Supercharge Your Innovation With Domain-Expert AI Agents!

RkNN (reverse k nearest neighbor) inquiring method based on Voronoi diagram

A query method and nearest neighbor technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve problems such as low efficiency and non-support

Inactive Publication Date: 2013-04-10
SHENYANG JIANZHU UNIVERSITY
View PDF0 Cites 5 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0009] (2) Does not support dynamically changing k value
Therefore, this technique is not suitable for situations where the value of k is not known in advance or may be changed dynamically
[0010] (3) Inefficient update operation

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • RkNN (reverse k nearest neighbor) inquiring method based on Voronoi diagram
  • RkNN (reverse k nearest neighbor) inquiring method based on Voronoi diagram
  • RkNN (reverse k nearest neighbor) inquiring method based on Voronoi diagram

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0036] Step 1: according to query site set, generate corresponding m order Voronoi diagram, method is: existing m order Voronoi diagram generating method; Because of each m order Voronoi diagram in the present invention can query R(k-1)NN, RkNN And the results of R(k+1)NN, so the Voronoi diagrams of order 1, 3, 6, 9... are generated according to the needs of the query. To generate 1, 3, 6, 9... order Voronoi diagrams according to the needs of the query.

[0037] Step 2: Import the query object data set, the method is: read the data file and display the data;

[0038] Step 3: Enter the k value and the coordinates of the query point q to get the RkNN query result; where:

[0039] When k=m, all query objects within the polygon containing station q are the results,

[0040] When k k, then it is the result, otherwise not,

[0041] When k>m, all the query objects in the Voronoi polygons containing the site q are the results; and check the query objects in the adjacent polygons of...

Embodiment 2

[0046] Step 1: Generate Voronoi graphic data according to the CD human landscape site collection, and save the file.

[0047] The document saved by the generate graphics module is as follows Figure 4 As shown, the data in the figure is the data of the third-order Voronoi diagram. Each row of data may describe a Voronoi polygon. The data in each row is divided into three parts, namely the generator, the vertices of the Voronoi polygon and the MBR. Each part of data is separated by ":". Let's look at the first row of data, the 6 sets of numbers before the first ":" are the generators of the polygon. Every two sets of numbers represent a pair of coordinates, because it is a polygon of order 3, and 3 pairs of coordinates represent exactly 3 generators. The data before the second ":" represents the vertices of the polygon, and also every two sets of numbers represent a pair of coordinates. The remaining 4 sets of data represent the MBR of the polygon, the first two groups rep...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses an RkNN (reverse k nearest neighbor) inquiring method based on a Voronoi diagram, and belongs to the technical field of space data inquiring. The method comprises the following steps of step1, according to an inquiring site set, generating the corresponding Voronoi diagram with an m order; step 2, importing a dataset of an inquiring object; step 3, inputting the k value and the coordinates of an inquiring site q, so as to obtain the RkNN inquiring results; and step 4, finishing. The method has the advantages that the double-color RkNN inquiring in the dataset with frequency change is realized, and the results of R(k-1)NN, RkNN and R(k+1)NN can be inquired on the Voronoi diagram with the m order; the pre-calculation amount is reduced; compared with the prior art, the inquiring efficiency is greatly improved; and the advantages are more obvious along with the increase of the quantity of inquiring object sets, and the application performance of the Voronoi diagram is improved.

Description

technical field [0001] The invention belongs to the technical field of spatial data query, in particular to an inverse k-nearest neighbor query method based on a Voronoi diagram. Background technique [0002] The mobile object query technology in the spatial database can be applied to the network with mobile objects such as urban transportation, aerospace, communication network, etc. It can mine information according to a large amount of spatio-temporal data to provide relevant consultation to customers. Typical spatial queries are nearest neighbors (NN) query and k nearest neighbors (k nearest neighbors, kNN) query. For example: Passengers will ask which hotel is the closest to the station; drivers will inquire where the two nearest gas stations are. Reverse k nearest neighbors (reverse k nearest neighbors, RkNN) query is a variant of kNN query, which answers who regards the query object as the nearest neighbor. For example, a series of chain stores in a certain city may p...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
IPC IPC(8): G06F17/30
Inventor 宋晓宇孙焕良许景科王永会赵明
Owner SHENYANG JIANZHU UNIVERSITY
Features
  • R&D
  • Intellectual Property
  • Life Sciences
  • Materials
  • Tech Scout
Why Patsnap Eureka
  • Unparalleled Data Quality
  • Higher Quality Content
  • 60% Fewer Hallucinations
Social media
Patsnap Eureka Blog
Learn More

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2025 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com