A Big Data Management and Analytics Framework for Supporting Machine Learning, OLAP, and Visualization on Big COVID-19 Data

Massive amounts of data, including big data, are generated and collected today from a variety of diverse data sources. These big data differ in terms of their veracity that ranges from imprecise and uncertain to precise. These data hide a huge amount of valuable information and precious knowledge th...

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Vydané v:Journal of database management Ročník 36; číslo 1; s. 1 - 44
Hlavní autori: Cuzzocrea, Alfredo, Leung, Carson K, Shang, Siyuan, Wen, Yan
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Hershey IGI Global 01.01.2025
Predmet:
Big Data
Coronaviruses
COVID-19
Data analysis
Data management
Data mining
Data processing
Data science
Disease
Disease control
Epidemiology
Health care
Health services
Information retrieval
Machine learning
Online analytical processing
Patients
Policy making
Scientific visualization
Secrets
Visualization
ISSN:1063-8016, 1533-8010
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Popis
Shrnutí:Massive amounts of data, including big data, are generated and collected today from a variety of diverse data sources. These big data differ in terms of their veracity that ranges from imprecise and uncertain to precise. These data hide a huge amount of valuable information and precious knowledge that ought to be discovered. Examples of big data in the healthcare and epidemiological fields include information about patients afflicted with diseases such as Coronavirus disease 2019 (COVID-19). Researchers, epidemiologists, and policy makers get a great deal of help from the knowledge discovered from these data via data science techniques such as machine learning, data mining and online analytical processing (OLAP) in order to fully uncover the secrets of the disease. Eventually that may also inspire them to come up with ways to detect, control and fight the disease. In the article, the authors present a machine learning and big data analytical tool useful to process and analyze COVID-19 epidemiological data, while supporting big data visualization and visual analytics.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1063-8016
1533-8010
DOI:10.4018/JDM.386132