A Scalable Heterogeneous Big Data Framework for e-Learning Systems

Otoo-Arthur, David; Zyl, Terence L. van

doi:10.1109/icabcd49160.2020.9183863

Cited by 14 publications

(8 citation statements)

References 54 publications

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“…The fundamental concept of digitalisation in HRD is the incorporation of digital technologies into many aspects of talent management, training, skill enhancement, and organisational learning (Dorasamy, 2021;Varshney, 2020). The aforementioned transformation has been instigated by the widespread use of various technological advancements, including but not limited to e-learning platforms, data analytics, artificial intelligence (AI), and virtual reality (VR) (Otoo-Arthur and van Zyl, 2020;Sengupta et al, 2021). The utilisation of these technologies has not only brought about a significant transformation in the manner in which businesses address HRD but has also played a crucial role in augmenting the competitive advantage of enterprises within the current business environment (Akdere et al, 2023;Malini and Srinivas, 2020;Otoo-Arthur and van Zyl, 2020).…”

Section: A Comprehensive Review Of Digitalisation In Hrd and Its Impa...mentioning

confidence: 99%

Digital wind of changes: navigating competitiveness in the maritime sector through the transformation in human resource development

Autsadee,

Jeevan,

Mohd Salleh

et al. 2024

MABR

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PurposeThe maritime industry, a linchpin of global trade, has embarked on a transformative journey catalysed by the relentless advance of digitalisation. There is a discernible gap in the literature concerning the specific consequences of digitalisation within the maritime sector. This research aims to examine the current body of literature on the influence of digitalisation in human resource development (HRD) on the competitive advantage of organisations and its potential within the maritime industry.Design/methodology/approachThis research paper conducts a comprehensive bibliometric analysis.FindingsThe findings of this research explore the literature landscape encompassing digitalisation in HRD, its influence on HR operations, learning and development, performance management, employee experience, and strategic alignment within maritime organisations.Originality/valueThis research provides valuable recommendations for maritime organisations and HRD practitioners seeking to leverage digitalisation to gain a competitive edge. Thus, the maritime industry can adopt digital HRD practices to streamline operations, improve performance, and align HR strategies with broader organisational goals.

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Section: A Comprehensive Review Of Digitalisation In Hrd and Its Impa...mentioning

confidence: 99%

Digital wind of changes: navigating competitiveness in the maritime sector through the transformation in human resource development

Autsadee,

Jeevan,

Mohd Salleh

et al. 2024

MABR

View full text Add to dashboard Cite

show abstract

“…Big data technologies can play signi cant role in improving data processing, data storage, data analytics and visualization (41). Big data signi cant impact the transformation of learning process and adoption of relevant innovative technologies (13).…”

Section: Big Data Lms and Big Data Analyticsmentioning

confidence: 99%

Designing and Evaluating a Big Data Analytics Approach for predicting students’ success factors

Fahd

Miah

2022

Preprint

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Reducing student attrition in tertiary education plays a significant role in the core mission and financial well-being of an educational institution. The availability of big data source from Learning Management System (LMS) can be analysed to help with the attrition issues. This study aims to use an integrated Design science research (DSR) methodology to develop and evaluate a Big Data Analytical Solution (BDAS) embedded in Educational Decision Support System as an educational artefact. The BDAS as DSR artefact harnesses the outcome of the application of Artificial Intelligence(AI) based approaches on the dataset collected from student interaction with LMS to train a predictive model to predict potential students at risk. Identifying students at risk helps to take timely intervention in the learning process to improve student academic progress to increase the retention rate. To evaluate the performance of the predictive model, we compare the accuracy of the collection of representational Artificial Intelligence algorithms in the literature. The BDAS aims not to replace any existing practice but to support educators to implement a variety of pedagogical practices to improve students’ academic performance.

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“…Hadoop is made up of two major components: HDFS which is Hadoop Distributed File System and the MapReduce programming framework. As a result, the storage system is not physically isolated from the processing system (Al- and Otoo-Arthur and Zyl, 2020). "Google's MapReduce and Google File System" were created in 2004 in response to the ever-increasing volume of data on the web.…”

Section: Hadoop Frameworkmentioning

confidence: 99%

Big Data Processing Frameworks for Handling Huge Data Efficiencies and Challenges: A Survey

Al-Barznji¹

2022

IJDSBDA

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The increasing expansion of digital data collected from many sources renders traditional storage, processing, and analysis methods obsolete. For these restrictions, new technologies for processing and storing very massive datasets have been developed. Big data processing is required to extract relevant information from it. Transforming data into information and knowledge is what processing implies. Big data processing is the process of dealing with massive amounts of data and changing it from its raw form into useable information in a more understandable manner. As a result, numerous big data processing execution frameworks have emerged, but determining and selecting the appropriate framework for processing your big data applications is a significant challenge. Therefore, this paper investigates the possible influence of big data challenges and discusses in depth the most well-known approaches to big data processing, which are divided into five classes: batch processing, streaming processing, realtime processing, interactive processing, and hybrid processing, as well as the variety of the most popular frameworks associated with them such as Apache Hadood, Dryad, Samza, IBM Infosphere, Storm, Amazon Kinesis, Drill, Impala, Flink, and Spark. Furthermore, this study presents a comparison among the several features of the frameworks by highlighting their drawbacks and strengths. Thus, it can be used as a guideline for picking the best application framework in IT analytics and will help busi-ness users make faster decisions.

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A Scalable Heterogeneous Big Data Framework for e-Learning Systems

Cited by 14 publications

References 54 publications

Digital wind of changes: navigating competitiveness in the maritime sector through the transformation in human resource development

Digital wind of changes: navigating competitiveness in the maritime sector through the transformation in human resource development

Designing and Evaluating a Big Data Analytics Approach for predicting students’ success factors

Big Data Processing Frameworks for Handling Huge Data Efficiencies and Challenges: A Survey

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