scholarly journals Studying Students' Knowledge of the Benefits, Challenges, and Applications of Big Data Analytics In Healthcare

2020 ◽  
Author(s):  
Elham Nazari ◽  
Maryam Edalati Khodabandeh ◽  
Ali Dadashi ◽  
Marjan Rasoulian ◽  
hamed tabesh
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Work Experience ◽  
Medical Records ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Big Data Analysis ◽  
Information Modeling ◽  
Field Of Study ◽  
Cross Sectional

Abstract Introdution Today, with the advent of technologies and the production of huge amounts of data, Big Data analytics has received much attention especially in healthcare. Understanding this field and recognizing its benefits, applications and challenges provide useful background for conducting efficient research. Therefore, the purpose of this study was to evaluate the students' familiarity from different universities of Mashhad with the benefits, applications and challenges of Big Data analysis.Method This is a cross-sectional study that was conducted on students of Medical Engineering, Medical Informatics, Medical Records and Health Information Management in Mashhad-Iran. A questionnaire was designed based on literature review in pubmed, google scholar, science direct and EMBASE databases, using Delphi method and presence of 10 experts from different fields of study. The designed questionnaire evaluated the opinion of students regarding benefits, challenges and applications of Big Data analytics. 200 students participated in the study and completed the designed questionnaire. Participants' opinions were evaluated descriptively and analytically. Result Most students were between 20 and 30 years old. 63% of them were male and 43.5% had no work experience. Current and previous field of study of most of the students were HIT, HIM, and Medical Records. Most of the participants in this study were undergraduates. 61.5% were economically active, 54.5% were exposed to Big Data. The mean scores of participants in benefits, applications, and challenges section were 3.71, 3.68, and 3.71, respectively, and process management was significant in different age groups (p=0.046), information, modeling, research, and health informatics across different fields of studies were significant (p=0.015, 0.033, 0.001, 0.024) Information and research were significantly different between groups (p=0.043 and 0.019), research in groups with / without economic activity was significant (p= 0.017) and information in exposure / non exposure to Big Data groups was significant (p=0.02). Conclusion Despite the importance and benefits of Big Data analytics, students' lack of familiarity with the necessity and importance of these analytics in industries and research is significant. The field of study and level of study do not appear to have an effect on the degree of knowledge of individuals regarding Big Data analysis. The design of technical training courses in this field may increase the level of knowledge of individuals regarding Big Data analysis.

scholarly journals A multilevel approach to big data analysis using analytic tools and actor network theory

2018 ◽  
Vol 20 (1) ◽  
Author(s):  
Tiko Iyamu
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Network Theory ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Actor Network Theory ◽  
Big Data Analysis ◽  
Data Sets ◽  
Multilevel Approach ◽  
Actor Network

Background: Over the years, big data analytics has been statically carried out in a programmed way, which does not allow for translation of data sets from a subjective perspective. This approach affects an understanding of why and how data sets manifest themselves into various forms in the way that they do. This has a negative impact on the accuracy, redundancy and usefulness of data sets, which in turn affects the value of operations and the competitive effectiveness of an organisation. Also, the current single approach lacks a detailed examination of data sets, which big data deserve in order to improve purposefulness and usefulness.Objective: The purpose of this study was to propose a multilevel approach to big data analysis. This includes examining how a sociotechnical theory, the actor network theory (ANT), can be complementarily used with analytic tools for big data analysis.Method: In the study, the qualitative methods were employed from the interpretivist approach perspective.Results: From the findings, a framework that offers big data analytics at two levels, micro- (strategic) and macro- (operational) levels, was developed. Based on the framework, a model was developed, which can be used to guide the analysis of heterogeneous data sets that exist within networks.Conclusion: The multilevel approach ensures a fully detailed analysis, which is intended to increase accuracy, reduce redundancy and put the manipulation and manifestation of data sets into perspectives for improved organisations’ competitiveness.

scholarly journals Hidden big data analytics issues in the healthcare industry

2019 ◽  
Vol 26 (2) ◽  
pp. 981-998 ◽  
Author(s):  
Kenneth David Strang ◽  
Zhaohao Sun
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Analytics ◽  
Research Method ◽  
Big Data Analytics ◽  
Big Data Analysis ◽  
Healthcare Industry ◽  
Statistical Control ◽  
Subject Matter Experts ◽  
Journal Articles

The goal of the study was to identify big data analysis issues that can impact empirical research in the healthcare industry. To accomplish that the author analyzed big data related keywords from a literature review of peer reviewed journal articles published since 2011. Topics, methods and techniques were summarized along with strengths and weaknesses. A panel of subject matter experts was interviewed to validate the intermediate results and synthesize the key problems that would likely impact researchers conducting quantitative big data analysis in healthcare studies. The systems thinking action research method was applied to identify and describe the hidden issues. The findings were similar to the extant literature but three hidden fatal issues were detected. Methodical and statistical control solutions were proposed to overcome the three fatal healthcare big data analysis issues.

