{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T10:37:40Z","timestamp":1776681460754,"version":"3.51.2"},"reference-count":39,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,2,21]],"date-time":"2022-02-21T00:00:00Z","timestamp":1645401600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>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\u2019 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\u2019 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.<\/jats:p>","DOI":"10.3390\/computers11020029","type":"journal-article","created":{"date-parts":[[2022,2,21]],"date-time":"2022-02-21T20:24:21Z","timestamp":1645475061000},"page":"29","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Assessment of SQL and NoSQL Systems to Store and Mine COVID-19 Data"],"prefix":"10.3390","volume":"11","author":[{"given":"Jo\u00e3o","family":"Antas","sequence":"first","affiliation":[{"name":"Polytechnic of Coimbra, Coimbra Institute of Engineering (ISEC), 3030-199 Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5741-6897","authenticated-orcid":false,"given":"Rodrigo","family":"Rocha Silva","sequence":"additional","affiliation":[{"name":"Centre of Informatics and Systems of University of Coimbra (CISUC), 3030-290 Coimbra, Portugal"},{"name":"FATEC Mogi das Cruzes, S\u00e3o Paulo Technological College, Mogi das Cruzes 08773-600, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9660-2011","authenticated-orcid":false,"given":"Jorge","family":"Bernardino","sequence":"additional","affiliation":[{"name":"Polytechnic of Coimbra, Coimbra Institute of Engineering (ISEC), 3030-199 Coimbra, Portugal"},{"name":"Centre of Informatics and Systems of University of Coimbra (CISUC), 3030-290 Coimbra, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,21]]},"reference":[{"key":"ref_1","first-page":"165","article-title":"A Historical Exploration of Pandemics of Some Selected Diseases in the World","volume":"4","author":"Samal","year":"2014","journal-title":"IJHSR"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1016\/S1473-3099(20)30086-4","article-title":"Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: A descriptive study","volume":"20","author":"Shi","year":"2020","journal-title":"Lancet Infect. Dis."},{"key":"ref_3","unstructured":"World Health Organization (2021). Corona disease 2019 (COVID-19) Situation Report\u2014No. 67, WHO."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Leshem, E., and Wilder-Smith, A. (2021). COVID-19 Vaccine Impact in Israel and a Way Out of the Pandemic, Elsevier.","DOI":"10.1016\/S0140-6736(21)01018-7"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1007\/s42979-020-00216-w","article-title":"Predictive data mining models for novel coronavirus (COVID-19) infected patients\u2019 recovery","volume":"1","author":"Muhammad","year":"2020","journal-title":"SN Comput. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Rohini, M., Naveena, K.R., Jothipriya, G., Kameshwaran, S., and Jagadeeswari, M. (2021, January 25\u201327). A Comparative Approach to Predict Corona Virus Using Machine Learning. Proceedings of the International Conference on Artificial Intelligence and Smart Systems (ICAIS), Coimbatore, India.","DOI":"10.1109\/ICAIS50930.2021.9395827"},{"key":"ref_7","first-page":"33","article-title":"Data mining in healthcare: Decision making and precision","volume":"VI","author":"Taranu","year":"2015","journal-title":"Database Syst. J."},{"key":"ref_8","unstructured":"(2021, September 01). Orange Data Mining. Available online: https:\/\/orangedatamining.com."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1504\/IJBPIM.2015.073655","article-title":"SQL or NoSQL? Performance and scalability evaluation","volume":"7","author":"Abramova","year":"2015","journal-title":"Int. J. Bus. Process. Integr. Manag."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Li, Y., and Manoharan, S. (2013, January 27\u201329). A performance comparison of SQL and NoSQL databases. Proceedings of the IEEE Pacific Rim Conference, Conference on Communications and Signal Processing (PACRIM), Victoria, BC, Canada.","DOI":"10.1109\/PACRIM.2013.6625441"},{"key":"ref_11","unstructured":"(2021, September 01). Microsoft SQL Server 2017. Available online: https:\/\/www.microsoft.com\/en-au\/sql-server\/sql-server-2017."},{"key":"ref_12","unstructured":"Mongo, D.B. (2021, September 01). Available online: https:\/\/www.mongodb.com\/."},{"key":"ref_13","unstructured":"(2021, September 01). Cassandra. Available online: https:\/\/cassandra.apache.org\/."},{"key":"ref_14","first-page":"16","article-title":"Type of nosql databases and its comparison with relational databases","volume":"5","author":"Nayak","year":"2013","journal-title":"Int. J. Appl. Inf. Syst. (IJAIS)"},{"key":"ref_15","first-page":"598","article-title":"Relational vs NoSQL Databases: A Survey","volume":"3","author":"Mohamed","year":"2014","journal-title":"Int. J. Comput. Inf. Technol."},{"key":"ref_16","first-page":"314","article-title":"NoSQL Database and Its Comparison with RDBMS","volume":"7","author":"Raut","year":"2015","journal-title":"Int. J. Comput. Intell. Res."