scholarly journals A big data framework for E-Government in Industry 4.0

2021 ◽  
Vol 11 (1) ◽  
pp. 461-479
Author(s):  
Cu Kim Long ◽  
Rashmi Agrawal ◽  
Ha Quoc Trung ◽  
Hai Van Pham
Keyword(s):  
Big Data ◽  
Industry 4.0 ◽  
Intelligent Systems ◽  
Big Data Analytics ◽  
Next Generation ◽  
Global Trends ◽  
Data Framework ◽  
The Us ◽  
Efficiency And Effectiveness ◽  
Potential Benefits

Abstract The next generation of E-Government and healthcare has the potential to increase the more intelligent governance with improvements in transparency, accountability, efficiency, and effectiveness. It enables organizations to use the benefits of information via big data analysis to settle the difficulties effectively. Big Data has emerged which plays a significant role in many sectors around the world. Global trends in taking advantage of the benefits from big data are considered with an overview of the US, European Union, and several developing countries. To deeply understand the utilization of big data in several domains, this study has presented a brief survey of key concepts (such as IoT-enabled data, blockchain-enabled data, and intelligent systems data) to deeply understand the utilization of big data in several domains. Our analysis sets out also the similarities and differences in these concepts. We have also surveyed state-of-the-art technologies including cloud computing, multi-cloud, webservice, and microservice which are used to exploit potential benefits of big data analytics. Furthermore, some typical big data frameworks are surveyed and a big data framework for E-Government is also proposed. Open research questions and challenges are highlighted (for researchers and developers) following our review. Our goal in presenting the novel concepts presented in this article is to promote creative ideas in the research endeavor to perform efficaciously next-generation E-Government in the context of Industry 4.0.

scholarly journals The importance of big data technology

2018 ◽  
Vol 7 (4.5) ◽  
pp. 485
Author(s):  
Samson Fadiya ◽  
Arif Sari
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Management Systems ◽  
Apache Hadoop ◽  
Industry Standard ◽  
Data Framework ◽  
Efficiency And Effectiveness ◽  
Relational Database Management ◽  
Big Data Technology

The adoption of Web 2.0 technologies, Internet of Things, etc. by individuals and organization has led to an explosion of data. As it stands, existing Relational Database Management Systems (RDBMSs) are incapable of handling this deluge of data. The term Big Data was coined to represent these vast, fast and complex datasets that regular RDBMSs could not handle. Special tools or frameworks were developed to deal with processing, managing and storing this big data. These tools are capable of functioning in distributed industry- standard environments thereby maintaining efficiency and effectiveness at a business level. Apache Hadoop is an example of such a framework. This report discusses big data, it origins, opportunities and challenges that it presents, big data analytics and the application of big data using existing big data tools or frameworks. It also discusses Apache Hadoop as a big data framework and provides a basic overview of this technology from technological and business perspectives.  

Big data/analytics platform for Industry 4.0 implementation in advanced manufacturing context

2021 ◽  
Author(s):  
Renan Bonnard ◽  
Márcio Da Silva Arantes ◽  
Rodolfo Lorbieski ◽  
Kléber Magno Maciel Vieira ◽  
Marcelo Canzian Nunes
Keyword(s):  
Big Data ◽  
Industry 4.0 ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Advanced Manufacturing

scholarly journals Predictors of outpatients’ no-show: big data analytics using apache spark

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Tahani Daghistani ◽  
Huda AlGhamdi ◽  
Riyad Alshammari ◽  
Raed H. AlHazme
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Negative Impact ◽  
Big Data Analytics ◽  
Quality Of Healthcare ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Healthcare Organizations ◽  
Data Framework ◽  
Learning Techniques

AbstractOutpatients who fail to attend their appointments have a negative impact on the healthcare outcome. Thus, healthcare organizations facing new opportunities, one of them is to improve the quality of healthcare. The main challenges is predictive analysis using techniques capable of handle the huge data generated. We propose a big data framework for identifying subject outpatients’ no-show via feature engineering and machine learning (MLlib) in the Spark platform. This study evaluates the performance of five machine learning techniques, using the (2,011,813‬) outpatients’ visits data. Conducting several experiments and using different validation methods, the Gradient Boosting (GB) performed best, resulting in an increase of accuracy and ROC to 79% and 81%, respectively. In addition, we showed that exploring and evaluating the performance of the machine learning models using various evaluation methods is critical as the accuracy of prediction can significantly differ. The aim of this paper is exploring factors that affect no-show rate and can be used to formulate predictions using big data machine learning techniques.

Call for Special Issue Papers: Big Data Analytics and Intelligent Systems for Cybersecurity

Big Data ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 146-147
Author(s):  
Ahmed A. Abd El-Latif ◽  
Lo'ai Tawalbeh ◽  
Yassine Maleh ◽  
Gokay Saldamli
Keyword(s):  
Big Data ◽  
Intelligent Systems ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Special Issue

Big Data Analytics and the Accessibility of Public Inquiries

2020 ◽  
pp. 217-230
Author(s):  
Philip Garnett ◽  
Sarah M. Hughes
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
The Public ◽  
The Us ◽  
The Uk

In this chapter, Garnett and Hughes focus on the role of big data in accessing information from public inquiries. Looking at the Chelsea Manning court martial in the US and the Leveson Inquiry in the UK, they argue that the manner in which information pertaining to inquiries is made public is, at best, unsatisfactory. They propose a variety of means to make this information more accessible and hence more transparent to the public through employing big data techniques.

Intelligent Manufacturing Systems Driven by Artificial Intelligence in Industry 4.0

2022 ◽  
pp. 406-428
Author(s):  
Lejla Banjanović-Mehmedović ◽  
Fahrudin Mehmedović
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Industry 4.0 ◽  
Data Analytics ◽  
Manufacturing Systems ◽  
System Modeling ◽  
Big Data Analytics ◽  
Intelligent Manufacturing ◽  
Intelligent Manufacturing Systems ◽  
Intelligent Decision Making

Intelligent manufacturing plays an important role in Industry 4.0. Key technologies such as artificial intelligence (AI), big data analytics (BDA), the internet of things (IoT), cyber-physical systems (CPSs), and cloud computing enable intelligent manufacturing systems (IMS). Artificial intelligence (AI) plays an essential role in IMS by providing typical features such as learning, reasoning, acting, modeling, intelligent interconnecting, and intelligent decision making. Artificial intelligence's impact on manufacturing is involved in Industry 4.0 through big data analytics, predictive maintenance, data-driven system modeling, control and optimization, human-robot collaboration, and smart machine communication. The recent advances in machine and deep learning algorithms combined with powerful computational hardware have opened new possibilities for technological progress in manufacturing, which led to improving and optimizing any business model.

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