Research of Big Data Processing Platform

2014 ◽  
Vol 484-485 ◽  
pp. 922-926
Author(s):  
Xiang Ju Liu
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Analysis ◽  
Network Architecture ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Big Data Analysis ◽  
Computing Platform ◽  
Operational Characteristics ◽  
Analysis Platform

This paper introduces the operational characteristics of the era of big data and the current era of big data challenges, and exhaustive research and design of big data analytics platform based on cloud computing, including big data analytics platform architecture system, big data analytics platform software architecture , big data analytics platform network architecture big data analysis platform unified program features and so on. The paper also analyzes the cloud computing platform for big data analysis program unified competitive advantage and development of business telecom operators play a certain role in the future.

Big Data Analysis for Trend Recognition Using Machine Learning Techniques

2020 ◽  
Vol 10 (4) ◽  
pp. 540-550
Author(s):  
Cerene Mariam Abraham ◽  
Mannathazhathu Sudheep Elayidom ◽  
Thankappan Santhanakrishnan
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Big Data ◽  
Data Analysis ◽  
Data Analytics ◽  
Research Work ◽  
Big Data Analytics ◽  
Big Data Analysis ◽  
Machine Learning Techniques ◽  
Derivative Market

Background: Machine learning is one of the most popular research areas today. It relates closely to the field of data mining, which extracts information and trends from large datasets. Aims: The objective of this paper is to (a) illustrate big data analytics for the Indian derivative market and (b) identify trends in the data. Methods: Based on input from experts in the equity domain, the data are verified statistically using data mining techniques. Specifically, ten years of daily derivative data is used for training and testing purposes. The methods that are adopted for this research work include model generation using ARIMA, Hadoop framework which comprises mapping and reducing for big data analysis. Results: The results of this work are the observation of a trend that indicates the rise and fall of price in derivatives , generation of time-series similarity graph and plotting of frequency of temporal data. Conclusion: Big data analytics is an underexplored topic in the Indian derivative market and the results from this paper can be used by investors to earn both short-term and long-term benefits.

scholarly journals BIG DATA ANALYTICS AND PRECISION ANIMAL AGRICULTURE SYMPOSIUM: Machine learning and data mining advance predictive big data analysis in precision animal agriculture1

2018 ◽  
Vol 96 (4) ◽  
pp. 1540-1550 ◽  
Author(s):  
Gota Morota ◽  
Ricardo V Ventura ◽  
Fabyano F Silva ◽  
Masanori Koyama ◽  
Samodha C Fernando
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Big Data ◽  
Data Analysis ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Big Data Analysis ◽  
Animal Agriculture

scholarly journals A Survey of Students’ Attitudes to Big Data Analysis in Iranian Universities

2021 ◽  
Vol 7 (2) ◽  
Author(s):  
Nazari E ◽  
◽  
Norouzi S ◽  
Aldaghi T ◽  
Rasoulian M ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Work Experience ◽  
New Technologies ◽  
Master's Degree ◽  
Big Data Analysis ◽  
Cross Sectional ◽  
Academic Degree ◽  
Fields Of Study ◽  
Master’S Degree

Introduction: Today, with the emergence of new technologies and massive data, big data analysis has attracted the attention of researchers, industries and universities on a global scale. The present research aims to explore students’ attitude to big data analysis in different fields of study. Methodology: The present cross-sectional study was conducted with students at different universities and fields of study in Iran. A questionnaire was developed. This questionnaire explored students’ attitude toward big data analysis. To this aim, 359 university students participated in the research. The data were analyzed using descriptive and inferential statistics. Results: The age of the students ranged between 25 and 34 years. 55.2% were female and 54% were economically active. 40.9% had a work experience of less than a year. The academic degree of the majority of participants was master’s degree. 93.9% of the participants believed that big data analysis was essential for the country. 43.2% maintained that big data mostly belonged to the communication industry. 28.1% perceived MATLAB useful software for analysis. 40.9% were familiar with the benefits of analysis. Engage in economic activities, less than 1 year of experience and studies for a Master’s degree showed to be significantly correlated with familiarity with the benefits of big data (p=0.01). Such issues as high costs, managers’ unfamiliarity and lack of expertise and complexity were raised by the respondents.

