云环境下改进自回归模型的网络数据去重仿真  

Simulation of Network Data Deduplication Using Improved Autoregressive Models in Cloud Environment

作　　者：胡艳华 张春玉[2] 崔亚楠 倪志平 HU Yan-hua;ZHANG Chun-yu;CUI Ya-nan;NI Zhi-ping(College of Information Science and Engineering,Liuzhou Institute of Technology,Liuzhou Guangxi 545616,China;College of Information Engineering,Xizang Minzu University,Xianyang Shannxi 712082,China)

机构地区：[1]柳州工学院信息科学与工程学院,广西柳州545616 [2]西藏民族大学信息工程学院,陕西咸阳712082

出　　处：《计算机仿真》2024年第1期443-446,536,共5页Computer Simulation

基　　金：国家自然科学基金项目(62262062);2021年度广西高校中青年教师科研基础能力提升项目(2021KY1710)。

摘　　要：云环境网络数据去重过程中,若不能及时对网络数据实施噪声抑制,会直接降低数据的去重效果,为提升数据去重精度,提出基于自回归模型的云环境中网络数据去重算法。建立云环境弹性空间模型,确定网络数据的空间自相关度量值完成数据去噪,基于去噪结果详细分析云环境中网络数据属性特征;根据提取的属性特征对云环境中网络数据聚类处理,结合自回归模型建立网络冗余数据预测模型,精准预测出云环境中的网络冗余数据,并对其进行剔除处理,实现网络数据的精准去重。实验结果表明,使用该方法开展数据去重时能够有效去除网络数据中的冗余数据,去重效果较好。In the process of network data deduplication in cloud environment,untimely noise suppression of network data can directly reduce the effect of data deduplication.To improve the accuracy of data deduplication,this article put forward a network data deduplication algorithm in cloud environment based on autoregressive model.Firstly,a flexible spatial model of cloud environment was built.After determining the spatial autocorrelation measure of network data,the data denoising was completed.Based on the denoising result,the attribute features of network data in cloud environment were analyzed in detail.According to the extracted features,network redundant data was clustered.Moreover,a prediction model of network redundant data was constructed by the autoregressive model,thus accurately predicting and removing the network redundant data in cloud environment.Finally,network data was precisely deduplicated.The experimental results show that this method can effectively remove redundant data from network data during data deduplication,and has good deduplication effect.

关 键 词：自回归模型 云环境 网络数据 去重算法 冗余数据预测模型 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]