Handling Big Data in Relational Database Management Systems

作     者：Kamal ElDahshan Eman Selim Ahmed Ismail Ebada Mohamed Abouhawwash Yunyoung Nam Gamal Behery 

作者机构：Faculty of ScienceAl-Azhar UniversityCairoEgypt Faculty of Computer and Artificial IntelligenceDamietta UniversityEgypt Department of MathematicsFaculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational MathematicsScienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA Department of Computer Science and EngineeringSoonchunhyang UniversityAsan31538Korea 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第72卷第9期

页      面：5149-5164页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute(KHIDI) funded by the Ministry of Health&Welfare Republic of Korea(Grant Number:HI21C1831)and the Soonchunhyang University Research Fund 

主　　题：Big data RDBMS NoSQL DBMSs MongoDB MySQL unstructured data semi-structured data 

摘      要：Currently, relational database management systems (RDBMSs)face different challenges in application development due to the massive growthof unstructured and semi-structured data. This introduced new DBMS categories, known as not only structured query language (NoSQL) DBMSs, whichdo not adhere to the relational model. The migration from relational databasesto NoSQL databases is challenging due to the data complexity. This study aimsto enhance the storage performance of RDBMSs in handling a variety of *** paper presents two approaches. The first approach proposes a convenientrepresentation of unstructured data storage. Several extensive experimentswere implemented to assess the efficiency of this approach that could resultin substantial improvements in the RDBMSs storage. The second approachproposes using the JavaScript Object Notation (JSON) format to representmultivalued attributes and many to many (M:N) relationships in relationaldatabases to create a flexible schema and store semi-structured data. Theresults indicate that the proposed approaches outperform similar approachesand improve data storage performance, which helps preserve software stabilityin huge organizations by improving existing software packages whose replacement may be highly costly.