Online Updating Algorithms of Statistical Methods for Big Data

被引：0
|
作者：
Li, Yihao [1 ]
Wang, Jin [2 ,3 ]
机构：
[1] Huazhong Univ Sci & Technol, Dept Math, Wuhan, Hubei, Peoples R China
[2] Valdosta State Univ, Dept Math, Valdosta, GA USA
[3] Valdosta State Univ, Data Sci Lab, Valdosta, GA USA
来源：
2019 2ND INTERNATIONAL CONFERENCE ON COMPUTING AND BIG DATA (ICCBD 2019) | 2019年
关键词：
Online Algorithm; Big Data; Linear Regression; Skewness; Kurtosis; Sample Moment;
D O I：
10.1145/3366650.3366667
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, we discuss online updating algorithms for Big Data. One of the main challenges of Big Data is the limitation of data storage. In the Big Data stream environment, online computation sometimes requires fast updates without the use of historical data. The focus of this research is on efficient online update algorithms for basic statistical computations, including mean, variance, covariance, skewness, kurtosis, confidence interval, test statistic, and linear regression. We demonstrate the implementation of R Language through a linear regress example.
引用
收藏
页码：81 / 85
页数：5
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