Multi-Database Query Translation for Heterogeneous Backup Tables
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Solution Overview
Problem
Current methods lack an effective way to query and execute operations across multiple databases of different types, such as SQL and NoSQL databases, requiring manual conversion of queries which is resource-intensive and often inaccurate.
Innovation Solution
A system utilizing generative AI models to classify column names and generate unified queries across database backups, allowing for free text queries to be executed efficiently on multiple databases without manual conversion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual conversion of queries is performed for each database type, then query compatibility across different database types is achieved, but computational resources and time are significantly consumed
Solution Approach 1:
The patent introduces an intermediary component (query translation service) that automatically converts queries between different database formats. This intermediary handles the complexity of query conversion, allowing users to submit queries in one format while the system automatically adapts them to the target database format, thus maintaining compatibility without manual intervention.
Solution Approach 2:
The system dynamically changes query parameters based on the target database type. By detecting the database format and automatically adjusting query syntax, data types, and structural parameters, the system achieves compatibility across SQL and NoSQL databases without requiring manual conversion for each query.
2Measurement precision
If manual conversion of queries is performed, then query accuracy may be maintained, but significant human resources and time are required
Solution Approach 1:
The system performs preliminary actions by pre-compiling and caching query templates for different database types. When a query needs to be converted, the system retrieves and adapts pre-prepared templates rather than converting from scratch, significantly reducing conversion time while maintaining accuracy through established conversion patterns.
Solution Approach 2:
The query conversion system operates autonomously without requiring human intervention. The automated translation service detects the source and target database formats, selects appropriate conversion rules, and executes the transformation independently, eliminating manual conversion time while preserving query accuracy through systematic conversion logic.
3Productivity
If separate query processing is performed for each database type, then database-specific optimizations are maintained, but system complexity increases
Solution Approach 1:
The system segments the query processing architecture into distinct modular components: query reception module, database detection module, translation module, and execution module. Each component handles a specific aspect of the process, allowing database-specific optimizations to be implemented in isolated modules without increasing overall system complexity. The segmentation enables independent optimization of each component while maintaining a unified interface.
Data Source
AI summary
A system and method for querying multiple database tables in a database backup are provided. The method includes receiving identification of database tables to query, wherein the database tables in a column-based format, and the database backup includes for each database table classification data for at least classifying its respective column names; generating a prompt for a first language model that provides a set of queries targeting the database tables, wherein the generated prompt is based on at least a received input query and the classification data for the database tables; executing the set of queries on the database tables, wherein each query of the set of queries is executed on a respective database table; and aggregating and displaying results returned in response to the execution of the set of queries.