Mobile network quality of experience using big data analytics approach

Yusuf-Asaju, Ayisat W.; Dahalin, Zulkhairi; Ta’a, Azman

doi:10.1109/icitech.2017.8079923

Cited by 7 publications

(2 citation statements)

References 29 publications

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“…The data preparation step involves (1) data preprocessing, (2) data exploratory analysis, (3) FS and feature extraction (FE) from the collected dataset (Yusuf et al, 2017). Data preprocessing involves the cleaning, integration, and transformation of the dataset before the data modeling step.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

A feature selection for video quality of experience modeling: A systematic literature review

Skaka-Čekić

Husić

2023

WIREs Data Min & Knowl

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Quality of Experience (QoE) multidimensional concept is the key for successful delivery of multimedia services. Higher user requirements for new experiences such as augmented reality, virtual reality, and future 6G services set higher requirements for QoE. A more complex QoE space requires the use of data mining methods in order to process the data for better QoE prediction. The increased dimensionality of the QoE space becomes a limiting factor for achieving the desired QoE prediction accuracy. Existing studies considering the QoE multidimensional concept with approaches that overcome the challenge of increased QoE space dimensionality are of great importance for future research. Accordingly, this article aims to review the applications of Feature Selection (FS) methods in video QoE modeling. It provides a comprehensive overview of the existing studies with the categorization and review of applied FS methods with reference to the data collection and data modeling steps. The analysis included 71 studies which provides overview of the FS methods applications in video QoE modeling depending on the input Influence Factor (IF) dimension sizes, type of IFs, QoE prediction methods used and QoE evaluation type. Our review revealed the advantages of using FS methods in video QoE modeling, frequency of application of FS methods with potential of applying more FS methods in a series or a parallel, gives an overview of the achieved dimensionality reduction degree for different methods, and provides insights in opportunities for researchers for applying FS methods on complex multidimensional QoE space. This article is categorized under: Technologies > Data Preprocessing Algorithmic Development > Multimedia

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Section: Theoretical Backgroundmentioning

confidence: 99%

A feature selection for video quality of experience modeling: A systematic literature review

Skaka-Čekić

Husić

2023

WIREs Data Min & Knowl

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“…Also, in the article present a methodological framework for measuring the information society. In the study (Yusuf-Asaju et al, 2017) the authors propose to apply the process of estimating perceived quality of experience to assist mobile network operators to effectively manage network performance and aid satisfactory provision of mobile internet services. In the study (Simaković et al, 2021) the authors propose a big data solution that can collect and process huge amounts of data to extract valuable information and help mobile operators to bring their networks to enhanced quality level and offer full IoT solutions to their customers.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting the Behavior of Target Segments to Activate Advertising Tools: Case of Mobile Operator Vodafone Ukraine

et al. 2022

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Today, the use of machine learning technology in combination with the use of big data are topics that are actively discussed in business around the world. This topic has long gone beyond the information sphere, as it now applies to almost every sphere of life: economic, telecommunications, education, medicine, administration, and especially defense. Predicting customer behavior based on scoring models is in its infancy in Ukrainian companies, the main ones being the introduction of artificial intelligence technologies and machine learning, which will be the leading catalyst that will facilitate decision-making in business in the nearest future. The aim of the study is to develop a scoring model that predicts the behavior of target segments, namely, updating their activity to activate advertising tools. To achieve the goal of the work a set of research methods was used: dialectical – to reveal the theoretical foundations of models and types of forecasting models; analytical – in the study of the functioning of the environment SAS, Anaconda; optimization methods – to choose the best model and generate features. Scientific novelty and theoretical significance lie in the development of a scoring model for predicting the activity of subscribers of the telecommunications company “VF Ukraine”, on the basis of which marketing campaigns are conducted. With the help of the built-in scoring model, the company “VF Ukraine” can target its campaigns to retain subscribers. The marketing directorate of the enterprise can choose the TOP-20 or TOP-30 of the most prone subscribers to non-resumption of activity, i.e., tend to switch to other mobile operators, and hold promotions for them – providing additional gifts and bonuses, money to mobile account.

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