摘要
组最近邻查询由于涉及多个查询点,因此比传统的最近邻查询更为复杂.充分考虑查询点的分布特征以及它们构成的几何图形的性质和特点,给出组最近邻所应满足的条件及判断组最近邻的理论方法.提出基于Voronoi图的组最近邻查询的VGNN算法,可以精确求解查询点集的最近邻.对于查询点不共线的情况,该算法的查询方式是以一点为中心、向外扩张式的;对于查询点共线的情况,该算法给出搜索范围,限定了参与计算的数据点的个数.给出基于Voronoi图的VTree索引.实验结果表明,基于VTree索引的VGNN算法具有较好的性能,并且当查询点不共线时,其性能具有较高的稳定性.
Group nearest neighbor query is more complex than the traditional nearest neighbor query because it contains one more query points. Its objective is to find a data point that has the smallest sum of distances to all query points. The conditions that group nearest neighbor should be satisfied and the theories that valuate the group nearest neighbor are put forward, which are obtained by considering the distribution characteristics of query points and the qualities of geometric figures that they form. The VGNN algorithm is presented to deal with group nearest neighbor query based on Voronoi diagrams. It can get the query points＇ nearest neighbor exactly by using the VGNN algorithm. When the query points are non-collinear, the query way of the VGNN algorithm is firstly to estimate one data point that is the query points＇ nearest neighbor possible, and then to outspread around the data point to find the final result. And it also concludes the search region when the query points are collinear to limit the quantity of data points which are included in the computation. The VTree index grounded on Voronoi diagrams is brought forward. Experimental results show that the VGNN algorithm based on the VTree index has better performance, and it is more stable when the query points are non-collinear.
出处
《计算机研究与发展》
EI
CSCD
北大核心
2010年第7期1244-1251,共8页
Journal of Computer Research and Development
基金
黑龙江省自然科学基金项目(F200601)
作者简介
（sundongpu@sina．com）孙冬璞，1979年生．博士研究生，主要研究方向为时空数据库．
郝忠孝，1940年生，教授，博士生导师，主要研究方向为数据库系统与理论、空值理论、无环数据库、主动数据库、时空数据库理论等．
