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Geo and Graph Analytics for Dynamic Cellular Transactions Insights, Improving Quality and Business Decisions: 'Quality X Map'

机译:动态蜂窝交易洞察的地理和图分析,提高质量和业务决策:“质量X地图”

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Everyday, millions of users on their phone generate huge amount of traffic; intelligent industrial machines and IoT devices also generate monumental traffic, both affecting Network performance due to the shared medium used. With the large amount of human and machine generated data, Communications Service Providers (CSPs) face the challenge of finding values and addressing Quality of Service (QoS) and Quality of Experience (QoE) in a dynamic way. The advance of Data and Predictive Analytics methods makes it not easier but possible to dive deep into Network transactions to build intelligent insights, helping with business decisions. In this paper, we use Geo-Analytics and Intelligent Localization algorithms, combined with Graph Processing to provide a dynamic insight of Network data, to sustain business decisions and improve users and devices' QoE.
机译:每天,他们手机上的数百万用户产生大量的流量;智能工业机器和物联网设备也会产生纪念性流量,这两种情况都会影响由于所使用的共享介质而影响网络性能。随着大量人员和机器生成的数据,通信服务提供商(CSP)面临着一种充满活力的方式寻找价值和解决服务质量(QoS)和体验质量(QoE)的挑战。数据和预测分析方法的进展使得潜入网络交易深入了解,以建立智能洞察力,帮助业务决策。在本文中,我们使用地理分析和智能本地化算法,结合图形处理来提供网络数据的动态识别,维持业务决策和改善用户和设备的QoE。

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