arbiter
JournalsAccount

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

Alfredo Cuzzocrea et al.

Journal of Database Management2025https://doi.org/10.4018/jdm.386132article
AJG 1ABDC A
Weight
0.37

Abstract

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.

1 citation

Open via your library →

Cite this paper

https://doi.org/https://doi.org/10.4018/jdm.386132

Or copy a formatted citation

@article{alfredo2025,
  title        = {{A Big Data Management and Analytics Framework for Supporting Machine Learning, OLAP, and Visualization on Big COVID-19 Data}},
  author       = {Alfredo Cuzzocrea et al.},
  journal      = {Journal of Database Management},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.4018/jdm.386132},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

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

Flags are reviewed by the Arbiter methodology team within 5 business days.

Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.