Research on Group Reverse Farthest Neighbour Query Algorithm

Abstract

In recent years, the demand for location-based services is growing. From resource tracking to personal life assistance, spatial data query technology plays an important role, especially the application of reverse nearest neighbor query technology in decision support, resource allocation optimization, data mining. By contrast, the reverse farthest neighbor query has become a more and more hot topic in the research of spatial database theory in recent years. The purpose of the reverse farthest neighbor query is to obtain the final result sets with the given point as its farthest neighbor, which is specially used to solve the problem of weak influence set in space. This paper gives a query optimization algorithm based on the least covering circle. The algorithm firstly introduces the minimal covering circle algorithm. Secondly, the p-ray pruning algorithm is used to filter the candidate set in the second level, and the candidate set is modified by the reverse far-neighborhood range query algorithm to obtain the final result set, which solves the problem in Euclidean effectively. Through specific comparative experimental analysis, c-grfn algorithm has good query performance not only in random distributed environment, but also in Gaussian distribution. It usually has 20% advantages of time in distributed space. As the data set increases, the time advantage becomes more obvious