Proceedings of the 19th International Conference on Quality in Research (QiR 2025)

19th International Conference on Quality in Research (QiR 2025)

📍Yogyakarta, Indonesia🗓️ 27-28 October 2025
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Enhancing AI-Driven Query Generator by Bridging Natural Language and Cloud Data Base

Authors
Michael Harditya1, *, Salma Dewi Taufiqoh1, Vemby Somadias1, Kenya Damayanti Priyatna1, Riri Fitri Sari1
1Electrical Engineering Department, Faculty of Engineering, University of Indonesia, Depok, Indonesia
*Corresponding author.
Corresponding Author
Michael Harditya
Available Online 25 June 2026.
DOI
10.2991/978-94-6239-717-0_12How to use a DOI?
Keywords
SQL; Query Generation; LLM; GPT-3.5
Abstract

Accessing data from modern cloud databases remains a significant challenge for users without expertise in Structured Query Language (SQL), creating a bottleneck in data-driven decision-making. This paper introduces Querier, a system that bridges this gap by leveraging a Large Language Model (LLM), specifically GPT-3.5, to translate high-level natural language prompts into efficient, executable SQL queries. The system is designed for seamless integration with cloud data warehouses like Google BigQuery, handling the end-to-end workflow from user input to result visualization. Performance evaluation demonstrates the system’s high reliability, successfully processing all test prompts of varying complexity, with an average end-to-end response time of under ten seconds. The results validate the effectiveness of using LLMs to enhance AI-driven query generation, providing an accessible and efficient bridge between human language and complex cloud data, thereby empowering a broader range of users to perform data analysis.

Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 19th International Conference on Quality in Research (QiR 2025)
Series
Advances in Engineering Research
Publication Date
25 June 2026
ISBN
978-94-6239-717-0
ISSN
2352-5401
DOI
10.2991/978-94-6239-717-0_12How to use a DOI?
Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Michael Harditya
AU  - Salma Dewi Taufiqoh
AU  - Vemby Somadias
AU  - Kenya Damayanti Priyatna
AU  - Riri Fitri Sari
PY  - 2026
DA  - 2026/06/25
TI  - Enhancing AI-Driven Query Generator by Bridging Natural Language and Cloud Data Base
BT  - Proceedings of the 19th International Conference on Quality in Research (QiR 2025)
PB  - Atlantis Press
SP  - 154
EP  - 169
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6239-717-0_12
DO  - 10.2991/978-94-6239-717-0_12
ID  - Harditya2026
ER  -