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