Abstract:
Problem: NL2SQL systems allow non-technical users to query databases without having
knowledge of SQL language and the database table structure. However, the users will struggle
to understand the output due to lack of knowledge on the table structure and the data is in a
RAW form. This project aims to address this gap in NL2SQL system by automating the system
to transform the RAW data into visualizations that are more human in readable report structure.
Methodology: The proposed approach is about developing an NL2SQL Data Visualization
Agent that uses machine learning algorithms to process SQL outputs. The system consists of a
chart selection model that dynamically chooses the most suitable chart types based on data
structure and user input. Real-time data processing techniques are used to ensure the system
can handle large datasets efficiently. The methodology follows an agile development process,
integrating user feedback and testing at each stage to refine the system's functionality.
Initial Results: A pre-trained Hugging Face transformer model is used for NL2SQL
translation, and the prototype system was evaluated using it. Several natural language queries
were fed to the model which were converted to a corresponding SQL statement and executed
on an SQLite database. Based on the data structure of each SQL query result, the model was
able to recommend the correct chart types (line, bar, pie, and more) for that result. These initial
results show that this Python based implementation is feasible to produce accurate data
visualizations consistent with SQL query output, and that integrating NLP with chart prediction
is effective.
