Spatial data partitioning method in cloud environment

Abstract

The invention discloses a spatial data partitioning method in a cloud environment, belonging to the technical field of computer networks. The method comprises the steps of firstly, uniformly partitioning a spatial data object by using a Hilbert curve coding method, and secondly partitioning adjacent spatial data objects into one type as far as possible by using an improved k-mean value clustering algorithm based on the first step. According to the spatial data partitioning method, the advantages of a conventional mean value method and a k-mean value clustering algorithm are synthesized, the standard in spatial data partitioning is met well, geographic spatial data can be uniformly distributed to map-reduce to be processed so as to establish an R-tree, so that the geographic spatial data index efficiency is improved. And moreover according to the method, the calculation complexity is low and the real-time capability of the algorithm is good.

Description

technical field [0001] The invention relates to a spatial data division method in a cloud environment, belonging to the technical field of computer networks. Background technique [0002] Cloud computing is a distributed system that can distribute computing tasks to multiple machines for processing, and can provide computing power, storage space and information services to various application systems. Now google Companies and Open Source Cloud Computing Platforms hadoop etc. are used map-reduce Parallel Computing Model. This model provides a general and efficient technical framework for the processing of massive data, so it has been more and more widely used in geospatial data query processing, data mining and other fields. [0003] Today, improving the efficiency of geospatial data indexing is a hot issue, and how to find an effective method to build an indexing mechanism on the cloud platform is of great significance. R The tree is a height-balanced tree, using the ...

AI Technical Summary

Problems solved by technology

This method does not satisfy the principle of data volume balance, k The mean clustering algorithm can only ensure that spatially adjacent objects are in the same family, but it cannot guarantee that the size of each family is relatively balanced

[0007] Liu Runtao et al proposed to use k Means Clustering Algorithm Establishment R The tree simply considers the adjacent relationship of spatial data objects, and does not consider the principle of data volume balance, which will make R Some branches of the tree are indexed too frequently, and it is not proposed to use map-reduce model to build R tree, which is simply built in parallel R tree, resulting in inefficient

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