scholarly journals Big Data for Public Domain: A bibliometric and visualized study of the scientific discourse during 2000–2020

2021 ◽  
Vol 5 (3) ◽  
pp. 220
Author(s):  
Prakoso Bhairawa Putera ◽  
Rostiena Pasciana
Keyword(s):  
Big Data ◽  
United Kingdom ◽  
Data Privacy ◽  
Data Science ◽  
Smart Cities ◽  
Open Data ◽  
The United States ◽  
Scientific Publications ◽  
Productive Author ◽  
The University

This article aims to investigate the trend of scientific publications under ‘big data and policy’ research during the last two decades, including the dynamics of the network structure of researchers and the institutions. Bibliometrics is utilized as a tool to reveal the dynamics of scientific discussions that occur through articles, published in international journals indexed/contained in the Scopus database; meanwhile, the analysis visualization is performed by using VOSviewer 1.6.16. The search results indicate that the United States serves as the country of origin for most productive author affiliations in publishing articles, the University of Oxford (United Kingdom) serves as the home institution for most productive author affiliations, and Williamson, B., from the University of Edinburgh (United Kingdom), is considered as the most prolific writer. In addition, the Swiss Sustainability Journal from MDPI is cited as the source for the most widely discussed publication topic in its journals. Further, ‘Big Data for Development: A Review of Promises and Challenges’ is regarded as the article with the most references. Additionally, the most discussed topics on ‘big data and policy’ include smart cities, open data, privacy, artificial intelligence, machine learning, and data science.

scholarly journals Big Data Analytics in Australian Local Government

Smart Cities ◽  
2020 ◽  
Vol 3 (3) ◽  
pp. 657-675
Author(s):  
Richard B. Watson ◽  
Peter J. Ryan
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Local Governments ◽  
Data Science ◽  
Smart Cities ◽  
Open Data ◽  
Big Data Analytics ◽  
State Level ◽  
Local Council ◽  
Open Datasets

Australian governments at all three levels—local (council), state, and federal—are beginning to exploit the massive amounts of data they collect through sensors and recording systems. Their aim is to enable Australian communities to benefit from “smart city” initiatives by providing greater efficiencies in their operations and strategic planning. Increasing numbers of datasets are being made freely available to the public. These so-called big data are amenable to data science analysis techniques including machine learning. While there are many cases of data use at the federal and state level, local councils are not taking full advantage of their data for a variety of reasons. This paper reviews the status of open datasets of Australian local governments and reports progress being made in several student and other projects to develop open data web services using machine learning for smart cities.

Big Data Driven Clinical Informatics & Surveillance (BDD_CIS) – A Multimodal Database Focused Clinical, Community, and Multi-Omics Surveillance Plan for COVID-19: A study Protocol (Preprint)

2020 ◽  
Author(s):  
Bankole Olatosi ◽  
Jiajia Zhang ◽  
Sharon Weissman ◽  
Zhenlong Li ◽  
Jianjun Hu ◽  
...  
Keyword(s):  
Big Data ◽  
South Carolina ◽  
Data Science ◽  
Age Groups ◽  
The Elderly ◽  
The United States ◽  
Data Sources ◽  
Patient Registries ◽  
Multiple Partner ◽  
Multimodal Data

BACKGROUND The Coronavirus Disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus (SARS-CoV-2) remains a serious global pandemic. Currently, all age groups are at risk for infection but the elderly and persons with underlying health conditions are at higher risk of severe complications. In the United States (US), the pandemic curve is rapidly changing with over 6,786,352 cases and 199,024 deaths reported. South Carolina (SC) as of 9/21/2020 reported 138,624 cases and 3,212 deaths across the state. OBJECTIVE The growing availability of COVID-19 data provides a basis for deploying Big Data science to leverage multitudinal and multimodal data sources for incremental learning. Doing this requires the acquisition and collation of multiple data sources at the individual and county level. METHODS The population for the comprehensive database comes from statewide COVID-19 testing surveillance data (March 2020- till present) for all SC COVID-19 patients (N≈140,000). This project will 1) connect multiple partner data sources for prediction and intelligence gathering, 2) build a REDCap database that links de-identified multitudinal and multimodal data sources useful for machine learning and deep learning algorithms to enable further studies. Additional data will include hospital based COVID-19 patient registries, Health Sciences South Carolina (HSSC) data, data from the office of Revenue and Fiscal Affairs (RFA), and Area Health Resource Files (AHRF). RESULTS The project was funded as of June 2020 by the National Institutes for Health. CONCLUSIONS The development of such a linked and integrated database will allow for the identification of important predictors of short- and long-term clinical outcomes for SC COVID-19 patients using data science.

