scholarly journals A proposed architecture of big educational data using hadoop at the University of Kufa

2019 ◽  
Vol 9 (6) ◽  
pp. 4970
Author(s):  
Ahmed Yaseen Mjhool ◽  
Ahmed Hazim Alhilali ◽  
Salam Al-augby
Keyword(s):  
Big Data ◽  
Data Science ◽  
Big Data Analytics ◽  
Data Repository ◽  
Web Based ◽  
E Learning ◽  
Data Policy ◽  
Windows Applications ◽  
The University ◽  
The Impact

<span>Nowadays, educational data have been increased rapidly because of the online services provided for both students and staff. University of Kufa (UoK) generates a massive amount of data annually due to the use of e-learning web-based systems, network servers, Windows applications, and Students Information System (SIS).  This data is wasted as traditional management software are not capable to analysis it. As a result, the Big Educational Data concept rises to help education sectors by providing new e-learning methods, allowing to meet individual demands and reach the learners' goals, and supporting the students and teacher’s interaction. This paper focuses on designing Big Data analysis architecture, based on the Hadoop in the UoK and the same case for other Iraqi universities. The impact of this work, help the students learn, emphasizing the need of academic researchers and data science specialist for learning and practicing Big Data analytics and support the analysis of the e-learning management system and set the first step toward developing data repository and data policy in UoK.</span>

An experimental investigation of the impact of using big data analytics on customers’ performance measurement

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Marwa Rabe Mohamed Elkmash ◽  
Magdy Gamal Abdel-Kader ◽  
Bassant Badr El Din
Keyword(s):  
Big Data ◽  
Performance Measurement ◽  
Data Analytics ◽  
Positive Impact ◽  
Big Data Analytics ◽  
Unstructured Data ◽  
Future Research ◽  
Web Based ◽  
Content Type ◽  
The Impact

Purpose This study aims to investigate and explore the impact of big data analytics (BDA) as a mechanism that could develop the ability to measure customers’ performance. To accomplish the research aim, the theoretical discussion was developed through the combination of the diffusion of innovation theory with the technology acceptance model (TAM) that is less developed for the research field of this study. Design/methodology/approach Empirical data was obtained using Web-based quasi-experiments with 104 Egyptian accounting professionals. Further, the Wilcoxon signed-rank test and the chi-square goodness-of-fit test were used to analyze data. Findings The empirical results indicate that measuring customers’ performance based on BDA increase the organizations’ ability to analyze the customers’ unstructured data, decrease the cost of customers’ unstructured data analysis, increase the ability to handle the customers’ problems quickly, minimize the time spent to analyze the customers’ data and obtaining the customers’ performance reports and control managers’ bias when they measure customer satisfaction. The study findings supported the accounting professionals’ acceptance of BDA through the TAM elements: the intention to use (R), perceived usefulness (U) and the perceived ease of use (E). Research limitations/implications This study has several limitations that could be addressed in future research. First, this study focuses on customers’ performance measurement (CPM) only and ignores other performance measurements such as employees’ performance measurement and financial performance measurement. Future research can examine these areas. Second, this study conducts a Web-based experiment with Master of Business Administration students as a study’s participants, researchers could conduct a laboratory experiment and report if there are differences. Third, owing to the novelty of the topic, there was a lack of theoretical evidence in developing the study’s hypotheses. Practical implications This study succeeds to provide the much-needed empirical evidence for BDA positive impact in improving CPM efficiency through the proposed framework (i.e. CPM and BDA framework). Furthermore, this study contributes to the improvement of the performance measurement process, thus, the decision-making process with meaningful and proper insights through the capability of collecting and analyzing the customers’ unstructured data. On a practical level, the company could eventually use this study’s results and the new insights to make better decisions and develop its policies. Originality/value This study holds significance as it provides the much-needed empirical evidence for BDA positive impact in improving CPM efficiency. The study findings will contribute to the enhancement of the performance measurement process through the ability of gathering and analyzing the customers’ unstructured data.

