Assessment of SQL and NoSQL Systems to Store and Mine COVID-19 Data
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 No...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Computers |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-431X/11/2/29 |
| _version_ | 1797481546709467136 |
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| author | João Antas Rodrigo Rocha Silva Jorge Bernardino |
| author_facet | João Antas Rodrigo Rocha Silva Jorge Bernardino |
| author_sort | João Antas |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T22:16:09Z |
| format | Article |
| id | doaj.art-4f8708967c85482495b0f11887330756 |
| institution | Directory Open Access Journal |
| issn | 2073-431X |
| language | English |
| last_indexed | 2024-03-09T22:16:09Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj.art-4f8708967c85482495b0f118873307562023-11-23T19:22:52ZengMDPI AGComputers2073-431X2022-02-011122910.3390/computers11020029Assessment of SQL and NoSQL Systems to Store and Mine COVID-19 DataJoão Antas0Rodrigo Rocha Silva1Jorge Bernardino2Polytechnic of Coimbra, Coimbra Institute of Engineering (ISEC), 3030-199 Coimbra, PortugalCentre of Informatics and Systems of University of Coimbra (CISUC), 3030-290 Coimbra, PortugalPolytechnic of Coimbra, Coimbra Institute of Engineering (ISEC), 3030-199 Coimbra, PortugalCOVID-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.https://www.mdpi.com/2073-431X/11/2/29big dataCOVID-19Data MiningSQL and NoSQL databases |
| spellingShingle | João Antas Rodrigo Rocha Silva Jorge Bernardino Assessment of SQL and NoSQL Systems to Store and Mine COVID-19 Data Computers big data COVID-19 Data Mining SQL and NoSQL databases |
| title | Assessment of SQL and NoSQL Systems to Store and Mine COVID-19 Data |
| title_full | Assessment of SQL and NoSQL Systems to Store and Mine COVID-19 Data |
| title_fullStr | Assessment of SQL and NoSQL Systems to Store and Mine COVID-19 Data |
| title_full_unstemmed | Assessment of SQL and NoSQL Systems to Store and Mine COVID-19 Data |
| title_short | Assessment of SQL and NoSQL Systems to Store and Mine COVID-19 Data |
| title_sort | assessment of sql and nosql systems to store and mine covid 19 data |
| topic | big data COVID-19 Data Mining SQL and NoSQL databases |
| url | https://www.mdpi.com/2073-431X/11/2/29 |
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