithm. The redundant computation is avoided. To maintenance and update the SCNNC, the SCNNC_H_CS algorithm is given. Theatrical analysis and experimental results show that when the scale of data set and the chain are great, the algorithm is superior to the methods based on the tree index structure.
Key words:
spatial database,
spatial data mining,
nearest neighbor query,
continues near neighbor chain,
R tree,
Hilbert curve
中图分类号:
张丽平, 李林, 李松, 郝晓红. 预定数据链规模的单纯型连续近邻链查询[J]. 计算机工程, 2012, 38(10): 51-53.
ZHANG Li-Beng, LI Lin, LI Song, HAO Xiao-Gong. Simple Continues Near Neighbor Chain Query with Predestination Data Chain Size[J]. Computer Engineering, 2012, 38(10): 51-53.