The implementation solution for automatic visualization of tabular data in relational databases based on large language models

In the data analysis process, visualized data can help users gain better insights. To make it easier and faster for users to obtain visual charts from data, natural language interfaces for data visualization have emerged. Users only need to provide the visualization model with the data to be visualized and a description of their visualization needs, and the model will return a visual chart(NL2VIS). In real-world scenarios, most data is stored in relational databases. To visualize this data, it is first necessary to generate a structured query statements based on the user’s visualization requirements(NL2SQL), and then proceed with the subsequent visualization operations. This study breaks down the task of automatic visualization of tabular data in relational databases into three main steps: generating SQL, determining the chart type, and mapping data to visual channels. We utilize the Chain-of-Thought(CoT) technique of generative large language models to address the task of automatic visualization of tabular data. Finally, we evaluated our approach on the nvBench dataset, and the results show that CoT-based automatic visualization of tabular data performs well.