Intelligent SQL query generation from natural language
Kudrin, Semen (2025)
2025
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2025060520731
https://urn.fi/URN:NBN:fi:amk-2025060520731
Tiivistelmä
The development of information processing technologies has traditionally aimed to simplify the interaction between humans and machines. The shift from machine code to high-level programming languages has made software development more accessible, but working with databases still demands specialized knowledge and remains inaccessible to a wide range of users.
The purpose of this thesis was to create a system that allows users to obtain data using common language queries without the need to learn specialized programming languages. This approach aimed to remove the main technical barrier, making information more accessible.
The thesis addresses several technical challenges in dealing with SQL: accurately interpreting natural language, automatically retrieving relevant data, and minimizing errors in command generation. Particular emphasis was placed on ensuring system flexibility for diverse data types and usage scenarios.
The theoretical foundation explores how people can interact with databases using everyday language. It discusses the difficulties of translating such language into correct data requests, especially when working with location-based information. The study looks at existing solutions and highlights the need for tools that help users get the right results without needing to understand technical query languages.
The result is a tool that not only simplifies access to information but also opens new opportunities for integration with analytical and visualization platforms where ease of data retrieval is fundamentally important.
The purpose of this thesis was to create a system that allows users to obtain data using common language queries without the need to learn specialized programming languages. This approach aimed to remove the main technical barrier, making information more accessible.
The thesis addresses several technical challenges in dealing with SQL: accurately interpreting natural language, automatically retrieving relevant data, and minimizing errors in command generation. Particular emphasis was placed on ensuring system flexibility for diverse data types and usage scenarios.
The theoretical foundation explores how people can interact with databases using everyday language. It discusses the difficulties of translating such language into correct data requests, especially when working with location-based information. The study looks at existing solutions and highlights the need for tools that help users get the right results without needing to understand technical query languages.
The result is a tool that not only simplifies access to information but also opens new opportunities for integration with analytical and visualization platforms where ease of data retrieval is fundamentally important.