Parallel indexing technology for vector QR trees

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 ...

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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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