Handling Big Data in Relational Database Management Systems
Source abstract
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.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Cloud Computing and Resource Management · Advanced Database Systems and Queries · Data Management and Algorithms
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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