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AI-driven SQL query optimization techniques

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Bibliographic Details
Title: AI-driven SQL query optimization techniques
Authors: Juopperi, Tatiana
Publication Year: 2024
Collection: Theseus.fi (Open Repository of the Universities of Applied Sciences / Ammattikorkeakoulujen julkaisuarkisto)
Subject Terms: fi=Tietojenkäsittely|sv=Informationsbehandling|en=Business Information Technology, SQL, query languages, databases, optimisation, artificial intelligence, database programs, machine learning, Query, relational databases, distributed systems, Degree Programme in Business Information Technology
Description: The subject of the research is AI-driven SQL query optimization techniques. The study aims to explore the role and capabilities of Artificial Intelligence in SQL query optimization as one of the methods of tuning and optimization. The research involves both quantitative and qualitative methods. This choice ensures a compre-hensive understanding of AI-driven SQL query optimization techniques. While direct contact with specific professionals was not established, information was collected from publicly available sources such as academic publications, industry blogs, forums, and documentation. The research first explored traditional SQL query optimization methods and their limitations. Then, it explored available AI techniques which can be used to improve query performance. The study included creating and processing SQL queries and execution plan data. The thesis explores how well AI models perform compared to traditional optimization methods in terms of query speed and quality. Additionally, were taken attention of risks with which face users if used both methods. Key findings highlight the role of indexes in traditional SQL query optimization and importance of query execution plans. Additionally, AI-driven techniques such as ChatGPT 3.5 SQL query opti-mization, cost optimization, and AI-driven detection of SQL injection risks. A review of existing AI tools for SQL query optimization is also provided. The research sheds light on the implications of AI-driven SQL query optimization for database administrators and developers, highlighting potential challenges and opportunities in integrating these techniques into existing systems.
Document Type: bachelor thesis
Language: English
Relation: https://www.theseus.fi/handle/10024/861818
Availability: https://www.theseus.fi/handle/10024/861818
Rights: fi=All rights reserved. This publication is copyrighted. You may download, display and print it for Your own personal use. Commercial use is prohibited.|sv=All rights reserved. This publication is copyrighted. You may download, display and print it for Your own personal use. Commercial use is prohibited.|en=All rights reserved. This publication is copyrighted. You may download, display and print it for Your own personal use. Commercial use is prohibited.|
Accession Number: edsbas.1EFE83CA
Database: BASE
Description
Abstract:The subject of the research is AI-driven SQL query optimization techniques. The study aims to explore the role and capabilities of Artificial Intelligence in SQL query optimization as one of the methods of tuning and optimization. The research involves both quantitative and qualitative methods. This choice ensures a compre-hensive understanding of AI-driven SQL query optimization techniques. While direct contact with specific professionals was not established, information was collected from publicly available sources such as academic publications, industry blogs, forums, and documentation. The research first explored traditional SQL query optimization methods and their limitations. Then, it explored available AI techniques which can be used to improve query performance. The study included creating and processing SQL queries and execution plan data. The thesis explores how well AI models perform compared to traditional optimization methods in terms of query speed and quality. Additionally, were taken attention of risks with which face users if used both methods. Key findings highlight the role of indexes in traditional SQL query optimization and importance of query execution plans. Additionally, AI-driven techniques such as ChatGPT 3.5 SQL query opti-mization, cost optimization, and AI-driven detection of SQL injection risks. A review of existing AI tools for SQL query optimization is also provided. The research sheds light on the implications of AI-driven SQL query optimization for database administrators and developers, highlighting potential challenges and opportunities in integrating these techniques into existing systems.