Test model for database architectures: an assessment for job search engine systems
Information systems are increasingly complex structures due to the diversity of processes involved and the big data generated, hence data management is essential. NoSQL databases adopt new approaches to data management differing from relational structures. In this study, three databases were designe...
- Autores:
-
Bernal, Mary Carlota
Molina, Yeimer
- Tipo de recurso:
- Fecha de publicación:
- 2022
- Institución:
- Universidad Simón Bolívar
- Repositorio:
- Repositorio Digital USB
- Idioma:
- eng
- OAI Identifier:
- oai:bonga.unisimon.edu.co:20.500.12442/11533
- Acceso en línea:
- https://hdl.handle.net/20.500.12442/11533
https://doi.org/10.22201/icat.24486736e.2022.20.3.1169
- Palabra clave:
- Relational database
NoSQL
Functionality
Efficiency
Test model
Job search engine systems
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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Test model for database architectures: an assessment for job search engine systems |
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Test model for database architectures: an assessment for job search engine systems |
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Test model for database architectures: an assessment for job search engine systems Relational database NoSQL Functionality Efficiency Test model Job search engine systems |
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Test model for database architectures: an assessment for job search engine systems |
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Test model for database architectures: an assessment for job search engine systems |
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Bernal, Mary Carlota Molina, Yeimer |
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Bernal, Mary Carlota Molina, Yeimer |
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Relational database NoSQL Functionality Efficiency Test model Job search engine systems |
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Relational database NoSQL Functionality Efficiency Test model Job search engine systems |
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Information systems are increasingly complex structures due to the diversity of processes involved and the big data generated, hence data management is essential. NoSQL databases adopt new approaches to data management differing from relational structures. In this study, three databases were designed, a relational database using PostgreSQL and two NoSQL databases made in MongoDB applied to operation of a job offer system, with the aim of comparing its operation and efficiency. A method was proposed for the metric-guided evaluation of database models using functionality and efficiency criteria according to Systems and Software Standard Quality Requirements and Evaluation (SQuaRE). Testing cases were created considering the International Software Testing Qualifications Board (ISTQB) best practices. Relational data model was selected as a pattern, for this reason, to populate NoSQL databases a reference framework was applied for data migration from one environment to another, thus the tests were performed under the same hardware, software and data conditions. This study determined that the SQL schema provides greater functionality, ensuring transaction support and data integrity. On the other hand, the NoSQL schemas are more efficient in response to big data processing, although they have a certain level of data duplication, transaction support fails and some join operations are not support. |
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Bernal, M., & Molina, Y. (2022). Test model for database architectures: an assessment for job search engine systems. Journal of Applied Research and Technology, 20(3), 306-319. https://doi.org/10.22201/icat.24486736e.2022.20.3.1169 |
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Bernal, M., & Molina, Y. (2022). Test model for database architectures: an assessment for job search engine systems. Journal of Applied Research and Technology, 20(3), 306-319. https://doi.org/10.22201/icat.24486736e.2022.20.3.1169 24486736 |
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Instituto de Ciencias Aplicadas y Tecnología, ICAT |
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Journal of Applied Reserch and Tecnology, JART |
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Vol 20 No. 3 (2022) |
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Bernal, Mary Carlotaa4a05de6-132a-41ef-b763-ddbdb1776814Molina, Yeimer3a0c8f71-eaa5-40a6-86a7-121ff2eccaef2022-11-26T14:38:33Z2022-11-26T14:38:33Z2022Bernal, M., & Molina, Y. (2022). Test model for database architectures: an assessment for job search engine systems. Journal of Applied Research and Technology, 20(3), 306-319. https://doi.org/10.22201/icat.24486736e.2022.20.3.116924486736https://hdl.handle.net/20.500.12442/11533https://doi.org/10.22201/icat.24486736e.2022.20.3.1169Information systems are increasingly complex structures due to the diversity of processes involved and the big data generated, hence data management is essential. NoSQL databases adopt new approaches to data management differing from relational structures. In this study, three databases were designed, a relational database using PostgreSQL and two NoSQL databases made in MongoDB applied to operation of a job offer system, with the aim of comparing its operation and efficiency. A method was proposed for the metric-guided evaluation of database models using functionality and efficiency criteria according to Systems and Software Standard Quality Requirements and Evaluation (SQuaRE). Testing cases were created considering the International Software Testing Qualifications Board (ISTQB) best practices. Relational data model was selected as a pattern, for this reason, to populate NoSQL databases a reference framework was applied for data migration from one environment to another, thus the tests were performed under the same hardware, software and data conditions. This study determined that the SQL schema provides greater functionality, ensuring transaction support and data