基于异构关联的大数据价值密度提升方法

Method of improving big data value density based on heterogeneous association

电信大数据通常分散存储在DPI、OIDD、CRM等多个系统中,且格式、表述和规则在各系统中互不相同;因而,同一对象在不同系统中的多类数据很难被有效识别及完整利用,大数据分析的样本规模和特征维度严重受限,导致分析结果可信度和准确率下降。提出了电信大数据的异构关联方法与实现架构,并进行了方法的流程举例和验证,从用户维度实现了多系统间的数据融合,优化了诸如用户画像等应用的数据样本空间,从而大幅提升电信大数据价值密度。

The big data resources possessed by telecom operators are usually distributed in many different systems, such as DPI, OIDD, CRM. Moreover, the formulation, interpretation and rules of the big data are not always the same in different systems. Therefore, it is difficult to identify and utilize the same object＇s multi-type data in different sys- tems. Big data analysis＇ sample size and dimension are limited, with the decreasing of analysis results＇ reality and accuracy. The methods, architectures and implementation examples of big data＇s heterogeneous association were pre- sented. The data fusion in user-dimension from different systems could optimize the data sample space of applications, such as user portrait. Thus, the value of carrier＇s big data density was greatly improved.