空间数据库中混合数据组最近邻查询

Groups Nearest Neighbor Query of Mixed Data in Spatial Database

现有的组最近邻查询方法主要将空间中数据对象抽象为点或线段进行处理。但在现实应用中,仅仅将空间对象抽象为点或者线段,往往会影响查询的精度及效率。针对现有的组最近邻查询方法无法直接有效地处理混合数据组最近邻查询的不足,提出空间数据库中混合数据组最近邻查询方法。首先提出了混合数据Voronoi图的概念和性质。接着基于混合数据Voronoi图对混合数据集进行剪枝,针对查询对象数量为1和查询对象数量大于1的情况分别给出了相应的剪枝算法。利用所提的剪枝算法能有效去除不可能成为结果的数据对象,得到候选集合。在精炼过程中根据各个数据对象之间的位置关系给出相应的距离计算方法,通过比较候选集中数据对象到各个查询对象的距离之和,最终得到正确的查询结果。理论研究和实验表明,所提算法能够准确、有效地处理混合数据组最近邻查询问题。

The existing group nearest neighbor query methods mainly abstract data objects in space as points or line segments for processing. However, in real applications, simply abstracting spatial objects into points or line segments often affects the accuracy and efficiency of the query. In view of the shortcomings that the existing group nearest neighbor query method cannot directly and effectively deal with the group nearest neighbor query of the mixed data, the group nearest neighbor query method of the mixed data in the spatial database is proposed in this paper. Firstly, the concept and properties of the mixed data Voronoi diagram are proposed. Then the mixed data set is pruned based on the mixed data Voronoi diagram. The corresponding pruning algorithm is given for the case that the number of query objects is 1 and the number of query objects is greater than 1. The proposed pruning algorithm can effectively remove the impossible resultant data objects and get the candidate set. In the refining process, a corresponding distance calculation method is given according to the position relationship between data objects, and the correct query result is finally obtained by comparing the sum of the distance between the data object in the candidate set and each query object. Theoretical research and experiments show that the proposed algorithm in this paper can accurately and effectively deal with the group nearest neighbor query problem of mixed data.