TY - GEN
T1 - 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
AU - Ogudo, Kingsley A.
AU - Nestor, Dahj Muwawa Jean
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/9/13
Y1 - 2018/9/13
N2 - 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.
AB - 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.
KW - Business Intelligence
KW - Data Traffic and ROI
KW - In-Memory Big Data
KW - Over The Top Application (OTT)
KW - Service Quality Index
KW - Service Quality Management
UR - http://www.scopus.com/inward/record.url?scp=85054705718&partnerID=8YFLogxK
U2 - 10.1109/ICABCD.2018.8465410
DO - 10.1109/ICABCD.2018.8465410
M3 - Conference contribution
AN - SCOPUS:85054705718
SN - 9781538630600
T3 - 2018 International Conference on Advances in Big Data, Computing and Data Communication Systems, icABCD 2018
BT - 2018 International Conference on Advances in Big Data, Computing and Data Communication Systems, icABCD 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 International Conference on Advances in Big Data, Computing and Data Communication Systems, icABCD 2018
Y2 - 6 August 2018 through 7 August 2018
ER -