Groups Nearest Neighbor Query of Mixed Data in Spatial Database

Abstract

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.

Keywords

• |geographic information system|spatial database|group nearest neighbor|mixed data|mixed data voronoi diagram