SQLMorpher was tested against 105 real-world data transformation scenarios within the smart building domain and other benchmarks outside the sector. The tool showed promising results based on execution accuracy, column similarity, and iteration counts, indicating its potential to revolutionize automated data transformation in the building sector and beyond.
Key takeaways:
- Recent research has highlighted the need for efficient data transformation in the building sector, which accounts for 40% of the total US energy use.
- SQLMorpher, a tool that uses large language models (LLMs) to generate SQL code, has been proposed to bridge the data transformation gap in this sector.
- Despite challenges such as schema alterations and code accuracy, SQLMorpher uses an iterative loop to refine SQL queries continuously for better accuracy.
- SQLMorpher has been tested and validated in 105 real-world data transformation scenarios within the smart building domain and has shown promising results for the future of automated data transformation.