What is the Business with AI? Preparing Future Decision Makers and Leaders

2020 ◽  
Author(s):  
Helena S. Wisniewski
Keyword(s):  
Data Science ◽  
Decision Makers ◽  
Management Skills ◽  
Business Courses ◽  
College Of Business ◽  
College Of Engineering ◽  
Investment Firm ◽  
Business Curricula ◽  
The University ◽  
The Impact

With companies now recognizing how artificial intelligence (AI), digitalization, the internet of things (IoT), and data science affect value creation and the maintenance of a competitive advantage, their demand for talented individuals with both management skills and a strong understanding of technology will grow dramatically. There is a need to prepare and train our current and future decision makers and leaders to have an understanding of AI and data science, the significant impact these technologies are having on business, how to develop AI strategies, and the impact all of this will have on their employees’ roles. This paper discusses how business schools can fulfill this need by incorporating AI into their business curricula, not only as stand-alone courses but also integrated into traditional business sequences, and establishing interdisciplinary efforts and collaborative industry partnerships. This article describes how the College of Business and Public Policy (CBPP) at the University of Alaska Anchorage is implementing multiple approaches to meet these needs and prepare future leaders and decision makers. These approaches include a detailed description of CBPP’s first AI course and related student successes, the integration of AI into additional business courses such as entrepreneurship and GSCM, and the creation of an AI and Data Science Lab in partnership with the College of Engineering and an investment firm.

Big Data and Sustainable Consumption: A Review and Research Agenda

2021 ◽  
pp. 097226292110225
Author(s):  
Shobhana Chandra ◽  
Sanjeev Verma
Keyword(s):  
Big Data ◽  
Consumer Behaviour ◽  
Business Models ◽  
Sustainable Consumption ◽  
Big Data Analytics ◽  
Consumption Behaviour ◽  
Consumer Experiences ◽  
Systematic Understanding ◽  
The Impact

Big data (BD) is making advances in promoting sustainable consumption behaviour and has attracted the attention of researchers worldwide. Despite the increased focus, the findings of studies on this topic are fragmented, and future researchers need a systematic understanding of the existing literature for identification of the research scope. This study offers a systematic review of the role of BD in promoting sustainable-consumption behaviour with the help of a bibliometric analysis, followed by a thematic analysis. The findings suggest that businesses deploy BD to create sustainable consumer experiences, predict consumer buying patterns, design and alter business models and create nudges for sustainable consumption, while consumers are forcing businesses to develop green operations and supply chains to reduce the latter’s carbon footprint. The major research gaps for future researchers are in the following areas: the impact of big data analytics (BDA) on consumerism, the role of BD in the formation of sustainable habits and consumer knowledge creation for sustainable consumption and prediction of green consumer behaviour.

scholarly journals Big Data Analytics for Search Engine Optimization

2020 ◽  
Vol 4 (2) ◽  
pp. 5 ◽  
Author(s):  
Ioannis C. Drivas ◽  
Damianos P. Sakas ◽  
Georgios A. Giannakopoulos ◽  
Daphne Kyriaki-Manessi
Keyword(s):  
Big Data ◽  
Cultural Heritage ◽  
Search Engine ◽  
Data Analytics ◽  
User Behavior ◽  
Big Data Analytics ◽  
Data Types ◽  
Search Engine Optimization ◽  
The Impact ◽  
The Web

In the Big Data era, search engine optimization deals with the encapsulation of datasets that are related to website performance in terms of architecture, content curation, and user behavior, with the purpose to convert them into actionable insights and improve visibility and findability on the Web. In this respect, big data analytics expands the opportunities for developing new methodological frameworks that are composed of valid, reliable, and consistent analytics that are practically useful to develop well-informed strategies for organic traffic optimization. In this paper, a novel methodology is implemented in order to increase organic search engine visits based on the impact of multiple SEO factors. In order to achieve this purpose, the authors examined 171 cultural heritage websites and their retrieved data analytics about their performance and user experience inside them. Massive amounts of Web-based collections are included and presented by cultural heritage organizations through their websites. Subsequently, users interact with these collections, producing behavioral analytics in a variety of different data types that come from multiple devices, with high velocity, in large volumes. Nevertheless, prior research efforts indicate that these massive cultural collections are difficult to browse while expressing low visibility and findability in the semantic Web era. Against this backdrop, this paper proposes the computational development of a search engine optimization (SEO) strategy that utilizes the generated big cultural data analytics and improves the visibility of cultural heritage websites. One step further, the statistical results of the study are integrated into a predictive model that is composed of two stages. First, a fuzzy cognitive mapping process is generated as an aggregated macro-level descriptive model. Secondly, a micro-level data-driven agent-based model follows up. The purpose of the model is to predict the most effective combinations of factors that achieve enhanced visibility and organic traffic on cultural heritage organizations’ websites. To this end, the study contributes to the knowledge expansion of researchers and practitioners in the big cultural analytics sector with the purpose to implement potential strategies for greater visibility and findability of cultural collections on the Web.

