摘要
传统的数据特征采集方法能够对数据的属性特征以及专属特征进行快速的深层识别采集,但是面对大数据分析环境下,由于大数据的特征提取过程比较繁琐,同时属性特征比较多,是用传统的数据特征采集方法已经不在适用,针对上述情况,提出一种基于大数据分析的海量数据特征智能采集方法。使用对应的节点部署模型代替传统的数据特征采集方法,有效的加强了对海量大数据的特征识别能力,避免出现特征分析错误以及数据干扰,优化了多特征融合采集算法,避免了采集过程特征提取不全的现象。为了有效的验证提出的基于大数据分析的海量数据特征智能采集方法的有效性,设计了对比仿真实验,通过实验数据的分析,有效的验证了提出的数据特征采集方法的有效性。
The traditional characteristics of the data acquisition method can attribute characteristics and exclusive features of data quickly and deep recognition, but in the face of big data analysis of the environment, because of the large data feature extraction process is tedious, attribute characteristics at the same time is more, is characteristic with the traditional data acquisition methods are no longer appli- cable, according to the above situation, based on a large mass characteristics of intelligent data acquisition methods of data analysis. Using the corresponding node deployment model instead of the traditional data acquisition model, effectively strengthen the ability to recognize the characteristic of large amounts of big data, avoid the happening of the characteristic analysis of error and data interference, multiple feature fusion acquisition algorithm was optimized, avoid the acquisition process of feature extraction is not complete. In order to effectively validation of the proposed based on analysis of large data the validity of the method of mass data characteristics of intelligent acquisition, design the contrast simulation experiment, through the analysis of experimental data, the effective verification of the validity of the method of characteristic collection of data is proposed.
出处
《自动化与仪器仪表》
2017年第11期69-71,共3页
Automation & Instrumentation
关键词
大数据分析
海量数据特征
智能采集方法
big data analysis
huge amounts of data characteristic
intelligent sampling method
