Open Access

ARTICLE

Handling Big Data in Relational Database Management Systems

Kamal ElDahshan¹, Eman Selim², Ahmed Ismail Ebada², Mohamed Abouhawwash^3,4, Yunyoung Nam^5,*, Gamal Behery²

1 Faculty of Science, Al-Azhar University, Cairo, Egypt
2 Faculty of Computer and Artificial Intelligence, Damietta University, Egypt
3 Department of Mathematics, Faculty of Science, Mansoura University, Mansoura, 35516, Egypt
4 Department of Computational Mathematics, Science, and Engineering (CMSE), Michigan State University, East Lansing, MI, 48824, USA
5 Department of Computer Science and Engineering, Soonchunhyang University, Asan, 31538, Korea

* Corresponding Author: Yunyoung Nam. Email:

Computers, Materials & Continua 2022, 72(3), 5149-5164. https://doi.org/10.32604/cmc.2022.028326

Received 07 February 2022; Accepted 16 March 2022; Issue published 21 April 2022

Abstract

Currently, relational database management systems (RDBMSs) face different challenges in application development due to the massive growth of unstructured and semi-structured data. This introduced new DBMS categories, known as not only structured query language (NoSQL) DBMSs, which do not adhere to the relational model. The migration from relational databases to NoSQL databases is challenging due to the data complexity. This study aims to enhance the storage performance of RDBMSs in handling a variety of data. The paper presents two approaches. The first approach proposes a convenient representation of unstructured data storage. Several extensive experiments were implemented to assess the efficiency of this approach that could result in substantial improvements in the RDBMSs storage. The second approach proposes using the JavaScript Object Notation (JSON) format to represent multivalued attributes and many to many (M:N) relationships in relational databases to create a flexible schema and store semi-structured data. The results indicate that the proposed approaches outperform similar approaches and improve data storage performance, which helps preserve software stability in huge organizations by improving existing software packages whose replacement may be highly costly.

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Keywords

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