K-nearest neighbor searching method based on regional coverage

Abstract

The invention provides a k-nearest neighbor searching method based on regional coverage, belonging to the technical field of mobile data indexes. The k-nearest neighbor searching method comprises the steps of: gridding a space, storing data points in corresponding grids, storing grids which are taken as the leaf nodes of a quadtree, meanwhile, storing the grids, which are taken as mobile objects, into a Voronoi image; and during searching, firstly finding out a grid in which an object is located according to coordinates of the object, further finding out the position of the grid in the Voronoi image, structuring the object in the grid according to an ascending order of distances to form a result linked list, meanwhile, putting adjacent grids into an access linked list based on the ascending order of the distances according to the Voronoi image, comparing the distances, and finally finding out K nearest neighbors of the object. In the k-nearest neighbor searching method, an index structure with the Voronoi image and a virtual grid quadtree is comprehensively utilized, and a Hash table is utilized for rapid searching and positioning, so that the searching efficiency is increased.

Description

technical field [0001] The invention belongs to the technical field of mobile data indexing, in particular to a k-nearest neighbor query method based on area coverage. Background technique [0002] With the advancement of science and technology, especially the development of wireless communication technology, Global Positioning System (Global Positioning System, GPS) technology and database technology, mobile object database technology has gradually become a research hotspot in the database field. Among them, the K-nearest neighbor query method for mobile object data is an important area of ​​research. The so-called K-Nearest Neighbor (KNN, K Nearest Neighbor) query refers to finding the K closest objects before a given point in space, where K can be one or more. However, most of the past research work is aimed at K-nearest neighbor queries in a static environment, where the attribute values ​​of the target points remain unchanged or rarely need to be updated. The research ...

AI Technical Summary

Problems solved by technology

ST 2 B-tree is a self-adjustable moving object B + Tree index structure, first in time, ST 2 The B tree will be the underlying B + The tree is divided into two subtrees BT 0 and BT 1 , each moving object selects the corresponding sub-tree for indexing according to the update time, and the two sub-trees manage the moving objects alternately with time; secondly, in terms of space, according to the distribution of moving objects in the area, a reference is selected according to the density point set, and divide the area according to the reference point Voronoi block, and then select different grid granularity in different blocks to index the moving object. For ST-based 2 The nearest neighbor query method of the B-tree adopts the method of continuously expanding the scope of the query radius until the number of moving objects in the query area meets the conditions of the nearest neighbor query. The disadvantage of the method is that the range query algorithm is continuously called, and the time cost is relatively large.

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