NETWORK FAULT DIAGNOSIS USING DATA MINING CLASSIFIERS
NETWORK FAULT DIAGNOSIS USING DATA MINING CLASSIFIERS
复制标题
使用数据挖掘分类器进行网络故障诊断
DOI：
10.5121/csit.2015.50703
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
E. Rozaki
中科院分区：
文献类型：
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作者：
E. Rozaki
由于智能手机用户数量的增加以及依赖移动的数据网络的人数的增加，移动的网络面临着比以往任何时候都更大的压力。随着用户数量的增加，服务质量问题对于网络运营商来说变得更加重要。必须在几分钟内发现移动的网络中降低服务质量的故障，以便解决问题并使网络恢复最佳性能。提出了一种基于决策树、规则和贝叶斯分类器的网络故障可视化自动诊断方法。使用数据挖掘技术，该模型分类优化标准的基础上的关键性能指标度量，以确定网络故障，支持最有效的优化决策。其目标是帮助无线提供商本地化关键性能指标警报，并确定应首先解决哪些服务质量因素以及在哪些位置解决这些因素。
Mobile networks are under more pressure than ever before because of the increasing number of smartphone users and the number of people relying on mobile data networks. With larger numbers of users, the issue of service quality has become more important for network operators. Identifying faults in mobile networks that reduce the quality of service must be found within minutes so that problems can be addressed and networks returned to optimised performance. In this paper, a method of automated fault diagnosis is presented using decision trees, rules and Bayesian classifiers for visualization of network faults. Using data mining techniques the model classifies optimisation criteria based on the key performance indicators metrics to identify network faults supporting the most efficient optimisation decisions. The goal is to help wireless providers to localize the key performance indicator alarms and determine which Quality of Service factors should be addressed first and at which locations.