摘要
以实现多种形态高维数据流的高效、精确并行计算为出发点,提出基于粒度理论的高维数据流并行计算方法。使用基于动态粒度的数据流挖掘模型,高效挖掘高维数据流;利用基于局部保持投影原理和主成分分析原理压制高维数据流噪声,减少高维数据流噪声隐患;依据降噪后不同高维数据流特点,采用高维数据流相关性分析并行计算方法,得到高维数据的皮尔逊积差相关系数,实现数据流关联,并基于数据流十字转门模型,定义适合高维数据流分析的滑动数据流窗口模式,实现高维数据流的并行计算。实验结果验证,上述方法挖掘高维数据流的内存消耗低,高维数据流数据去噪能力强,具备较高的高维数据流并行计算精度,且并行计算效率高。
This paper proposes a parallel computing method of high-dimensional data streams based on granularity theory for achieving efficient and accurate parallel computing of multi-dimensional data streams. The data stream mining model based on dynamic granularity was adopted to mine high-dimensional data stream efficiently. The noise of high-dimensional data stream was compressed based on the principles of locality preserving projection and major constituent analysis, lowing the hidden trouble of high-dimensional data stream noise. Based on the characteristics of different high-dimensional data streams after noise reduction, the parallel computing method was analyzed in detail to get the Pearson product difference correlation coefficient of high-dimensional data via the correlation of high-dimensional data streams, realizing the data stream correlation. According to the data flow turnstile model, the sliding data flow window mode suitable for high-dimensional data flow analysis was defined, and the parallel computing of high-dimensional data flow was finally achieved. The results show that the proposed method has low memory consumption, excellent denoising ability, high parallel computing accuracy and efficiency.
作者
路晶
胡顺仿
LU Jing;HU Shun-fang(China Civil Aviation Flight Academy,Guanghan Sichuan 618307,China;Yunnan National University,Kunming Yunnan 650000,China)
出处
《计算机仿真》
北大核心
2021年第5期246-249,422,共5页
Computer Simulation
基金
民航安全能力基金项目(省部级)(ASSA2019/20)
民航飞行技术与飞行安全重点实验室自主研究项目(校级)(FZ2020ZZ02)。
关键词
粒度理论
动态粒度
高维数据流
皮尔逊积差
并行计算
Granularity theory
Dynamic granularity
High dimensional data stream
Pearson product difference
Parallel computing
