Understanding Big Data

2020 ◽  
pp. 1-29
Author(s):  
Naciye Güliz Uğur ◽  
Aykut Hamit Turan
Keyword(s):  
Health Care ◽  
Big Data ◽  
Literature Review ◽  
Computing Methods ◽  
Current Status ◽  
Health Care Education ◽  
Business Decisions ◽  
Full Extent ◽  
Implementation Challenges ◽  
Organizational Effects

In today's world, it is necessary to use data or information available in a wise manner to make effective business decisions and define better objectives. If the information available is not utilized to its full extent, organizations might lose their reputation and position in this competitive world. However, data needs to be processed appropriately to gain constructive insights from it, and the heterogeneous nature of this data makes this increasingly more complex and time-consuming. The ever-increasing growth of data generated is far more than human processing capabilities and thus computing methods need to be automated to scale effectively. This chapter defines Big Data basically and provides an overview of Big Data in terms of current status, organizational effects (technology, health care, education, etc.), implementation challenges and Big Data projects. This research adopted literature review as methodology and refined valuable information through current journals, books, magazines and blogs.

Strategic issues of big data analytics applications for managing health-care sector: a systematic literature review and future research agenda

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rajesh Kumar Singh ◽  
Saurabh Agrawal ◽  
Abhishek Sahu ◽  
Yigit Kazancoglu
Keyword(s):  
Health Care ◽  
Big Data ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Health Care Sector ◽  
Future Research ◽  
Research Gaps ◽  
Content Type

PurposeThe proposed article is aimed at exploring the opportunities, challenges and possible outcomes of incorporating big data analytics (BDA) into health-care sector. The purpose of this study is to find the research gaps in the literature and to investigate the scope of incorporating new strategies in the health-care sector for increasing the efficiency of the system.Design/methodology/approachFora state-of-the-art literature review, a systematic literature review has been carried out to find out research gaps in the field of healthcare using big data (BD) applications. A detailed research methodology including material collection, descriptive analysis and categorization is utilized to carry out the literature review.FindingsBD analysis is rapidly being adopted in health-care sector for utilizing precious information available in terms of BD. However, it puts forth certain challenges that need to be focused upon. The article identifies and explains the challenges thoroughly.Research limitations/implicationsThe proposed study will provide useful guidance to the health-care sector professionals for managing health-care system. It will help academicians and physicians for evaluating, improving and benchmarking the health-care strategies through BDA in the health-care sector. One of the limitations of the study is that it is based on literature review and more in-depth studies may be carried out for the generalization of results.Originality/valueThere are certain effective tools available in the market today that are currently being used by both small and large businesses and corporations. One of them is BD, which may be very useful for health-care sector. A comprehensive literature review is carried out for research papers published between 1974 and 2021.

scholarly journals Design of Visibility for Order Lifecycle using Datawarehouse

2019 ◽  
Vol 8 (6) ◽  
pp. 4700-4707
Keyword(s):  
Health Care ◽  
Supply Chain ◽  
Data Warehouse ◽  
Historical Data ◽  
Decision Makers ◽  
Health Care Education ◽  
Business Decisions ◽  
The Public ◽  
Design Considerations

Data warehouse, shortly called DW, a repository to store historical data was widely used across organizations for analyzing the data for any business decisions to be decided. It acts as a decision support system, which will help the decision makers to provide any conclusion based on the analyzed data. DW can be used across any particular fields in the public domain. Some of them would include Retail, Insurance, Finance, Sales, Services, Health Care, Education, etc. This paper analyses and proposes the datawarehouse design considerations for the supply chain. The design was explained with a detailed case study on understanding the visibility of sales order at various stages.

scholarly journals Big data management with machine learning inscribed by domain knowledge for health care

2017 ◽  
Vol 6 (4) ◽  
pp. 98 ◽  
Author(s):  
EPhzibah E.P. ◽  
Sujatha R
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Big Data ◽  
Data Management ◽  
Domain Knowledge ◽  
Disease Diagnosis ◽  
Computing Methods ◽  
Support Vector ◽  
Vector Method ◽  
Medical Practitioners

In this work, a framework that helps in the disease diagnosis process with big-data management and machine learning using rule based, instance based, statistical, neural network and support vector method is given. Concerning this, big-data that contains the details of various diseases are collected, preprocessed and managed for classification. Diagnosis is a day-to-day activity for the medical practitioners and is also a decision-making task that requires domain knowledge and expertise in the specific field. This framework suggests different machine learning methods to aid the practitioner to diagnose disease based on the best classifier that is identified in the health care system. The framework has three main segments like big-data management, machine learning and input/output details of the patient. It has been already proved in the literature that the computing methods do help in disease diagnosis, provided the data about that particular disease is available in the data center. Thus this framework will provide a source of confidence and satisfaction to the doctors, as the model generated is based on the accuracy of the classifier compared to other classifiers.