Analysis of Big Data Analytics in Healthcare Sector: Applications and Tools

2020 ◽  
Vol 17 (12) ◽  
pp. 5605-5612
Author(s):  
A. Kaliappan ◽  
D. Chitra
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Health Sector ◽  
Big Data Analytics ◽  
High Volume ◽  
Healthcare Sector ◽  
Genome Database ◽  
Tremendous Amount ◽  
The Impact ◽  
Different Sources

In today’s world, an immense measure of information in the form of unstructured, semi-structured and unstructured is generated by different sources all over the world in a tremendous amount. Big data is the termed coined to address these enormous amounts of data. One of the major challenges in the health sector is handling a high-volume variety of data generated from diverse sources and utilizing it for the wellbeing of human. Big data analytics is one of technique designed to operate with monstrous measures of information. The impact of big data in healthcare field and utilization of Hadoop system tools for supervising the big data are deliberated in this paper. The big data analytics role and its theoretical and conceptual architecture include the gathering of diverse information’s such as electronic health records, genome database and clinical decisions support systems, text representation in health care industry is investigated in this paper.

scholarly journals Big Data Analytics and E Learning in Higher Education

2016 ◽  
Vol 5 (1) ◽  
pp. 81-85
Author(s):  
Tulasi B ◽  
Suchithra R
Keyword(s):  
Higher Education ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
E Learning

scholarly journals IOT And Big Data Analytics In E-Learning: A Technological Perspective and Review

2018 ◽  
Vol 7 (1.8) ◽  
pp. 164 ◽  
Author(s):  
S Kusuma ◽  
D Kasi Viswanath
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Digital Culture ◽  
Educational Institutions ◽  
Learning Approaches ◽  
E Learning ◽  
Recent Trends ◽  
Conducting Research ◽  
The Internet Of Things

The internet of things & Big data analytics in eLearning brings tremendous challenges & opportunities to educational institutions & students. In recent trends, the growth of Pervasive computing, Social media, evolving IoT capabilities, technologies such as cloud computing, and big data and analytics are improving the core values of teaching and conducting research but also instilling a new digital culture and developing an IoT-centric society. The primary purpose of this paper is to provide an impact of IoT & Big data analytics in the area of E-learning and study on different E-learning approaches. 

The impact of big data analytics on the dynamics of social change

2018 ◽  
Vol 130 ◽  
pp. 56 ◽  
Author(s):  
Fang-Mei Tseng ◽  
Robert Harmon
Keyword(s):  
Big Data ◽  
Social Change ◽  
Data Analytics ◽  
Big Data Analytics ◽  
The Impact

Data Science and Big Data Analytics

2020 ◽  
pp. 129-150
Author(s):  
Ananta Charan Ojha ◽  
Subhendu Kumar Pani
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Data Science ◽  
Big Data Analytics

scholarly journals Multi Objective Optimization Based Feature Selection Algorithms for Big Data Analytics: A Review

2021 ◽  
pp. 1-4
Author(s):  
Aakriti Shukla ◽  
◽  
Dr Damodar Prasad Tiwari ◽  
Keyword(s):  
Feature Selection ◽  
Big Data ◽  
Data Science ◽  
Big Data Analytics ◽  
Data Sets ◽  
Big Data Applications ◽  
High Workload ◽  
Long Time ◽  
Analytical Result ◽  
Result Analysis

Dimension reduction or feature selection is thought to be the backbone of big data applications in order to improve performance. Many scholars have shifted their attention in recent years to data science and analysis for real-time applications using big data integration. It takes a long time for humans to interact with big data. As a result, while handling high workload in a distributed system, it is necessary to make feature selection elastic and scalable. In this study, a survey of alternative optimizing techniques for feature selection are presented, as well as an analytical result analysis of their limits. This study contributes to the development of a method for improving the efficiency of feature selection in big complicated data sets.

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