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Parallel indexing technology for vector QR trees

A QR tree and vector technology, applied in the field of spatial data management and retrieval, can solve problems such as difficult to solve inter-process load balancing, unable to break through the bottleneck of master node access, etc.

Inactive Publication Date: 2013-04-17
吴立新 +2
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  • Application Information

AI Technical Summary

Problems solved by technology

Relevant scholars have studied the parallel algorithm of R tree [10-13] , such as the GPR tree [10] , Master-clientR tree [11] , Upgraded Parallel R tree [12] etc., but these parallel indexes still cannot break through the bottleneck of master node access, and it is difficult to solve the load balancing problem between processes

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  • Parallel indexing technology for vector QR trees
  • Parallel indexing technology for vector QR trees
  • Parallel indexing technology for vector QR trees

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Embodiment Construction

[0073] An example using QR tree parallel indexing. The size of the spatial data range is Rect{(0, 0), (8,000, 8,000)}, and the number of spatial object sets SpatialDataSets=512,000.

[0074]1. Taking 64 (8×8) multi-channels as an example, the process of dividing the above data set by the matrix MultiChannelArray is as follows Figure 7 Shown:

[0075] 1) Initialize the spatial dataset with a range of Rect{(0, 0), (8,000, 8,000)} to 64 (8×8) channels (such as Figure 7 A), the length of each sub-channel ChannelLength=1,000, the width of ChannelWidth=1,000, each channel is initially assigned data record value NumofObjects=0;

[0076] 2) Traverse the spatial object set to obtain the minimum bounding rectangle (MBR) of each object. Calculate the channel MultiChannelArray[i][j] (0≤i Figure 7 B;

[0077] 3) MultiChannelArray[i][j] of each channel rotates and divides the entered spatial objects into each process in sequence (rotation method) until the spatial data set of the chan...

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Abstract

The invention discloses a parallel indexing technology for vector QR trees. Efficient indexing for massive spatial data is particularly important in a novel parallel environment, and the spatial data indexing efficiency is critical in measuring the integral performance of a spatial database. The main node access bottleneck cannot be broken through by an existing parallel indexing technology, and load balance among processes is difficult to realize. In order to solve the problems, the parallel indexing technology for collaboration among vector Q trees and vector R trees includes 1), dividing adjacent spatial data sets into different processes by a multi-channel method and realizing load balance of tasks among the processes; 2), constructing the QR trees on the basis of central points of minimum bounding rectangles of spatial objects, and optimizing indexing paths; 3), performing master-slave storage for the QR trees and optimizing index reading; and 4), collaboratively retrieving the QR trees and breaking the root node access bottleneck. The parallel indexing technology for the QR trees comprises spatial object set division, QR tree construction, master-slave storage and collaborative retrieval for the QR trees. The parallel indexing technology has the advantage that an efficient vector spatial retrieval method can be provided for developing and serving single-unit and multi-core or integrated-core massive data spatial indexing software in a high-performance cluster environment via parallel indexing for the QR trees.

Description

technical field [0001] The invention relates to the field of spatial data management and retrieval, in particular to a parallel indexing technology for massive vector spatial data in a parallel environment. Background technique [0002] Spatial data has the characteristics of complexity, abstraction, multi-temporal, polymorphic and unstructured. Spatial index is the key to spatial data retrieval, sharing and service. Efficient indexing of massive spatial data in spatial data service is particularly important. At present, spatial indexes mainly include grid indexes [1，2] ,Quadtree [3，4] (Q tree) and R tree [5，6] . Among them, the grid index is mainly for raster data, and the Q-tree and R-tree are mainly for vector data. Q-tree is a type of indexing mechanism based on space division organization index structure. Q-tree is built for line and surface set, which has large data redundancy and low retrieval efficiency, but it is high-efficiency for retrieval by building Q-tree ...

Claims

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Application Information

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IPC IPC(8): G06F17/30
Inventor 吴立新杨宜舟郭甲腾
Owner 吴立新
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