Semantic based pandemic prediction using big data

Document Type : Review articles

Authors

1 Dept. of Mathematics, Computer science Division, Faculty of Science (Girls), Al-Azhar University, Cairo, Egypt

2 Dept. of Mathematics, Computer science Division, Faculty of Science, Al-Azhar University, Cairo, Egypt.

Abstract

The world is currently afflicted by a Coronavirus mutation that first appeared in China in 2019 (COVID-19). COVID-19 affected the whole world and was later declared a pandemic by the WHO. The COVID-19 data sources have three key characteristics: a large volume, a high rate of change (velocity), and a wide range of data (variety). Hence it would be better to use NoSQL databases to store and manipulate the data rather than using a relational database. The governments sought a mechanism to forecast the virus's spread so that they could be prepared to deal with it. Instead of using a statistical hypothesis to predict the growth rate of the infected cases, the authors proposed Big Data technology, data mining techniques, and Ontology-based approaches to achieve that. This paper introduces Big Data concepts, technologies, and challenges. It studies NoSQL databases and their types. It also studies the relationship between NoSQL Database and Ontology. A comparative study of pandemic prediction approaches has been reviewed. This review provides important references for building semantic NoSQL Application Program Interface to use in pandemic prediction.

Keywords

International Journal of Theoretical and Applied Research
Volume 1, Issue 1
December 2022
Pages 49-58

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