社交网络中大数据非结构稳定性检测仿真研究
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摘要
为了检测非结构化大数据在社交网络中的稳定性,需要研究大数据非结构稳定性检测方法。采用当前方法检测社交网络中大数据非结构的稳定性时,检测所用的时间较长,得到的检测结果与实际不符,检测过程中的能量消耗较高,存在检测效率低、检测结果准确率低和能耗低的问题。提出社交网络中大数据非结构稳定性检测方法,根据社交网络数据之间的相关性,采用一元线性归回模型构建社交网络大数据采集模型。通过不同决策者对大数据属性的评价值,计算属性评价值的熵,在数据非结构稳定性和属性权重之间关系的基础上,根据计算结果建立社交网络中大数据非结构稳定性检测模型,完成社交网络中大数据非结构稳定性的检测。仿真结果表明,所提方法的检测效率高、检测结果准确率高、能耗低。
In order to detect the stability of non-structural big data in social network, this paper put forward a method to detect the non-structural stability of big data in social network. According to the correlation between social network data, the unitary linear regression model was used to construct the model to collect big data in social network. Then, evaluation value of big data attribute was calculated by different decision makers. The entropy of attribute evaluation value was calculated. Based on the relationship between the non-structural stability of data and the attribute weight, the non-structural stability detection model for big data in social network was established. Finally, the non-structural stability detection of big data in social network was completed. Simulation results show that the proposed method has high detection efficiency, high accuracy and low energy consumption.
In order to detect the stability of non-structural big data in social network, this paper put forward a method to detect the non-structural stability of big data in social network. According to the correlation between social network data, the unitary linear regression model was used to construct the model to collect big data in social network. Then, evaluation value of big data attribute was calculated by different decision makers. The entropy of attribute evaluation value was calculated. Based on the relationship between the non-structural stability of data and the attribute weight, the non-structural stability detection model for big data in social network was established. Finally, the non-structural stability detection of big data in social network was completed. Simulation results show that the proposed method has high detection efficiency, high accuracy and low energy consumption.
引文
[1] 薛鑫.大型数据库中异常信息检测仿真研究[J].计算机仿真,2017,34(8):399-402.
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[5] 邢海龙,高长元,张树臣.基才系统动力学的大数据联盟稳定性模型构建与仿真研究[J].情报杂志,2017,36(10):159-165.
[6] 甘敦文.基于贝叶斯聚类的高速列车横向稳定性识别方法及其实时在线监测装置[J].中国铁道科学,2016,37(4):139-144.
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[9] 吴敬生,王靖,杜吉祥.基于稀疏重构权的错误标注数据检测方法[J].计算机工程与科学,2017,39(11):2115-2121.
[10] 陈睿,杨孟飞,郭向英.基于变量访问序模式的中断数据竞争检测方法[J].软件学报,2016,27(3):547-561.
[2] 祝恩国,刘宣,葛磊蛟.用电信息采集系统非结构化数据管理设计[J].电力系统及其自动化学报,2016,28(10):123-128.
[3] 吕劲松,等.大数据环境下商业银行审计非结构化数据研究[J].软科学,2017,31(1):141-144.
[4] 程雯,杨阳,陈峰.基于社交网络的Web缓存架构研究[J].电子设计工程,2017,25(17):191-193.
[5] 邢海龙,高长元,张树臣.基才系统动力学的大数据联盟稳定性模型构建与仿真研究[J].情报杂志,2017,36(10):159-165.
[6] 甘敦文.基于贝叶斯聚类的高速列车横向稳定性识别方法及其实时在线监测装置[J].中国铁道科学,2016,37(4):139-144.
[7] 蔡美,刘波.基于蚁群算法的异常数据检测方法[J].计算机工程,2016,42(8):166-169.
[8] 顾伟华,黄天印,郭鹏.面向大数据的城市轨道交通非结构化数据管理[J].城市轨道交通研究,2016,19(11):77-80.
[9] 吴敬生,王靖,杜吉祥.基于稀疏重构权的错误标注数据检测方法[J].计算机工程与科学,2017,39(11):2115-2121.
[10] 陈睿,杨孟飞,郭向英.基于变量访问序模式的中断数据竞争检测方法[J].软件学报,2016,27(3):547-561.