On Internet of Things and Big Data in University Courses

2019 ◽  
pp. 1393-1406
Author(s):  
Dmitry Namiot ◽  
Manfred Sneps-Sneppe
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Information And Communication Technologies ◽  
Smart Cities ◽  
Communication Technologies ◽  
Machine To Machine ◽  
University Courses ◽  
Information And Communication ◽  
The University ◽  
Educational Course

In this paper, the authors discuss Internet of Things educational programs for universities. The authors' final goal is to provide a structure for a new educational course for Internet of Things and related areas such as Machine to Machine communications and Smart Cities. The Internet of Things skills are in high demands nowadays and, of course, Internet of Things models, as well as appropriate Big Data proceedings elements should have a place in the university courses. The purpose of the proposed educational course is to cover information and communication technologies used in Internet of Things systems and related areas, such as Smart Cities. The educational course proposed in this paper aims to introduce students to modern information and communication technologies and create the formation of competencies needed for such areas as Machine to Machine communications, Internet of Things, and Smart Cities. Also, the authors discuss Big Data issues for IoT course and explain the importance of data engineering.

Consumer Privacy Regulations

2021 ◽  
pp. 1844-1860
Author(s):  
Martha Davis
Keyword(s):  
Big Data ◽  
Consumer Protection ◽  
Data Privacy ◽  
Personal Information ◽  
Smart Cities ◽  
Consumer Trust ◽  
Consumer Privacy ◽  
Societal Benefits ◽  
Globalized Economy ◽  
Information Consumers

Big data and analytics have not only changed how businesses interact with consumers, but also how consumers interact with the larger world. Smart cities, IoT, cloud, and edge computing technologies are all enabled by data and can provide significant societal benefits via efficiencies and reduction of waste. However, data breaches have also caused serious harm to customers by exposing personal information. Consumers often are unable to make informed decisions about their digital privacy because they are in a position of asymmetric information. There are an increasing number of privacy regulations to give consumers more control over their data. This chapter provides an overview of data privacy regulations, including GDPR. In today's globalized economy, the patchwork of international privacy regulations is difficult to navigate, and, in many instances, fails to provide adequate business certainty or consumer protection. This chapter also discusses current research and implications for costs, data-driven innovation, and consumer trust.

scholarly journals Big Data and Analytics in Retailing

2019 ◽  
Vol 11 (1) ◽  
pp. 36-40 ◽  
Author(s):  
Venky Shankar
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Dynamic Pricing ◽  
Data Privacy ◽  
Data Science ◽  
Shopping Behavior ◽  
Computer Algorithms ◽  
Multiple Sources ◽  
Privacy And Security ◽  
Customer Data

AbstractBig data are taking center stage for decision-making in many retail organizations. Customer data on attitudes and behavior across channels, touchpoints, devices and platforms are often readily available and constantly recorded. These data are integrated from multiple sources and stored or warehoused, often in a cloud-based environment. Statistical, econometric and data science models are developed for enabling appropriate decisions. Computer algorithms and programs are created for these models. Machine learning based models, are particularly useful for learning from the data and making predictive decisions. These machine learning models form the backbone for the generation and development of AI-assisted decisions. In many cases, such decisions are automated using systems such as chatbots and robots.Of special interest are issues such as omnichannel shopping behavior, resource allocation across channels, the effects of the mobile channel and mobile apps on shopper behavior, dynamic pricing, data privacy and security. Research on these issues reveals several interesting insights on which retailers can build. To fully leverage big data in today’s retailing environment, CRM strategies must be location specific, time specific and channel specific in addition to being customer specific.

scholarly journals Green Internet of Things and Big Data Application in Smart Cities Development

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Zhai Yang ◽  
Liu Jianjun ◽  
Humaira Faqiri ◽  
Wasswa Shafik ◽  
Alanazi Talal Abdulrahman ◽  
...  
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Life Span ◽  
Information And Communication Technologies ◽  
Resource Sharing ◽  
Wireless Sensors ◽  
Data Science ◽  
Raw Materials ◽  
Technological Development ◽  
Smart Cities

