Vector data preorder quadtree encoding and indexing method based on key/value nosql database
A vector data and quadtree technology, applied in the field of spatial databases, can solve the problems of not being able to effectively utilize the advantages of Key/Value type NoSQL database sequential storage, affecting query efficiency, etc., achieve consistent physical storage order, improve query efficiency, and reduce Effects of I/O operations
- Summary
- Abstract
- Description
- Claims
- Application Information
AI Technical Summary
Problems solved by technology
Method used
Image
Examples
Embodiment 1
[0024] Such as figure 1 , 2 , 3 and 4, in the present embodiment, this vector data pre-order quadtree encoding and indexing method based on the Key / Value type NoSQL database of the present invention:
[0025] Step 1: Complete quadtree space division and preorder quadtree node encoding;
[0026] (a) Complete quadtree space division;
[0027] Set the quadtree height maxLevel=4. Divide the root node into four to obtain four sub-nodes of equal size, and then recursively divide the four sub-nodes until the maximum level is equal to the height of the quadtree, and obtain a complete quadtree with a height of maxLevel;
[0028] (b) Quadtree pre-order coding;
[0029] Perform pre-order encoding on the quadtree, the root node is encoded as 1, and the quadtree pre-order encoding with height = 4 is obtained;
[0030] Step 2: Vector data prefix encoding and index construction;
[0031] (a) Prefix encoding: For each vector object 1 (0001), 2 (0002), 3 (0003), 4 (0004), 5 (0005), 6 (00...
PUM
Login to View More Abstract
Description
Claims
Application Information
Login to View More 