摘要
大数据质量是实现大数据治理的基础和前提,可在一定程度上影响大数据的价值和效益。如何评估和提升大数据质量,避免错误、缺失、噪声等质量问题是一个亟待解决的重要问题。基于此,文章提出了一种基于深度学习的大数据质量评估与提升方法。该方法可利用深度神经网络对大数据进行特征提取、异常检测、缺失值填充、噪声消除和质量评分等操作,从而有效提高大数据的质量水平和可用性。此外,文章通过基准方法或最新方法对该方法进行了比较和分析。
The quality of big data is the foundation and prerequisite for achieving big data governance,which can to some extent affect the value and benefits of big data.How to evaluate and improve the quality of big data,and avoid quality issues such as errors,omissions,and noise,is an urgent and important issue to be solved.Based on this,the article proposes a deep learning based method for evaluating and improving the quality of big data.This method can utilize deep neural networks for feature extraction,anomaly detection,missing value filling,noise elimination,and quality evaluation of big data,effectively improving the quality level and availability of big data.In addition,the article compares and analyzes this method using benchmark or latest methods.
作者
唐晓晗
TANG Xiaohan(Henan Provincial Government Big Data Center,Zhengzhou 450000,China)
出处
《计算机应用文摘》
2024年第8期105-107,共3页
Chinese Journal of Computer Application
关键词
大数据质量
深度学习
数据质量评估
数据质量提升
big data quality
deep learning
data quality assessment
data quality improvement
