scholarly journals FRAMEWORK FOR MODELLING MOBILE NETWORK QUALITY OF EXPERIENCE THROUGH BIG DATA ANALYTICS APPROACH

2017 ◽  
Author(s):  
Ayisat Wuraola Yusuf-Asaju ◽  
Zulkhairi Md. Dahalin ◽  
Azman Ta’a
Keyword(s):  
User Experience ◽  
Data Analytics ◽  
Network Performance ◽  
Laboratory Experiments ◽  
Quality Of Experience ◽  
Big Data Analytics ◽  
Mobile Network ◽  
Mobile Internet ◽  
Mobile Network Operators

The increase in the usage of different mobile internet applications can cause deterioration in the mobile network performance. Such deterioration often declines the performance of the mobile network services that can influence the mobile Internet user’s experience, which can make the internet users switch between different mobile network operators to get good user experience. In this case, the success of mobile network operators primarily depends on the ability to ensure good quality of experience (QoE), which is a measure of users’ perceived quality of mobile Internet service. Traditionally, QoE is usually examined in laboratory experiments to enable a fixed contextual factor among the participants even though the results derived from these laboratory experiments presented an estimated mean opinion score representing perceived QoE. The use of user experience dataset involving time and location gathered from the mobile network traffic for modelling perceived QoE is still limited in the literature. The mobile Internet user experience dataset involving the time and location constituted in the mobile network can be used by the mobile network operators to make data-driven decisions to deal with disruptions observed in the network performance and provide an optimal solution based on the insights derived from the user experience data. Therefore, this paper proposed a framework for modelling mobile network QoE using the big data analytics approach. The proposed framework describes the process of estimating or predicting perceived QoE based on the datasets obtained or gathered from the mobile network to enable the mobile network operators effectively to manage the network performance and provide the users a satisfactory mobile Internet QoE.  

scholarly journals Framework towards the Process of Estimating or Predicting Perceived QoE Based on the Datasets Obtained the Mobile Network

2019 ◽  
Vol 9 (2) ◽  
pp. 3627-3631
Keyword(s):  
Artificial Intelligence ◽  
Consumer Satisfaction ◽  
Quality Of Experience ◽  
Research Study ◽  
Mobile Network ◽  
Mobile Internet ◽  
Software Defined Network ◽  
Network Functionality ◽  
Mobile Network Operators

Nowadays, the research study community visualizes a standard shift that is going to put the focus on Quality of Experience metrics, which relate directly to complete consumer satisfaction. Yet, determining QoE coming from QoS sizes is a daunting job that powerful Software Defined Network operators are currently able to tackle through artificial intelligence strategies. In this paper, our experts pay attention to a few essential QoE factors, and we to begin with proposing a Bayesian Network design to anticipate re-buffering proportion. This paper suggested a structure for modeling mobile network QoE, making use of the vast records analytics approach. The planned platform explains the method of estimating or forecasting perceived QoE based upon the datasets obtained or collected from the mobile network to enable the mobile network operators efficiently to deal with the network functionality as well as supply the individuals an adequate mobile Internet QoE.

scholarly journals Predicting the Quality of Service Experienced By the Users Based on the Measurement History of the Network

2019 ◽  
Vol 9 (2) ◽  
pp. 2286-2290
Keyword(s):  
Mobile Phone ◽  
Network Performance ◽  
Mobile Network ◽  
Mobile Internet ◽  
Mobile Phone Network ◽  
History Of ◽  
Set Up ◽  
Mobile Network Operators ◽  
Internet Company

Growing from one generation to the following, wireless networks have frequently been enhancing their efficiency in various methods and also for varied purposes. The increase in the usage of different mobile phone world broad web functions may result in degeneration in the mobile network performance. Such destruction often drops the efficiency of the mobile network solutions that can influence the mobile World broad web consumer's experience, which can create the world wide web individuals switch between different mobile network operators to get excellent customer experience. In this particular situation, the effectiveness of mobile phone network operators primarily relies on the capability to make a certain top quality of experience (QoE), which is a procedure of consumers' identified quality of mobile Internet company. The goal is actually to exploit the information made by and already accessible in the network to appropriately release, set up, and optimize network nodules.

scholarly journals Network performance assessment with Quality of experience benchmarks

2014 ◽  
Author(s):  
Decebal Constantin Mocanu ◽  
Giuliano Santandrea ◽  
Walter Cerroni ◽  
Franco Callegati ◽  
Antonio Liotta
Keyword(s):  
Performance Assessment ◽  
Network Performance ◽  
Quality Of Experience

scholarly journals SMOTE-BD: An Exact and Scalable Oversampling Method for Imbalanced Classification in Big Data

2018 ◽  
Vol 18 (03) ◽  
pp. e23 ◽  
Author(s):  
María José Basgall ◽  
Waldo Hasperué ◽  
Marcelo Naiouf ◽  
Alberto Fernández ◽  
Francisco Herrera
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Model Design ◽  
Minority Class ◽  
Imbalanced Classification ◽  
Design And Implementation ◽  
Learning Issues ◽  
Intelligent Model

The volume of data in today's applications has meant a change in the way Machine Learning issues are addressed. Indeed, the Big Data scenario involves scalability constraints that can only be achieved through intelligent model design and the use of distributed technologies. In this context, solutions based on the Spark platform have established themselves as a de facto standard. In this contribution, we focus on a very important framework within Big Data Analytics, namely classification with imbalanced datasets. The main characteristic of this problem is that one of the classes is underrepresented, and therefore it is usually more complex to find a model that identifies it correctly. For this reason, it is common to apply preprocessing techniques such as oversampling to balance the distribution of examples in classes. In this work we present SMOTE-BD, a fully scalable preprocessing approach for imbalanced classification in Big Data. It is based on one of the most widespread preprocessing solutions for imbalanced classification, namely the SMOTE algorithm, which creates new synthetic instances according to the neighborhood of each example of the minority class. Our novel development is made to be independent of the number of partitions or processes created to achieve a higher degree of efficiency. Experiments conducted on different standard and Big Data datasets show the quality of the proposed design and implementation.

