Open Access

REVIEW

A Systematic Review of Automated Classification for Simple and Complex Query SQL on NoSQL Database

Nurhadi, Rabiah Abdul Kadir^*, Ely Salwana Mat Surin, Mahidur R. Sarker^*

Institute of Visual Informatics, The National University of Malaysia, Selangor, 43600, Malaysia

* Corresponding Authors: Rabiah Abdul Kadir. Email: ; Mahidur R. Sarker. Email:

Computer Systems Science and Engineering 2024, 48(6), 1405-1435. https://doi.org/10.32604/csse.2024.051851

Received 17 March 2024; Accepted 08 August 2024; Issue published 22 November 2024

Abstract

A data lake (DL), abbreviated as DL, denotes a vast reservoir or repository of data. It accumulates substantial volumes of data and employs advanced analytics to correlate data from diverse origins containing various forms of semi-structured, structured, and unstructured information. These systems use a flat architecture and run different types of data analytics. NoSQL databases are nontabular and store data in a different manner than the relational table. NoSQL databases come in various forms, including key-value pairs, documents, wide columns, and graphs, each based on its data model. They offer simpler scalability and generally outperform traditional relational databases. While NoSQL databases can store diverse data types, they lack full support for atomicity, consistency, isolation, and durability features found in relational databases. Consequently, employing machine learning approaches becomes necessary to categorize complex structured query language (SQL) queries. Results indicate that the most frequently used automatic classification technique in processing SQL queries on NoSQL databases is machine learning-based classification. Overall, this study provides an overview of the automatic classification techniques used in processing SQL queries on NoSQL databases. Understanding these techniques can aid in the development of effective and efficient NoSQL database applications.

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Keywords

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