Abstract:
"The use of natural language to query databases has become increasingly popular, particularly for non-technical users. However, mapping natural language queries to SQL remains a challenging task due to the inherent complexity of natural language processing. This poses a significant problem for businesses that want to leverage big data but lack the technical expertise to do so. People who do not have SQL knowledge, on the other hand, will be unable to obtain the necessary information.
To overcome these issues GenSQL proposes a solution that gives hand for non-technical users to interact with SQL Queries when they need to perform any action with databases. The system automates the conversion of natural language text into SQL queries, enabling non-technical users to interact with databases easily and retrieve the required data. It can process complex queries and manage multiple SQL query creation, making data retrieval more efficient and effective. Overall, GenSQL provides a valuable tool for businesses to access data insights and make informed decisions based on data.
The system's performance was evaluated using BLEU and ROUGE scores, which are data science metrics that measure the similarity between generated SQL queries and the ground truth. Additionally, user studies were conducted to evaluate the system's usability, where it was found to be effective in retrieving the desired data. GenSQL offers a valuable solution for businesses interested in leveraging big data, particularly for non-technical users without SQL expertise."
