动态受限区域内的单纯型连续近邻链查询方法

Simple Continues Near Neighbor Chain Query in Dynamic Constrained Regions

受限区域内的单纯型连续近邻链查询在空间数据挖掘、数据的相似分析和推理、空间数据库等方面具有重要的作用。为了弥补已有方法的不足,详细研究了动态受限区域内的单纯型连续近邻链查询方法。基于计算几何中的Voronoi图给出了VOR_IN_CRSCNNC算法、VOR_EX_CRSCNNC算法和VOR_DE_CRSCNNC算法。进一步进行了实验比较和分析。理论研究和实验分析表明,所提出的算法在查询过程中减少了数据逐一筛选和判断的冗余计算,在处理空间数据量较大、初始受限区域数据量较多、受限区域形状较为复杂的单纯型连续近邻链查询方面具有较大的优势。

The simple continues near neighbor chain query in the constrained regions（CRSCNNC-Query） has important significance in the spatial data mining,similarity analysis and reasoning of data,spatial database etc.To remedy the deficiency of the existing work,the simple continues near neighbor chain query in the dynamic constrained regions was studied respectively.The VOR_IN_CRSCNNC algorithm,VOR_EX_CRSCNNC and the VOR_DE_CRSCNNC algorithm were presented based on the Voronoi diagram.Furthermore,the performance of the methods were analyzed and compared by experiment.The theatrical study and the experimental results show that the redundant calculation is reduced and the algorithms hold large advantage at the big data sets and the regions with complex shapes.