A Spatial Data Indexing Method in Cloud Computing Environment

Abstract

The invention discloses a method for indexing spatial data in a cloud computing environment. The method comprises the following steps of: 1) establishing a data storing table on the basis of the spatial data; 2) building an R+-tree index on the basis of spatial object distribution; 3) building a grid index on the basis of spatial object coverage range; 4) carrying out dimensionality reduction andcoding on the R+-tree index and the grid index by utilizing a Z curve; 5) establishing a code mapping table of R+- trees and grids; 6) establishing an index table on the basis of R+- tree codes and spatial objects ID; and 7) carrying out spatial range query. The method has the beneficial effects of making full use of the data organization characteristics of a NoSQL (Not Only Structured Query Language) database, optimizing the traditional spatial index and realizing the efficient access of the spatial data in the cloud environment.

Description

technical field [0001] The invention relates to the management field of spatial data, in particular to a method for constructing a spatial data index in a cloud computing environment. Background technique [0002] GIS is playing an increasingly important role in the development of modern cities. Spatial information has become an important part of the data source of digital city infrastructure construction. People need convenient and efficient spatial information service support in various aspects such as economy, transportation, politics and culture. At present, space data mainly comes from high-precision space sensors, satellites and GPS equipment, and the data is updated frequently and on a large scale. Due to the explosive growth of spatial data and the complex nature of geospatial models, spatial data operations often have high time complexity and consume a lot of computing resources. With the development of spatial information sharing services, the future integrated G...

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Problems solved by technology

[0003] However, traditional spatial data engines combined with large-scale relational database spatial data management methods have disadvantages such as low query efficiency, difficult expansion, and high maintenance costs, which cannot meet the needs of efficient access to massive spatial data

With the development of spatial information cloud computing technology, NoSQL databases play an increasingly important role in spatial data management, but there are a lot of performance bottlenecks in the classical spatial data index when organizing spatial data in a distributed environment

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