Toward Mobile Web 2.0-Based Business Methods

2011 ◽  
pp. 1515-1535
Author(s):  
Katarzyna Wac ◽  
Richard Bults ◽  
Bert-Jan van Beijnum ◽  
Hong Chen
Keyword(s):  
Service Providers ◽  
Mobile Network ◽  
Mobile Service ◽  
Mobile Web ◽  
Service Deployment ◽  
Business Cases ◽  
Novel Method ◽  
Assurance Process ◽  
Mobile Network Operators

Mobile service providers (MoSPs) emerge, driven by the ubiquitous availability of mobile devices and wireless communication infrastructures. MoSPs’ customers satisfaction and consequently their revenues, largely depend on the quality of service (QoS) provided by wireless network providers (WNPs) available at a particular location-time to support a mobile service delivery. This chapter presents a novel method for the MoSP’s QoS-assurance business process. The method incorporates a location- and time-based QoS-predictions’ service, facilitating the WNP’s selection. The authors explore different business cases for the service deployment. Particularly, they introduce and analyze business viability of QoSIS.net, an enterprise that can provide the QoS-predictions service to MoSPs, Mobile Network Operators (as MoSPs), or directly to their customers (i.e. in B2B/B2C settings). QoSIS.net provides its service based on collaborative-sharing of QoS-information by its users. The authors argue that this service can improve the MoSP’s QoS-assurance process and consequently may increase its revenues, while creating revenues for QoSIS.net.

Using Big Data Analytics to Assist a Smart City to Prevent Cyber Security Threats

2021 ◽  
pp. 107-124
Author(s):  
Fenio Annansingh
Keyword(s):  
Big Data ◽  
Cyber Security ◽  
Smart City ◽  
Data Analytics ◽  
Life Quality ◽  
Big Data Analytics ◽  
Review Of The Literature ◽  
Smart Services ◽  
Prevention Model

The concept of a smart city as a means to enhance the life quality of citizens has been gaining increasing importance in recent years globally. A smart city consists of city infrastructure, which includes smart services, devices, and institutions. Every second, these components of the smart city infrastructure are generating data. The vast amount of data is called big data. This chapter explores the possibilities of using big data analytics to prevent cybersecurity threats in a smart city. It also analyzed how big data tools and concepts can solve cybersecurity challenges and detect and prevent attacks. Using interviews and an extensive review of the literature have developed the data analytics and cyber prevention model. The chapter concludes by indicating that big data analytics allow a smart city to identify and solve cybersecurity challenges quickly and efficiently.

A Closer Look at Direct Carrier Billing

2020 ◽  
Vol 10 (4) ◽  
pp. 18-40
Author(s):  
Lorena Herrera López
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Mobile Network ◽  
Marketing Channels ◽  
Digital Marketing ◽  
Mobile Service ◽  
Web Analytics ◽  
Network Operator ◽  
Over The Top

The impulse to digitalization by telecom operators requires the commercialization of over-the-top services (OTT) based on the fine understanding and prediction of customer behaviour through pattern recognition involving big data, resulting in an essential part of web analytics and digital marketing. The objective of this research is to analyse factors influencing the purchase and use of a mobile game commercialized by a mobile network operator (MNO), through different digital marketing channels and using direct carrier billing (DCB) as payment channel. The novelty contribution of this study is twofold. Firstly, it assesses determinants related to the purchase and use of a mobile service through the analysis of variables identified in the scientific literature's review. In addition, it also incorporates a set of variables based on data retrieved from big data analytics. Secondly, this research analyses the willingness of consumers to pay through DCB.

On Quality of Experience in Remote Visualization on Mobile Devices

2010 ◽  
Vol 2 (1) ◽  
pp. 1-20 ◽  
Author(s):  
Gianluca Paravati ◽  
Andrea Sanna ◽  
Fabrizio Lamberti ◽  
Luigi Ciminiera
Keyword(s):  
Mobile Devices ◽  
User Satisfaction ◽  
Network Performance ◽  
Quality Of Experience ◽  
Experimental Tests ◽  
User Preferences ◽  
Frame Rate ◽  
Remote Visualization ◽  
Server Side

Quality of Experience (QoE) is a relatively new concept which represents a way of measuring user satisfaction in the use of a certain kind of service. This work investigates issues related to the QoE in manipulating 3D scenes on mobile devices, by focusing on scenarios based on the remote visualization paradigm where a remote server is in charge of computing a flow of compressed images to be delivered to client devices. A novel approach able to dynamically set the encoding parameters at the server side is presented; the considered parameters are frame resolution, frame rate and image quality. The proposed solution is able to tune the above parameters according to both user preferences and network performance. Experimental tests are exploited to assess the relationship between the involved parameters and the QoE. Results obtained by considering low resource hardware (e.g. mobile devices) and unreliable connections (e.g. wireless networks) are presented. User feedback proves the effectiveness of the proposed approach.

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