Universal Database Query Template System for Cross-Database Governance
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems face challenges in efficiently generating and managing database queries across different enterprise contexts, particularly in creating reusable queries that can function across multiple databases without requiring specific row or column definitions, and in effectively managing reporting assets and data governance within existing workflows.
Innovation Solution
A method and system for generating database queries by receiving an asset template associated with an enterprise context, identifying mappings of enterprise terms to database tables, compiling the template into a query, verifying and approving the query, and storing it for use across various databases, while also managing reporting assets and data governance through modules like the Concierge, Intelligence Inventory, Curator, and Integration modules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If database queries are created specifically for each database with row or column definitions, then the queries are precise and reliable for that specific database, but the complexity of creating and maintaining queries increases significantly when working across multiple databases
Solution Approach 1:
The patent creates a universal query template system that can function across multiple different databases without requiring database-specific customizations. The template includes parameters that automatically adapt to different database schemas, allowing one template to serve multiple databases while maintaining query accuracy through parameterized adaptation rather than hardcoding database-specific details
Solution Approach 2:
The system uses parameterized queries where database-specific details (row names, column names, table structures) are stored as configurable parameters rather than fixed code. This allows the same query template to be executed against different databases by simply changing the parameter values, maintaining reliability while reducing creation complexity
2Adaptability or versatility
If reusable query templates are created without specific row or column definitions, then the queries can be applied across multiple databases, but the precision and accuracy of data retrieval may be compromised
Solution Approach 1:
The query template system applies local quality by allowing different parts of the template to be customized at the appropriate level of specificity. Common structural elements remain fixed for reusability, while database-specific elements (table names, column names, relationships) are localized as configurable parameters that can be precisely adjusted for each target database, ensuring both reusability and precision
3Reliability
If manual query creation and verification processes are used, then query accuracy can be ensured through review, but the time and resources required for creating and maintaining queries increase
Solution Approach 1:
The system performs preliminary actions by pre-defining query templates with proper structure, relationships, and validation rules before actual query execution. The template compilation process automatically verifies syntax and logic in advance, catching errors before deployment and reducing the need for time-consuming manual verification of individual queries
Solution Approach 2:
The template compilation and verification process is automated, allowing the system to self-verify query templates without requiring manual review for each template. The automatic compilation checks for syntax errors, validates parameter definitions, and ensures template correctness, reducing time loss while maintaining reliability through systematic automated verification
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating database queries, including receiving an asset template, the asset template associated with an enterprise context and one or more enterprise terms; identifying respective mappings of the one or more enterprise terms to one or more database tables; compiling the asset template based on the mappings to provide a database query; providing for display the database query; verifying the database query based on the displaying; receiving, in response to the verifying, an approval signal associated with the database query; storing the database query; querying a different database utilizing the database query; and in response to the querying, identifying data stored by the different database that is responsive to the database query.