AI Schema Mapping Graph Context Analysis
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Solution Overview
Problem
Existing database schema mapping processes require substantial manual intervention, are laborious, and lack precision and accuracy, especially when dealing with diverse database systems and regulatory reporting requirements like the BIRD initiative.
Innovation Solution
The use of a trained machine learning model to compare graphical context data between nodes in source and destination database schema graphs, enabling automated mapping and continuous improvement through AI-driven feedback loops.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual intervention is used for schema mapping, then mapping precision and accuracy can be maintained, but the process becomes laborious and time-consuming
Solution Approach 1:
The system enables self-service mapping by using machine learning models to automatically perform schema mapping tasks that previously required manual intervention. The AI model independently analyzes source and destination schemas, generates mapping relationships, and validates mappings without human involvement, thereby eliminating the trade-off between precision and time consumption.
Solution Approach 2:
The patent replaces the mechanical manual mapping process with an automated machine learning-based system. The machine learning model substitutes human expertise and manual operations, enabling rapid automated schema mapping that maintains high precision while significantly reducing time requirements.
2Productivity
If automated mapping scripts are used, then mapping speed increases, but the scripts require frequent reprogramming for different database systems
Solution Approach 1:
The system handles different database schemas by changing the input parameters (source and destination schema definitions) rather than reprogramming the core mapping logic. The machine learning model adapts to different database systems through parameter variation, maintaining consistent mapping behavior across diverse schemas without requiring script modifications.
Solution Approach 2:
The machine learning-based mapping system provides universality by being able to handle multiple different database schema types through a single unified model. The model generalizes mapping patterns across different database systems, eliminating the need for separate scripts for each database type and reducing overall system complexity.
3Reliability
If exhaustive manual validation is performed, then mapping accuracy is ensured, but the process requires hundreds of hours of SME time
Solution Approach 1:
The system incorporates feedback mechanisms where the machine learning model continuously refines its mappings based on validation results. The feedback loop allows the model to learn from validated mappings and improve its accuracy over time, maintaining high reliability while reducing the time required for initial validation compared to exhaustive manual checking.
Data Source
AI summary
Systems, methods, and computer program products provide for mapping a source database schema to a destination database schema. Initially, a source database schema graph and a destination database schema graph are generated, each graph having a plurality of nodes, each node corresponding to a data object in the source database schema or the destination database schema. Graphical context data is then generated for the source database schema graph nodes and the destination database schema graph nodes. Using a trained artificial intelligence model, the graphical context data for a selected source database schema graph node is compared to the graphical context data for a selected destination database schema graph node, and a mapping between the selected source database schema graph node and the selected destination database schema graph node is labeled where the artificial intelligence model determines that the selected nodes are sufficiently similar.