Journal of Frontiers of Computer Science and Technology ›› 2022, Vol. 16 ›› Issue (2): 348-358.DOI: 10.3778/j.issn.1673-9418.2009032

• Database Technology • Previous Articles     Next Articles

Groups Nearest Neighbor Query of Mixed Data in Spatial Database

JIANG Yiying, ZHANG Liping, JIN Feihu+(), HAO Xiaohong   

  1. College of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China
  • Received:2020-09-14 Revised:2020-11-27 Online:2022-02-01 Published:2021-01-12
  • About author:JIANG Yiying, born in 1995, Ph.D. candidate, student member of CCF. Her research interests include spatial database and big data.
    ZHANG Liping, born in 1976, Ph.D., associate professor. Her research interests include spatio-temporal database and information data security.
    JIN Feihu, born in 1973, Ph.D., associate professor. His research interests include spatio-temporal database, big data and artificial intelligence.
    HAO Xiaohong, born in 1969, M.S., expert experimenter. Her research interests include data-base theory and application, spatial reasoning, spatial relations and data mining.
  • Supported by:
    National Natural Science Foundation of China(61872105);Science Foundation of Heilongjiang Province(LH2020F047);Science and Technology Research Project of Heilongjiang Provincial Department of Education(12531z004)


蒋祎莹, 张丽平, 金飞虎+(), 郝晓红   

  1. 哈尔滨理工大学 计算机科学与技术学院,哈尔滨 150080
  • 通讯作者: + E-mail:
  • 作者简介:蒋祎莹(1995—),女,黑龙江齐齐哈尔人,博士研究生,CCF学生会员,主要研究方向为空间数据库、大数据。
  • 基金资助:


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.

Key words: geographic information system, spatial database, group nearest neighbor, mixed data, mixed data Voronoi diagram



关键词: 地理信息系统, 空间数据库, 组最近邻, 混合数据, 混合数据Voronoi图

CLC Number: