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Alarm Correlation in Mobile Telecommunications Networks based on k-means Cluster Analysis Method

机译:基于K-Means群集分析方法的移动电信网络中的报警相关

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摘要

Event correlation and root cause analysis play a fundamental role in the process of troubleshooting all tech- nical faults and malfunctions. An in-depth, complicated mul- tiprotocol analysis can be greatly supported or even replaced by a troubleshooting methodology based on data analysis ap- proaches. The mobile telecommunications domain has been experiencing rapid development recently. Introduction of new technologies and services, as well as multivendor environment distributed across the same geographical area create a lot of challenges in network operation routines. Maintenance tasks have been recently becoming more and more complicated, time consuming and require big data analyses to be per- formed. Most network maintenance activities are completed manually by experts using raw network management informa- tion available in the network management system via multiple applications and direct database queries. With these circum- stances considered, identification of network failures is a very difficult, if not an impossible task. This explains why effec- tive yet simple tools and methods providing network opera- tors with carefully selected, essential information are needed. Hence, in this paper efficient approximated alarm correlation algorithm based on the k-means cluster analysis method is proposed.
机译:事件相关性和根本原因分析在排除所有技术故障和故障的过程中起着基本作用。可以通过基于数据分析APACHES的故障排除方法极大地支持或甚至可以大大支持或甚至替换深度复杂的MUL-TIPROTOCOL分析。移动电信领域最近一直经历迅速发展。介绍新技术和服务,以及分布在同一地理区域的多士商环境在网络运营惯例中创造了很多挑战。维护任务最近越来越复杂,耗时,并且需要大数据分析。大多数网络维护活动由网络管理系统中可用的原始网络管理信息通过多个应用程序和直接数据库查询手动完成。通过考虑这些围攻,网络故障的识别是非常困难的,如果不是不可能的任务。这解释了为什么需要仔细选择提供网络操作的有效且简单的工具和方法。因此,在本文中,提出了基于K-Means群集分析方法的高效近似报警相关算法。

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