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www.T-Science.org       p-ISSN 2308-4944 (print)       e-ISSN 2409-0085 (online)
SOI: 1.1/TAS         DOI: 10.15863/TAS

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ISJ Theoretical & Applied Science 05(133) 2024

Philadelphia, USA

* Scientific Article * Impact Factor 6.630


Sabinin, O., & Zhemchugova, E.

Automation of database design and development: from domain description to logical model.

Full Article: PDF

Scientific Object Identifier: http://s-o-i.org/1.1/TAS-05-133-15

DOI: https://dx.doi.org/10.15863/TAS.2024.05.133.15

Language: English

Citation: Sabinin, O., & Zhemchugova, E. (2024). Automation of database design and development: from domain description to logical model. ISJ Theoretical & Applied Science, 05 (133), 66-70. Soi: http://s-o-i.org/1.1/TAS-05-133-15 Doi: https://dx.doi.org/10.15863/TAS.2024.05.133.15

Pages: 66-70

Published: 30.05.2024

Abstract: In the era of digital transformation, databases become key elements of information systems, underlining the importance of research in the area of their automated design and development. Particularly pertinent is the task of automating the creation of databases from textual descriptions of subject areas, considering the limited adaptation and availability of existing solutions for the Russian-speaking segment. The goal of this work is to develop a method for the automated designing of databases, adapted to Russian-language descriptions, combining theoretical analysis and practical development. The approach used includes natural language processing and machine learning technologies, enabling the automation of creating logical database models from structured and unstructured texts. The research is theoretical in nature and thus does not involve the demonstration of working results. However, in the future, the results could demonstrate the efficiency of the proposed method in reducing the costs of database design, extending automation capabilities beyond the English-speaking context. This study makes a significant contribution to the automation methods of database design in the Russian-speaking segment. The practical significance of the work lies in simplifying and accelerating database development, making the design process accessible to a wide range of specialists and paving the way for optimizing the creation of information systems.

Key words: relational database, natural language processing (NLP), structured query language (SQL), machine learning.


 

 

 

 

 

 

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