K nearest neighbor search method for variable weight network

A search method and k-nearest neighbor technology, applied in the information field, can solve the problems of increasing server load and repeated searches, and achieve the effect of reducing the number, narrowing the search range, and avoiding frequent adjustment of indexes and constraints

Inactive Publication Date: 2012-12-05
SHANGHAI UNIVERSITY OF ELECTRIC POWER
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Problems solved by technology

On the one hand, the server must constantly call and update the constraints of the branch and bound method and the initialization process of the Voronoi diagram, which will lead to a large number of frequent updates and increase the burden on the server
On the other hand, when faced with tens of thousands of query requests, currently based on the current query k Nearest neighbor queries will inevitably lead to a large number of repeated searches

Method used

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  • K nearest neighbor search method for variable weight network
  • K nearest neighbor search method for variable weight network
  • K nearest neighbor search method for variable weight network

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example

[0063]Example: basic data structure The data structure of a road node is shown in Table 2, which mainly includes the id value, coordinates of the node and the id value of the road section connected to it. The variable id is used as the primary key to uniquely identify the road node; the number of roadIds adjacent to it is specified by adjNodeNum. The starting point of each road segment is the road node, and the end point is another adjacent road node. In the actual road network, the number of adjacent road segments of a road node is generally not more than 5.

[0064] Table 2

[0065] serial number variable type illustrate 1 id unsigned int Primary key, identifying the road node serial number 2 x CoordinateType Coordinate type (double or unsigned int), abscissa 3 y CoordinateType Coordinate type (double or unsigned int), ordinate 4 adjNodeNum unsigned int Record the number of road segments connected to this node 5 r...

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Abstract

The invention relates to a k nearest neighbor search method for a variable weight network. The method comprises the steps as follows: routing thinking in a computer network is led in k nearest neighbor query, and predecessor/subsequent information in shortest path tree obtained in computation is taken as history data and is saved in a routing table by designing an update strategy of the routing table. Due to the dynamical update strategy of the routing table, the routing mechanism can adapt to dynamic changes of local road weights in a road net. The k nearest neighbor search method provides the candidate set screening rule and the road net area cutting rule of a non-mobile object as well as the position prediction method and the candidate set screening rule of a mobile object in the dynamical road net, effectively reduces the number of candidate points and narrows down the search range of the k nearest neighbor query, and avoids the defect that indexing and constraint conditions are adjusted frequently because the changes of a k nearest neighbors algorithm are caused by the road weights continuously in the conventional dynamical road net extended from a static road net.

Description

technical field [0001] The present invention relates to an information technology, in particular to a variable weight network k Neighbor search method. Background technique [0002] k The nearest neighbor query problem is a very important geometric problem and has practical applications in many fields. Such as data mining, document retrieval, DNA matching, spatial database and multimedia database, etc. With the development of geographic information system (GIS), computer-aided design (CAD), multimedia system, medical and satellite image data processing and other fields, spatial databases and spatial data k The research of nearest neighbor query has also become a research hotspot in recent years. For example, a user may click on a specific location on the computer's electronic map and ask to find the five closest objects to it. Due to the huge amount of spatial data, complex data structure, and expensive operation costs (including computing overhead and I / O overhead, etc...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30
Inventor 张栋良
Owner SHANGHAI UNIVERSITY OF ELECTRIC POWER
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