基于大数据平台的网络低效能挖掘分析

Analysis of Network Low Efficiency Mining based on Big Data Platform

大数据越来越受到运营商的重视,如何在既有的海量数据中挖掘更多的“金矿”,需要依托大数据平台,整合不同部门之间的数据,从而得到新的交叉价值。在对网络侧(O域)数据的挖掘中,引入用户侧(B域)数据,通过机器学习算法建立一套智能化的网络低效能评估体系,动态化定位并分析低效能区域,根据逻辑回归算法输出用户数、流量以及收益等维度的低效能场景,针对性地进行网络和用户的潜能挖掘,从而改善低效能区域,提升整体的网络效能与收益。

Big data is attracting more and more attention from operators. How to mine more "gold deposits” from existing massive data needs to rely on the big data platform to integrate data between different departments, so as to obtain new cross-value. In the mining of network side (0 domain) data, user side (B domain) data is introduced, and an intelligent network low-efficiency evaluation system is established by machine learning algorithms. The low-efficiency areas are positioned and analyzed dynamically. According to the low-efficiency scenarios o￡ user number, traffic and revenue dimension output by logistic regression algorithm, the potential of network and user is tapped pertinently, so as to improve the low-efficiency areas and enhance the overall network efficiency and benefits.