JSON Processing and Error Correction in SQL Functions

This paper introduces a tool designed to enhance query accuracy in relational databases by automatically detecting and correcting typographical errors in Structured Query Language (SQL) functions and parameters. By leveraging schema metadata, such as table structures and function signatures, converted into JavaScript Object Notation (JSON) format, the tool validates and corrects errors with minimal manual intervention. Using algorithms like Levenshtein distance, the system automates error correction, streamlining SQL query execution and reducing debugging efforts. This innovative approach bridges gaps in existing tools, offering a practical solution for improving accuracy and efficiency in database management.