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.
引文
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