Implementation of Quality of Experience Prediction Framework through Mobile Network Data

Generally, a reliable method of analyzing the quality of experience is through the subjective method, which is time consuming, lacks usability, lacks repeatability in real-time and near real-time. Another method is the objective measurement that aims at predicting the subjective measurement based on the estimated mean opinion score. Therefore, this study adopted the objective measurement by implementing a quality of experience framework, which employed predictive analytics techniques to analyze the mobile internet user experience dataset gathered through the mobile network. The predictive analytics employed the use of multiple regression, neural network, decision trees, random forest, and decision forest to predict the mobile internet perceived quality of experience. Result from the study shows that decision forests performs better than other algorithms used for the predictive analytics. In addition, the result indicates that the predictive analytics can be used to enhance the allocation of network resources based on location and time constituted in the dataset

A Proposed Analytical Customer Satisfaction Prediction Model for Mobile Internet Networks

Subjective method (such as survey, interview, etc.) has been the most common and reliable method used in analyzing customer satisfaction. However, the subjective method is expensive, time consuming, lacks repeatability in real-time and may not capture the technical aspect of the telecoms network service performance in telecommunication industry. As a result, perceived quality of experience (QoE) has been traditionally used to evaluate the satisfaction of telecommunication services from both Internet service providers and customer’s perspective. However, the result of perceived QoE in relation to mean opinion score found not suitable enough to quantify customer satisfaction, and it eliminates the diversity of customer assessment while quantifying satisfaction. Therefore, this paper proposed an analytical customer satisfaction prediction model based on perceived QoE, perceived QoE influence factors, perceived QoE measurements and perceived QoE estimations to overcome the limitations of the subjective measurement. The paper presents how the mean opinion score can be used to quantify customer satisfaction by ensuring the diversity of customer’s assessment is not eliminated

Business Intelligence Model for Monitoring Blended Learning Usage

Online learning for students and for lecturers are one of the fastest learning trends in Institute of Higher Education (IHE) in Malaysia. The Ambient Insight Comprehensive Report (2015) estimated that Malaysia is 3rd top self-paced e-Learning growth in Asia, since 2010. In April 2011, Malaysia launched the Malaysia Education Online (MEdO) national online learning portal. The goal of new National e-Learning Policy is to have 30% of all higher education courses delivered online by 2015. Moreover, 90 percent of higher education already has an e-Learning policy, and most 70 percent make use of online learning or blended Learning compulsory among their lecturers and students. The internet-based learning application, interactive blended learning approaches, and which blended learning is effective for supporting lecturers teaching competencies were explored. This paper demonstrates the integration several data sources of online learning systems in UUM (known as UUMLearning) to systematically monitor the usage of blended learning tools. The Blended Learning is defined as using several items in an online learning system (UUMLearning), which is complying with the requirements setting by the IHE. The information required to be captured and analyzed is determined prior to perform the data pre-processing and produce results of monitoring blended learning usage. Therefore, the Business Intelligence and Data Warehouse approach have been used to capture, process, integrate and analyze data on blended learning utilization in order to understand the usage of blended e-learning technology among the lecturers in Universiti Utara Malaysia (UUM)

BUSINESS INTELLIGENCE MODELLING FOR GRADUATE ENTREPRENEUR PROGRAMME

Business Intelligence (BI), which is the process of collecting, analysing, and transforming data using Data Warehouse (DW) is seen as one of the growing approaches to provide meaningful information for the Malaysian Ministry of Higher Education (MOHE). MOHE is responsible for managing various activities to encourage graduate entrepreneurs to venture into businesses and ensure that the country has many successful entrepreneurs. Therefore, systematic and accurate information needs to be available for planning, implementing, and monitoring entrepreneurs’ performances. This paper proposes the modelling and designing of the graduate entrepreneur profi le system – Intelligent Profi le Analysis Graduate Entrepreneur (iPAGE) using the BI approach. Two main methodologies were used, namely the Requirements Centric Operational Data Store (ReCODS) and the Rapid Application Development (RAD) to model and design this system. The iPAGE was validated and evaluated by users, entrepreneurs’ personnel and DW experts. Indeed, the approach will be used to benchmark the development of an entrepreneurial information system in the future.

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

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.

GOAL-ONTOLOGY ETL PROCESSES SPECIFICATION

The design-related problems for extract, transform, load (ETL) processes are far away from being resolved due to the ambiguity of user requirements and the complexity of operations. Current approaches are based on existing software requirement methods that have limitations on reconciliation of the requirement semantics toward generating the ETL processes specification. The solution is to apply the RAMEPs (Requirement Analysis Method for ETL Processes) that was developed to facilitate the design of the ETL processes in the perspectives of organization, decision-maker, and developer. The results are the ETL processes specification, which was validated on the correctness of the goal-ontology model and evaluated in the case study of Gas Malaysia Data Warehouse (DW) system. The case study illustrated how the goal-ontology approach was successfully implemented in designing and generating the ETL processes specification.

A Comparison of GIS Packages for Geospatial Data Pre-processing

This paper aims to assess the choice of geographic information systems (GIS) used in pre-process geospatial data sets. The study conducted a comparative review of some of the commonly used GIS packages with the aim of proposing the most reliable in terms of consistency, functionality, user-friendliness and cost-effectiveness, which are the determinants in adopting any GIS packages. By this systematic assessment, both the current and potential users will be able to take full advantage of the most efficient GIS package to perform various analytical pre-processing tasks. The outcome of the assessment could be adopted as a guide for selecting an appropriate and reliable open source GIS platform for a timely and efficient pre-preprocessing geospatial data for environmental analysis

Penilaian Keperluan Pelajar Terhadap Pembangunan Sistem E-Penjejakan Secara Masa Nyata Melalui Aplikasi Telefon Pintar

Kelewatan ke kuliah adalah satu fenomena yang sering berlaku di dalam kampus universiti disebabkan pelbagai faktor. Kelewatan ini antaranya adalah disebabkan oleh ketidakcekapan penyedia perkhidmatan bas yang terjebak di dalam kesesakan, ketidakcukupan bilangan bas, masa menunggu yang lama dan sebagainya. Ianya dikatakan antara punca mengapa pelajar kurang konsentrasi ketika berada dalam sesi pengkuliahan. Lantaran itu, kajian ini bertujuan untuk mendapatkan maklumbalas kepuasan hati pelajar terhadap perkhidmatan bas di dalam kampus Universiti Utara Malaysia (UUM) di Sintok, Kedah. Keputusan yang diperolehi mendapati pelajar menghadapi masalah kritikal terhadap pengurusan masa dari aspek tempoh beratur, menunggu bas dan ketika transit. Pada masa yang sama kajian ini telah mendapatkan maklumbalas untuk membangunkan sistem prototaip E-Penjejakan bagi membantu pelajar mengatur masa melalui telefon pintar. Sistem ini berpotensi untuk menjangka masa ketibaan, kelewatan dan kapasiti penumpang sesebuah bas ketika sedang beroperasi. Sistem ini juga akan membantu pihak universiti untuk merealisasikan program ‘green campus’ dan ‘green transportation’ pada masa yang sama. Keputusan dari analisis data menunjukkan mereka bersetuju bahawa sistem prototaip ini berpotensi untuk; mengurangkan masa menunggu, membantu mereka mengetahui masa ketibaan, masa bertolak, dan tempoh perjalanan bas dengan lebih tepat dan pantas