scholarly journals 9.1. Big data analytics

2020 ◽  
Author(s):  
В.Т. Чая ◽  
Н.И. Чупахина
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analysis ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Big Data Analysis ◽  
Digital Economy ◽  
Big Data Technologies

В связи с развитием технологий цифровой экономики возрастает по экспоненте и объем оцифрованной информации. Но информация имеет ценность, только если она анализируется определенным образом. Большие же объемы информации привычными методами анализировать невозможно. Речь уже идет о больших данных и технологиях больших данных. В статье описаны особенности больших данных. Рассмотрены методы и инструменты анализа больших данных. Подробно рассматривается такой метод решения задач на основе больших данных, как машинное обучение. In connection with the development of digital economy technologies, the volume of digitized information is growing exponentially. But information has value only if it is analyzed in a certain way. It is impossible to analyze large amounts of information using the usual methods. We are already talking about big data and big data technologies. The article describes the features of big data. Methods and tools for big data analysis are considered. Such a method of solving problems based on big data as machine learning is considered in detail.

Big Data Mining and Knowledge Discovery

2016 ◽  
Vol 9 ◽  
pp. 5 ◽  
Author(s):  
Tomas Ruzgas ◽  
Kristina Jakubėlienė ◽  
Aistė Buivytė
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Analysis ◽  
Knowledge Discovery ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Big Data Analysis ◽  
Big Data Mining

The article dealt with exploration methods and tools for big data. It identifies the challenges encountered in the analysis of big data. Defined notion of big data. describe the technology for big data analysis. Article provides an overview of tools which are designed for big data analytics.

scholarly journals Universities Students' Knowledge About Big Data Analysis

2020 ◽  
Vol 9 (1) ◽  
pp. 50
Author(s):  
Elham Nazari ◽  
Marjan Rasoulian ◽  
Samane Sistani ◽  
Maryam Edalati Khodabandeh ◽  
Hamed Tabesh
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
University Students ◽  
Work Experience ◽  
Basic Science ◽  
Big Data Analysis ◽  
Science Students ◽  
Cross Sectional ◽  
Related Literature ◽  
Fields Of Study

Introduction: Big data analysis has raised controversies today and attracted many students and academics for its dramatic advantages. The present research aims to investigate the extent to which students in different universities of Mashhad are familiar with this type of analysis.Material and Methods: The present cross-sectional research was conducted on university students of different fields of study in Mashhad, Iran. A questionnaire was developed based on a review of the related literature in PubMed, Google Scholar, Science Direct and EMBAS. The target questionnaire explored students' knowledge of big data analysis. To this aim, 142 students participated in this research and completed the target questionnaire. Their responses were analyzed descriptively.Results: The majority age of participants ranged between 21 and 28 years. 59% of these participants were female; 27% had less than a year of work experience; the academic grade of the majority of participants was Master's or Ph.D. 42% enjoyed a desirable knowledge of big data analysis. The largest number of hours of scientific and non-scientific studies belonged to basic science students and more specifically that of pharmacology.Conclusion: Despite the significance and benefits of big data analysis, students' unfamiliarity with the essentiality of these analyses in industries and research is considerable. It seems that the field or grade of studies has no effect on one's knowledge of big data analysis. Probably, the design of specialized educational courses with this concern can help to promote individuals' knowledge of big data analysis.

scholarly journals Applications of Big Data Analytics to Control COVID-19 Pandemic

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2282
Author(s):  
Shikah J. Alsunaidi ◽  
Abdullah M. Almuhaideb ◽  
Nehad M. Ibrahim ◽  
Fatema S. Shaikh ◽  
Kawther S. Alqudaihi ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Big Data Analysis ◽  
Vital Role ◽  
Future Research ◽  
Precautionary Measures ◽  
And Behavior ◽  
And Control

The COVID-19 epidemic has caused a large number of human losses and havoc in the economic, social, societal, and health systems around the world. Controlling such epidemic requires understanding its characteristics and behavior, which can be identified by collecting and analyzing the related big data. Big data analytics tools play a vital role in building knowledge required in making decisions and precautionary measures. However, due to the vast amount of data available on COVID-19 from various sources, there is a need to review the roles of big data analysis in controlling the spread of COVID-19, presenting the main challenges and directions of COVID-19 data analysis, as well as providing a framework on the related existing applications and studies to facilitate future research on COVID-19 analysis. Therefore, in this paper, we conduct a literature review to highlight the contributions of several studies in the domain of COVID-19-based big data analysis. The study presents as a taxonomy several applications used to manage and control the pandemic. Moreover, this study discusses several challenges encountered when analyzing COVID-19 data. The findings of this paper suggest valuable future directions to be considered for further research and applications.

Sign in / Sign up

Export Citation Format

Share Document