Abstract
With the increasing amount of textual data and the growing demand for data-driven decision-making, automated SQL generation systems have gained significant attention in recent years. This paper presents GenSQL, a novel natural language sequential model for generating queries with multiple SQL commands. GenSQL is designed to simplify the query writing process for individuals who do not have advanced SQL knowledge, by allowing them to generate queries through natural language input. GenSQL consists of a template-based approach to handle various SQL commands and their respective parameters. Experimental results show that GenSQL achieves high accuracy in generating multi-command queries, outperforming other state-of-the-art models. The system has the potential to improve query generation efficiency, reduce errors, and improve accessibility for users who are not SQL specialists. The preliminary results show that the system has a 91.80% chance of producing accurate results. Ultimately, the goal of natural language text to SQL generation is to enable users to query databases more efficiently and accurately and to reduce the need for specialized skills in writing SQL queries.
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Sonnadara, T.C., Priyadarshana, Y.H.P.P. (2024). A Natural Language Understanding Sequential Model for Generating Queries with Multiple SQL Commands. In: Nagar, A.K., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. WorldS4 2023. Lecture Notes in Networks and Systems, vol 812. Springer, Singapore. https://doi.org/10.1007/978-981-99-8031-4_12
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