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首页> 外文会议>International Conference on Advances in Big Data, Computing and Data Communication Systems >Modeling of an Efficient Low Cost, Tree Based Data Service Quality Management for Mobile Operators Using in-Memory Big Data Processing and Business Intelligence use Cases
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Modeling of an Efficient Low Cost, Tree Based Data Service Quality Management for Mobile Operators Using in-Memory Big Data Processing and Business Intelligence use Cases

机译：高效低成本的建模，基于树的移动运营商的数据服务质量管理，使用内存大数据处理和商业智能用例

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摘要

Network Operators are shifting their business interest towards Data services in a geometric progression manner, as Data services is becoming the major source of Telco revenue. The wide use of Data platforms; such as WhatsApp, Skype, Hangout and other Over the Top (OTT) voice applications over the traditional voice services is a clear indication that Network Operators need to adjust their business model and needs. And couple with the adoption of Smartphones usage which grows continuously year by year, this means more subscribers to manage, large amount of transactions generated, more network resources to be added and evidently more human technical expertise required to ensure good service quality. That has led to high investment on Robust Service Quality Management (SQM) and Customer Experience Management (CEM) to stay competitive in the market. The high investment is justified by the integration of Big Data Solutions, Machine Learning capabilities and good visualization of insight data. However, the Return on Investment (ROI) of the expensive systems are not as conspicuous as the provided functionalities and business rules. Therefore, in this paper an efficient model for low cost SQM system is presented, exploring the advantages of In-Memory Big Data processing and low cost business Intelligence tools to showcase how a good Service Quality Management can be implemented with no big investment.
机译：网络运营商正在以几何进展方式转向数据服务的业务兴趣，因为数据服务正成为电信收入的主要来源。使用数据平台的广泛使用;如WhatsApp，Skype，Houghout等顶部（OTT）语音应用程序上方的传统语音服务是一个明确的指示，网络运营商需要调整其业务模式和需求。并加上通过年度持续增长的智能手机使用，这意味着更多的订户来管理，大量的交易，更多的网络资源增加，明显更多的人力技术专业知识，以确保良好的服务质量。这导致了对强大的服务质量管理（SQM）和客户体验管理（CEM）的高投资，以保持市场竞争力。高投资通过集成大数据解决方案，机器学习能力和洞察数据的良好可视化，是合理的。但是，昂贵系统的投资回报（ROI）并不像提供的功能和业务规则那样显着。因此，本文提出了一种有效的低成本SQM系统模型，探讨了内存大数据处理和低成本商业智能工具的优点，以展示如何实现良好的服务质量管理，没有大投资。

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