Providing Clarity on Big Data Technologies: A Structured Literature Review

2017 ◽  
Author(s):  
Matthias Volk ◽  
Sascha Bosse ◽  
Klaus Turowski
Keyword(s):  
Big Data ◽  
Literature Review ◽  
Big Data Technologies ◽  
Structured Literature Review

scholarly journals Big Data Applications in Vaccinology

2021 ◽  
Vol 6 (2) ◽  
pp. 59-80
Author(s):  
Joseph E. Kasten
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analytics ◽  
Human Race ◽  
Big Data Applications ◽  
The World ◽  
Big Data Technologies ◽  
History Of ◽  
Structured Literature Review ◽  
Over Time

The development of vaccines has been one of the most important medical and pharmacological breakthroughs in the history of the world. Besides saving untold lives, they have enabled the human race to live and thrive in conditions thought far too dangerous only a few centuries ago. In recent times, the development of the COVID-19 vaccine has captured the world’s attention as the primary tool to defeat the current pandemic. The tools used to develop these vaccines have changed dramatically over time, with the use of big data technologies becoming standard in many instances. This study performs a structured literature review centered on the development, distribution, and evaluation of vaccines and the role played by big data tools such as data analytics, datamining, and machine learning. Through this review, the paper identifies where these technologies have made important contributions and in what areas further research is likely to be useful.

scholarly journals Text Mining for Big Data Analysis in Financial Sector: A Literature Review

2019 ◽  
Vol 11 (5) ◽  
pp. 1277 ◽  
Author(s):  
Mirjana Pejić Bach ◽  
Živko Krstić ◽  
Sanja Seljan ◽  
Lejla Turulja
Keyword(s):  
Big Data ◽  
Text Mining ◽  
Literature Review ◽  
Financial Sector ◽  
Structured Data ◽  
Decision Making Process ◽  
Strong Impact ◽  
Big Data Technologies ◽  
Research Questions ◽  
Data Investigation

Big data technologies have a strong impact on different industries, starting from the last decade, which continues nowadays, with the tendency to become omnipresent. The financial sector, as most of the other sectors, concentrated their operating activities mostly on structured data investigation. However, with the support of big data technologies, information stored in diverse sources of semi-structured and unstructured data could be harvested. Recent research and practice indicate that such information can be interesting for the decision-making process. Questions about how and to what extent research on data mining in the financial sector has developed and which tools are used for these purposes remains largely unexplored. This study aims to answer three research questions: (i) What is the intellectual core of the field? (ii) Which techniques are used in the financial sector for textual mining, especially in the era of the Internet, big data, and social media? (iii) Which data sources are the most often used for text mining in the financial sector, and for which purposes? In order to answer these questions, a qualitative analysis of literature is carried out using a systematic literature review, citation and co-citation analysis.

Opportunities and Challenges of Using Big Data Applications in Institutions of Higher Learning Libraries and Research Institutions

2021 ◽  
pp. 107-122
Author(s):  
Josiline Phiri Chigwada
Keyword(s):  
Big Data ◽  
Literature Review ◽  
Higher Learning ◽  
Data Accuracy ◽  
Knowledge And Skills ◽  
Search Terms ◽  
Big Data Applications ◽  
Institutions Of Higher Learning ◽  
Accuracy Data ◽  
Structured Literature Review

The chapter documents opportunities and challenges experienced when using big data applications in libraries. The objective of the study was to examine the big data applications that are used in libraries. The big data concept is new, and some librarians are not aware of it while others do not have the knowledge and skills of using big data applications. A structured literature review was done to examine how libraries use big data. The search terms that were used were “big data AND libraries.” The findings revealed that libraries are generating big data. The challenges that are experienced include data accuracy, data confidentiality and security, lack of skills to deal with data reduction and compression, and the unavailability of big data processing systems and technology in libraries. The author recommends the up skilling of librarians so that they are able to deal with the challenges of working with big data applications.

scholarly journals Data-driven smart eco-cities and sustainable integrated districts: A best-evidence synthesis approach to an extensive literature review

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Simon Elias Bibri
Keyword(s):  
Big Data ◽  
Literature Review ◽  
Urban Ecology ◽  
Smart Cities ◽  
Evidence Synthesis ◽  
Data Driven ◽  
Extensive Literature ◽  
Best Evidence Synthesis ◽  
Big Data Technologies ◽  
Smart Urbanism