Use of Big Data in Disaster Recovery: An Integrative Literature Review

2021 ◽  
pp. 1-7
Author(s):  
Andrew J. Rosenblum ◽  
Christopher M. Wend ◽  
Zohaib Akhtar ◽  
Lori Rosman ◽  
Jeffrey D. Freeman ◽  
...  
Keyword(s):  
Big Data ◽  
Literature Review ◽  
Full Text ◽  
Disaster Recovery ◽  
English Language ◽  
Final Analysis ◽  
Current Status ◽  
Inclusion Criteria ◽  
Robust Implementation ◽  
Full Text Review

Abstract Objective: Disasters of all varieties have been steadily increasing in frequency. Simultaneously, “big data” has seen explosive growth as a tool in business and private industries while opportunities for robust implementation in disaster management remain nascent. To more explicitly ascertain the current status of big data as applied to disaster recovery, we conducted an integrative literature review. Methods: Eleven databases were searched using iteratively developed keywords to target big data in a disaster recovery context. All studies were dual-screened by title and abstract followed by dual full-text review to determine if they met inclusion criteria. Articles were included if they focused on big data in a disaster recovery setting and were published in the English-language peer-reviewed literature. Results: After removing duplicates, 25,417 articles were originally identified. Following dual title/abstract review and full-text review, 18 studies were included in the final analysis. Among those, 44% were United States-based and 39% focused on hurricane recovery. Qualitative themes emerged surrounding geographic information systems (GIS), social media, and mental health. Conclusions: Big data is an evolving tool for recovery from disasters. More research, particularly in real-time applied disaster recovery settings, is needed to further expand the knowledge base for future applications.

scholarly journals Current status of use of big data and artificial intelligence in RMDs: a systematic literature review informing EULAR recommendations

RMD Open ◽  
2019 ◽  
Vol 5 (2) ◽  
pp. e001004 ◽  
Author(s):  
Joanna Kedra ◽  
Timothy Radstake ◽  
Aridaman Pandit ◽  
Xenofon Baraliakos ◽  
Francis Berenbaum ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Musculoskeletal Diseases ◽  
Data Sources ◽  
Current Status ◽  
Eular Recommendations ◽  
European League Against Rheumatism ◽  
Data Points

ObjectiveTo assess the current use of big data and artificial intelligence (AI) in the field of rheumatic and musculoskeletal diseases (RMDs).MethodsA systematic literature review was performed in PubMed MEDLINE in November 2018, with key words referring to big data, AI and RMDs. All original reports published in English were analysed. A mirror literature review was also performed outside of RMDs on the same number of articles. The number of data analysed, data sources and statistical methods used (traditional statistics, AI or both) were collected. The analysis compared findings within and beyond the field of RMDs.ResultsOf 567 articles relating to RMDs, 55 met the inclusion criteria and were analysed, as well as 55 articles in other medical fields. The mean number of data points was 746 million (range 2000–5 billion) in RMDs, and 9.1 billion (range 100 000–200 billion) outside of RMDs. Data sources were varied: in RMDs, 26 (47%) were clinical, 8 (15%) biological and 16 (29%) radiological. Both traditional and AI methods were used to analyse big data (respectively, 10 (18%) and 45 (82%) in RMDs and 8 (15%) and 47 (85%) out of RMDs). Machine learning represented 97% of AI methods in RMDs and among these methods, the most represented was artificial neural network (20/44 articles in RMDs).ConclusionsBig data sources and types are varied within the field of RMDs, and methods used to analyse big data were heterogeneous. These findings will inform a European League Against Rheumatism taskforce on big data in RMDs.

Mental health care for rural veterans: A systematic literature review, descriptive analysis, and future directions.

2017 ◽  
Vol 41 (3) ◽  
pp. 222-233 ◽  
Author(s):  
David J. Bumgarner ◽  
Elizabeth J. Polinsky ◽  
Katharine G. Herman ◽  
Joanne M. Fordiani ◽  
Carmen P. Lewis ◽  
...  
Keyword(s):  
Mental Health ◽  
Health Care ◽  
Literature Review ◽  
Mental Health Care ◽  
Systematic Literature Review ◽  
Descriptive Analysis ◽  
Future Directions

Learning Analytics Considered Harmful

2012 ◽  
Vol 16 (3) ◽  
Author(s):  
Laurie P Dringus
Keyword(s):  
Big Data ◽  
Online Learning ◽  
Learning Analytics ◽  
Online Courses ◽  
Research Community ◽  
Current Status ◽  
Online Course ◽  
Full Potential

This essay is written to present a prospective stance on how learning analytics, as a core evaluative approach, must help instructors uncover the important trends and evidence of quality learner data in the online course. A critique is presented of strategic and tactical issues of learning analytics. The approach to the critique is taken through the lens of questioning the current status of applying learning analytics to online courses. The goal of the discussion is twofold: (1) to inform online learning practitioners (e.g., instructors and administrators) of the potential of learning analytics in online courses and (2) to broaden discussion in the research community about the advancement of learning analytics in online learning. In recognizing the full potential of formalizing big data in online coures, the community must address this issue also in the context of the potentially "harmful" application of learning analytics.

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