This study reveals that increases in the global population command an augmented demand for products and services that calls for more effective ways of using existing natural resources and materials. The recent development of information and communication technologies, which had a great impact on many areas, also had a damaging effect on the environment and human health. Therefore, societies are moving toward a greener future by reducing the consumption of nonrenewable materials, raw materials, and resources while at the same time decreasing energy pollution and consumption. Since information technology is considered a tool for solving ecological difficulties, the green Internet of things (G-IoT) is playing a vital role in creating a sustainable home. Extensive data analysis is required to obtain a valuable overview of the large and diverse data generated by the G-IoT. The gathered information will facilitate forecasting, decision-making, and other activities related to smart urban services and then contribute to the incessant development of G-IoT technology. Therefore, even if sustainable and smart cities become an actuality, the G-IoT approach and the knowledge gained through big data (BD) analysis will make cities more sustainable, safer, and smarter. The goal of this article is to combine innovation in technological development with the main focus on resource sharing in creating cities that improve the quality of life while reducing pollution and realizing more efficient use of the raw materials. In the practice of big data science, it is always of interest to provide the best description of the data under consideration. Recent studies have pointed out the applicability of the statistical distributions in modeling data in applied sciences. In this article, we introduce a new family of statistical models to provide the best description of the life span of the wireless sensors network’s data. Based on the proposed approach, a special submodel called new exponent power-Weibull distribution is studied in detail. The applicability of the proposed model is shown by analyzing the life span of the wireless sensors network’s data.

scholarly journals The Challenge of Big Data and Data Science

2019 ◽  
Vol 22 (1) ◽  
pp. 297-323 ◽  
Author(s):  
Henry E. Brady
Keyword(s):  
Big Data ◽  
Data Science ◽  
Smart Cities ◽  
Video Data ◽  
Cyber Warfare ◽  
Ethical Challenges ◽  
Cyber Terrorism ◽  
Social Scientists ◽  
The Media ◽  
Audio Video

Big data and data science are transforming the world in ways that spawn new concerns for social scientists, such as the impacts of the internet on citizens and the media, the repercussions of smart cities, the possibilities of cyber-warfare and cyber-terrorism, the implications of precision medicine, and the consequences of artificial intelligence and automation. Along with these changes in society, powerful new data science methods support research using administrative, internet, textual, and sensor-audio-video data. Burgeoning data and innovative methods facilitate answering previously hard-to-tackle questions about society by offering new ways to form concepts from data, to do descriptive inference, to make causal inferences, and to generate predictions. They also pose challenges as social scientists must grasp the meaning of concepts and predictions generated by convoluted algorithms, weigh the relative value of prediction versus causal inference, and cope with ethical challenges as their methods, such as algorithms for mobilizing voters or determining bail, are adopted by policy makers.

Smart Stadium as a Laboratory of Innovation: Technology, Sport, and Datafied Normalization of the Fans

2020 ◽  
pp. 216747952094357
Author(s):  
Chamee Yang ◽  
C. L. Cole
Keyword(s):  
Big Data ◽  
Smart Cities ◽  
The United States ◽  
New Techniques ◽  
Critical Studies ◽  
Social Ordering ◽  
Definition Of ◽  
Smart Technologies ◽  
Contemporary Development ◽  
The Relationship

This article addresses the relationship between the contemporary development of the “smart” stadium and changing norms of innovation in sports. Given the evolving forms of smart technologies blurring the boundaries between the actual and mediated domains of sports, an approach that grapples with the broad sociotechnical dynamics within and around sport is necessary. Drawing from critical studies on big data, innovation, and smart cities, this study adopts a sociotechnical perspective to approach Arizona State University’s Sun Devil Stadium, known as one of the first smart stadiums in the United States. This study examines how the smart stadium employs a range of techniques and technologies to engage with and influence broader sociocultural themes in society: the prevalent imperative of innovation and the hyperdigitalization of sport through which bodies in space are becoming knowable and governable in new ways. We conclude that the smart stadium, articulated both literally and figuratively as a “living laboratory of innovation,” appropriates sport as a useful motif to affect broader cultural debates around big data and spatializes new techniques of social ordering through a parametric and processual definition of normalcy.

Open Data in a Big Data World

2016 ◽  
Vol 38 (2) ◽  
Keyword(s):  
Developing Countries ◽  
Big Data ◽  
Open Access ◽  
Environmental Protection ◽  
Health Research ◽  
Smart Cities ◽  
Open Data ◽  
Scientific Process ◽  
Guiding Principles ◽  
Global Research

AbstractFour major international science organisations (ICSU, ISSC, IAP and TWAS) have joined together to develop and support an accord that includes a set of guiding principles on open access to big data, which is necessary to protect the scientific process and assure that developing countries can participate more fully in the global research enterprise. Limits on access to big data knowledge, they warn, raises the risk that progress will slow in areas such as advanced health research, environmental protection, food productio,n and the development of smart cities.

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