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Title: Assessment of SQL and NoSQL Systems to Store and Mine COVID-19 Data
Authors: Antas, João
Silva, Rodrigo Rocha 
Bernardino, Jorge 
Keywords: big data; COVID-19; Data Mining; SQL and NoSQL databases
Issue Date: 2022
Serial title, monograph or event: Computers
Volume: 11
Issue: 2
Abstract: COVID-19 has provoked enormous negative impacts on human lives and the world economy. In order to help in the fight against this pandemic, this study evaluates different databases’ systems and selects the most suitable for storing, handling, and mining COVID-19 data. We evaluate different SQL and NoSQL database systems using the following metrics: query runtime, memory used, CPU used, and storage size. The databases systems assessed were Microsoft SQL Server, MongoDB, and Cassandra. We also evaluate Data Mining algorithms, including Decision Trees, Random Forest, Naive Bayes, and Logistic Regression using Orange Data Mining software data classification tests. Classification tests were performed using cross-validation in a table with about 3 M records, including COVID-19 exams with patients’ symptoms. The Random Forest algorithm has obtained the best average accuracy, recall, precision, and F1 Score in the COVID-19 predictive model performed in the mining stage. In performance evaluation, MongoDB has presented the best results for almost all tests with a large data volume.
ISSN: 2073-431X
DOI: 10.3390/computers11020029
Rights: openAccess
Appears in Collections:I&D CISUC - Artigos em Revistas Internacionais

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