AbstractAs materializations of trends toward developing and implementing urban socio-technical and enviro-economic experiments for transition, eco-cities have recently received strong government and institutional support in many countries around the world due to their ability to function as an innovative strategic niche where to test and introduce various  reforms. There are many models of the eco-city based mainly on either following the principles of urban ecology or combining the strategies of sustainable cities and the solutions of smart cities. The most prominent among these models are sustainable integrated districts and data-driven smart eco-cities. The latter model represents the unprecedented transformative changes the eco-city is currently undergoing in light of the recent paradigm shift in science and technology brought on by big data science and analytics.  This is motivated by the growing need to tackle the problematicity surrounding eco-cities in terms of their planning, development, and governance approaches and practices. Employing a combination of both best-evidence synthesis and narrative approaches, this paper provides a comprehensive state-of-the-art and thematic literature review on sustainable integrated districts and data-driven smart eco-cities. The latter new area is a significant gap in and of itself that this paper seeks to fill together with to what extent the integration of eco-urbanism and smart urbanism is addressed in the era of big data, what driving factors are behind it, and what forms and directions it takes. This study reveals that eco-city district developments are increasingly embracing compact city strategies and becoming a common expansion route for growing cities to achieve urban ecology or urban sustainability. It also shows that the new eco-city projects are increasingly capitalizing on data-driven smart technologies to implement environmental, economic, and social reforms. This is being accomplished by combining the strengths of eco-cities and smart cities and harnessing the synergies of their strategies and solutions in ways that enable eco-cities to improve their performance with respect to sustainability as to its tripartite composition. This in turn means that big data technologies will change eco-urbanism in fundamental and irreversible ways in terms of how eco-cities will be monitored, understood, analyzed, planned, designed, and governed. However, smart urbanism poses significant risks and drawbacks that need to be addressed and overcome in order to achieve the desired outcomes of ecological sustainability in its broader sense. One of the key critical questions raised in this regard pertains to the very potentiality of the technocratic governance of data-driven smart eco-cities and the associated negative implications and hidden pitfalls. In addition, by shedding light on the increasing adoption and uptake of big data technologies in eco-urbanism, this study seeks to assist policymakers and planners in assessing the pros and cons of smart urbanism when effectuating ecologically sustainable urban transformations in the era of big data, as well as to stimulate prospective research and further critical debates on this topic.

Trends and Opportunities in Health Analytics as a Service and Implications for Use in Low Resource Settings: A Literature Review Abstract (Preprint)

2019 ◽  
Author(s):  
Meghana Bastwadkar ◽  
Carolyn McGregor ◽  
S Balaji
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Big Data ◽  
Intensive Care ◽  
Literature Review ◽  
Health Monitoring ◽  
Data Analytics ◽  
Neonatal Intensive Care ◽  
Big Data Analytics ◽  
Healthcare Facilities

BACKGROUND This paper presents a systematic literature review of existing remote health monitoring systems with special reference to neonatal intensive care (NICU). Articles on NICU clinical decision support systems (CDSSs) which used cloud computing and big data analytics were surveyed. OBJECTIVE The aim of this study is to review technologies used to provide NICU CDSS. The literature review highlights the gaps within frameworks providing HAaaS paradigm for big data analytics METHODS Literature searches were performed in Google Scholar, IEEE Digital Library, JMIR Medical Informatics, JMIR Human Factors and JMIR mHealth and only English articles published on and after 2015 were included. The overall search strategy was to retrieve articles that included terms that were related to “health analytics” and “as a service” or “internet of things” / ”IoT” and “neonatal intensive care unit” / ”NICU”. Title and abstracts were reviewed to assess relevance. RESULTS In total, 17 full papers met all criteria and were selected for full review. Results showed that in most cases bedside medical devices like pulse oximeters have been used as the sensor device. Results revealed a great diversity in data acquisition techniques used however in most cases the same physiological data (heart rate, respiratory rate, blood pressure, blood oxygen saturation) was acquired. Results obtained have shown that in most cases data analytics involved data mining classification techniques, fuzzy logic-NICU decision support systems (DSS) etc where as big data analytics involving Artemis cloud data analysis have used CRISP-TDM and STDM temporal data mining technique to support clinical research studies. In most scenarios both real-time and retrospective analytics have been performed. Results reveal that most of the research study has been performed within small and medium sized urban hospitals so there is wide scope for research within rural and remote hospitals with NICU set ups. Results have shown creating a HAaaS approach where data acquisition and data analytics are not tightly coupled remains an open research area. Reviewed articles have described architecture and base technologies for neonatal health monitoring with an IoT approach. CONCLUSIONS The current work supports implementation of the expanded Artemis cloud as a commercial offering to healthcare facilities in Canada and worldwide to provide cloud computing services to critical care. However, no work till date has been completed for low resource setting environment within healthcare facilities in India which results in scope for research. It is observed that all the big data analytics frameworks which have been reviewed in this study have tight coupling of components within the framework, so there is a need for a framework with functional decoupling of components.

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