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Chakraborty, S., Paul, S., and Hasan, K.M.A. (2021, January 5\u20137). Performance Comparison for Data Retrieval from NoSQL and SQL Databases: A Case Study for COVID-19 Genome Sequence Dataset. Proceedings of the International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST), Dhaka, Bangladesh.","DOI":"10.1109\/ICREST51555.2021.9331044"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Abramova, V., and Bernardino, J. (2013, January 10\u201312). NoSQL Databases: MongoDB vs Cassandra. Proceedings of the C3S2E Proceedings of the International Conference on Computer Science and Software Engineering, Porto, Portugal.","DOI":"10.1145\/2494444.2494447"},{"key":"ref_19","first-page":"17","article-title":"Which NoSQL Database?","volume":"1","author":"Abramova","year":"2014","journal-title":"A Performance Overview. Open J. Databases (OJDB)"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.5121\/ijdms.2014.6301","article-title":"Experimental evaluation of NoSQL databases","volume":"6","author":"Abramova","year":"2014","journal-title":"Int. J. Database Manag. Syst."},{"key":"ref_21","first-page":"741","article-title":"NoSQL Databases: A Software Engineering Perspective","volume":"Volume 353","author":"Abramova","year":"2015","journal-title":"New Contributions in Information Systems and Technologies Advances in Intelligent Systems and Computing"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1007\/s42979-020-00394-7","article-title":"Supervised Machine Learning Models for Prediction of COVID-19 Infection using Epidemiology Dataset","volume":"2","author":"Muhammad","year":"2021","journal-title":"SN Comput. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"022081","DOI":"10.1088\/1742-6596\/1879\/2\/022081","article-title":"Predictions of COVID-19 Spread by Using Supervised Data Mining Techniques","volume":"1879","author":"Awadh","year":"2020","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"100","DOI":"10.48161\/qaj.v1n2a53","article-title":"COVID-19 World Vaccination Progress Using Machine Learning Classification Algorithms","volume":"1","author":"Abdulkareem","year":"2021","journal-title":"Qubahan Acad. J."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Guzm\u00e1n-Torres, J.A., Alonso-Guzm\u00e1n, E.M., Dom\u00ednguez-Mota, F.J., and Tinoco-Guerrero, G. (2021). Estimation of the Main Conditions in (SARS-CoV-2) COVID-19 Patients That Increase the Risk of Death Using Machine Learning, the Case of Mexico, Elsevier.","DOI":"10.1016\/j.rinp.2021.104483"},{"key":"ref_26","first-page":"154","article-title":"Comparing of Data Mining Techniques for Predicting In-Hospital Mortality among Patients with COVID-19","volume":"7","author":"Shanbehzadeh","year":"2021","journal-title":"J. Biostat. Epidemiol."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Keshavarzi, A. Coronavirus Infectious Disease (COVID-19) Modeling: Evidence of Geographical Signals. SSRN Electron. J., 2020.","DOI":"10.2139\/ssrn.3568425"},{"key":"ref_28","unstructured":"Saire, J.E.C. Data Mining Approach to Analyze Covid-19 Dataset of Brazilian Patients. medRxiv, 2020."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Thange, U., Shukla, V.K., and Punhani, R. (2021, January 19\u201321). Analyzing COVID-19 Dataset through Data Mining Tool \u201cOrange\u201d. Proceedings of the International Conference on Computation, Automation and Knowledge Management (ICCAKM), Dubai, United Arab Emirates.","DOI":"10.1109\/ICCAKM50778.2021.9357754"},{"key":"ref_30","unstructured":"Bramer, M. (2007). Principles of Data Mining, Springer."},{"key":"ref_31","first-page":"37","article-title":"From Data Mining to Knowledge Discover in Databases","volume":"17","author":"Fayyad","year":"1996","journal-title":"AI Mag."},{"key":"ref_32","unstructured":"Han, J., Kamber, M., and Pei, J. (2006). Data Mining Concepts and Techniques, Elsevier. [3rd ed.]."},{"key":"ref_33","unstructured":"(2021, September 01). Orange Data Mining Models. Available online: https:\/\/orange3.readthedocs.io\/projects\/orange-visual-programming\/en\/latest\/index.html#."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"14","DOI":"10.36548\/jtcsst.2019.1.002","article-title":"Survey of data mining algorithms for intelligent computing system","volume":"1","author":"Joseph","year":"2019","journal-title":"J. Trends Comput. Sci. Smart Technol. (TCSST)"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"12","DOI":"10.11613\/BM.2014.003","article-title":"Understanding logistic Regression analysis","volume":"24","author":"Sperandei","year":"2014","journal-title":"Biochem. Med."},{"key":"ref_37","unstructured":"Bernardino, J., and Madeira, H. (2001, January 16\u201318). Experimental evaluation of a new distributed partitioning technique for data warehouses. Proceedings of the 2001 International Database Engineering and Applications Symposium, Grenoble, France."},{"key":"ref_38","unstructured":"Bernardino, J., Furtado, P., and Madeira, H. (2002, January 14\u201316). DWS-AQA: A cost effective approach for very large data warehouses. Proceedings of the International Database Engineering and Applications Symposium, Montreal, QC, Canada."},{"key":"ref_39","unstructured":"(2021, September 01). SQL Server Integration Services. Available online: https:\/\/docs.microsoft.com\/en-us\/sql\/integration-services\/ssis-how-to-create-an-etl-package?view=sql-server-ver15."}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/11\/2\/29\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:24:18Z","timestamp":1760135058000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/11\/2\/29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,21]]},"references-count":39,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["computers11020029"],"URL":"https:\/\/doi.org\/10.3390\/computers11020029","relation":{},"ISSN":["2073-431X"],"issn-type":[{"value":"2073-431X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,21]]}}}