Geo and Graph Analytics for Dynamic Cellular Transactions Insights, Improving Quality of Service and Business Decisions: “Quality X Map”

EasyChair Preprint 687

Abstract

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 communication 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 Experience (QoE). 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’ QoS and QoE.

Keyphrases: Cellular Transactions, Communication Service Providers (CSPs), Geo analytics, Internet of Things (IoT)., Quality of Experience (QoE), Quality of Service (QoS), business decisions, graph analytics, machine learning

@booklet{EasyChair:687,
  author    = {Dahj Muwawa Jean Nestor and Kingsley A. Ogudo},
  title     = {Geo and Graph Analytics for Dynamic Cellular Transactions Insights, Improving Quality of Service and Business Decisions: “Quality X Map”},
  howpublished = {EasyChair Preprint 687},
  year      = {EasyChair, 2018}}