基于march算法的网络多维数据优化存储方法

doi:10.16039/j.cnki.cn22-1249.2021.09.015

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摘要

传统的多维数据优化存储方法存在存储性能较差，信噪比较低的问题。为此，提出一种基于march算法的网络多维数据优化存储方法。根据映射函数，对网络多维数据进行映射函数整数编码；通过映射获得多维数组的坐标值，完成维数据组织构建；依据二进制编码获取更高的网络多维数据存储效率；利用顺序存储与分块存储完成度量数据组织，通过块的压缩存储节约数据存储占用空间；利用march算法测试网络多维数据存储方法，完成存储方法的优化。实验结果表明，在不同压缩比时，该方法在数据存储过程中的最高信噪比可达96；在不同稀疏度时，该方法在数据存储过程中的信噪比依旧较高，具备较优的数据存储性能。

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	胡茂美

关键词: Maarch算法网络多维数据优化存储维数据度量数据

Abstract:

The traditional multi-dimensional data optimized storage methods have the problems of poor storage performance and low signal-to-noise ratio. Therefore, a network multidimensional data optimal storage method based on March algorithm is proposed. According to the mapping function, the network multidimensional data is encoded by integer mapping function.The coordinate values of multidimensional array are obtained by mapping to complete the construction of dimensional data organization. Higher storage efficiency of network multidimensional data is obtained according to binary coding; Sequential storage and block storage are used to organize measurement data, and the occupied space of data storage is saved through block compression storage. Use March algorithm to test the network multidimensional data storage method and complete the optimization of the storage method. The experimental results show that the maximum signal-to-noise ratio of this method in the data storage process can reach 96 at different compression ratios. At different sparsity, the signal-to-noise ratio of this method is still high in the data storage process, and has better data storage performance.

Key words: Maarch algorithm Network Multidimensional data Optimized storage D data Measurement data

出版日期: 2021-09-25 发布日期: 2021-09-25 整期出版日期: 2021-09-25

TP311.13

引用本文:

胡茂美. 基于march算法的网络多维数据优化存储方法 [J]. 吉林化工学院学报, 2021, 38(9): 78-81.
HU Maomei. Optimal Network Multidimensional Data Storage Method Based on MARCH Algorithm . Journal of Jilin Institute of Chemical Technology, 2021, 38(9): 78-81.

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https://xuebao.jlict.edu.cn/CN/10.16039/j.cnki.cn22-1249.2021.09.015 或 https://xuebao.jlict.edu.cn/CN/Y2021/V38/I9/78

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