integrity. On the other hand, the NoSQL schemas are more efficient in response to big data processing, although they have a certain level of data duplication, transaction support fails and some join operations are not support.pdfengInstituto de Ciencias Aplicadas y Tecnología, ICATAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Journal of Applied Reserch and Tecnology, JARTVol 20 No. 3 (2022)Relational databaseNoSQLFunctionalityEfficiencyTest modelJob search engine systemsTest model for database architectures: an assessment for job search engine systemsinfo:eu-repo/semantics/articleArtículo científicohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_2df8fbb1Antaño, A. C. M., Castro, J. M. M., & Valencia, R. E. C. (2014). Migración de Bases de Datos SQL a NoSQL. Tlamati, Especial 3, 144-148. CICOM.Marqués, M. (2011). Bases de datos (Publicacions de la Universitat Jaume I. Servei de Comunicació i Publicacions (Ed.); Primera edición).Beach, P. M., Langhals, B. T., Grimaila, M. R., Hodson, D. D., & Engle, R. D. L. (2020). A Methodology to Identify Alternative Suitable NoSQL Data Models via Observation of Relational Database Interactions. Theses and Dissertations. 4339.Bugiotti, F., Cabibbo, L., Atzeni, P., & Torlone, R. (2014). Database design for NoSQL systems. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8824, 223–231. https://doi.org/10.1007/978-3-319-12206-9_18Codd, E. F. (1971). A data base sublanguage founded on the relational calculus. In Proceedings of the 1971 ACM SIGFIDET (now SIGMOD) workshop on data description, access and control (pp. 35-68). https://doi.org/10.1145/1734714.1734718Date, C. J. (2000). An Introduction to Database Systems: eBook Addison-Wesley Longman Publishing Co., Inc. (7th Ed.).Ercan, M. Z., & Lane, M. (2014). An evaluation of NoSQL databases for EHR systems. In Proceedings of the 25th Australasian Conference on Information Systems. Auckland University of Technology, School of Business Information Systems.Fouad, T., & Mohamed, B. (2019). Model Transformation From Object Relational Database to NoSQL Document Database. In Proceedings of the 2nd International Conference on Networking, Information Systems & Security (pp. 1-5). https://doi.org/10.1145/3320326.3320381Fraczek, K., & Plechawska-Wojcik, M. (2017). Comparative analysis of relational and non-relational databases in the context of performance in web applications. In International Conference: Beyond Databases, Architectures and Structures 716, 153-164. https://doi.org/10.1007/978-3-319-58274-0_13Ghotiya, S., Mandal, J., & Kandasamy, S. (2017). Migration from relational to NoSQL database. In IOP Conference Series: Materials Science and Engineering, 263(4), 042055. https://doi.org/10.1088/1757-899X/263/4/042055Han, J., Haihong, E., Le, G., & Du, J. (2011). Survey on NoSQL database. In 2011 6th International Conference on Pervasive Computing and Applications. 363-366. IEEE. https://doi.org/10.1109/ICPCA.2011.6106531Hows, D., Membrey, P., & Plugge, E. (2014). MongoDB Basics. In MongoDB Basics. Apress. https://doi.org/10.1007/978-1-4842-0895-3Imam, A. A., Basri, S., Ahmad, R., & González-Aparicio, M. T. (2019). Schema proposition model for NoSQL applications. Advances in Intelligent Systems and Computing, 843, 30–39. https://doi.org/10.1007/978-3-319-99007-1_3Imam, A. A., Basri, S., Ahmad, R., Watada, J., & González Aparicio, M. T. (2018). Automatic schema suggestion model for NoSQL document-stores databases. Journal of Big Data, 5(1), Imam, A. A., Basri, S., Ahmad, R., Watada, J., & González Aparicio, M. T. (2018). Automatic schema suggestion model for NoSQL document-stores databases. Journal of Big Data, 5(1), 46. https://doi.org/10.1186/s40537-018-0156-1ISO. (2014). ISO/IEC. Systems and software engineering — Systems and software Quality Requirements and Evaluation (SQuaRE) — Guide to SQuaRE. https://www.iso.org/obp/ui/#iso:std:iso-iec:25000:ed-2:v1:enISTQB® International Software Testing Qualifications Board. (2019). Downloads - ISTQB® International Software Testing Qualifications Board. https://www.istqb.org/downloads.htmlKumar, M. S., & . Jayagopal, P.. (2018). Comparison of NoSQL Database and Traditional Database-An emphatic analysis. JOIV : International Journal on Informatics Visualization, 2(2), 51. https://doi.org/10.30630/joiv.2.2.58Mearaj, I., Maheshwari, P., & Kaur, M. J. (2019). Data conversion from Traditional Relational Database to MongoDB using XAMPP and NoSQL. In 2018 Fifth HCT Information Technology Trends (ITT) (pp. 94-98). IEEE. https://doi.org/10.1109/CTIT.2018.8649513Elmasri Ramez, & Navathe Shamkant (2022). Fundamentals of Database Systems. eBook. In S. Dissano (Ed.), USA (7th ed.). Pearson.Paredaens, J., De Bra, P., Gyssens, M., & Van Gucht, D. (1989). Relational Database Model. In The Structure of the Relational Database Model. EATCS Monographs on Theoretical Computer Science, vol 17. https://doi.org/10.1007/978-3-642-69956-6_1Scherzinger, S., Klettke, M., & Störl, U. (2013). Managing schema evolution in NoSQL data stores. https://doi.org/10.48550/arxiv.1308.0514Schreiner, G. A., Duarte, D., & dos Santos Mello, R. (2020). Bringing SQL databases to key-based NoSQL databases: a canonical approach. Computing, 102(1), 221-246. https://doi.org/10.1007/s00607-019-00736-1Sokolova, M. V., Gómez, F. J., & Borisoglebskaya, L. N. (2020). Migration from an SQL to a hybrid SQL/NoSQL data model. Journal of Management Analytics, 7(1), 1–11. https://doi.org/10.1080/23270012.2019.1700401The PostgreSQL Global Development Group (2013). The world’s most advanced open source database. https://www.postgresql.org/Tziatzios, D. (2019). Model-based Testing for SQL Databases. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-269424Wijaya, Y. S., & Arman, A. A. (2018). A framework for data migration between different datastore of NoSQL database. In 2018 International Conference on ICT for Smart Society (ICISS) (pp. 1-6). IEEE. https://doi.org/10.1109/ICTSS.2018.8549944Zafar, R., Yafi, E., Zuhairi, M. F., & Dao, H. (2016, May). Big data: the NoSQL and RDBMS review. In 2016 International Conference on Information and Communication Technology (ICICTM) (pp. 120-126). 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