Automated Transformation Rule Generation for Database Schema Alignment
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Solution Overview
Problem
The manual process of generating and validating transformation rules for aligning disparate data schemas in database systems is labor-intensive, costly, and prone to errors, especially for large data models, due to the lack of automated or semi-automated intelligent support.
Innovation Solution
A system that generates and evaluates transformation rules automatically or semi-automatically, using a transformation rule engine that identifies mappings between source and target databases, applies identified functions, and provides user interface support for rule generation and evaluation, thereby reducing the need for manual intervention and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual rule building by domain experts is used to match data silos, then transformation rules can be created with high accuracy, but the process becomes very labor-intensive and costly
Solution Approach 1:
The system enables self-service automated transformation rule generation by analyzing source and target data models, identifying mappings, and generating transformation rules without requiring manual intervention from domain experts, thereby maintaining accuracy while dramatically improving productivity
Solution Approach 2:
The patent replaces the manual mechanical process of rule building by domain experts with an automated computational system that uses algorithms to analyze data models, identify mappings, and generate transformation rules, substituting human labor with automated processing
2Reliability
If manual rule building processes are used for large data models, then transformation rules can be created, but the process becomes excessively time-consuming and expensive
Solution Approach 1:
The system performs preliminary automated analysis of source and target data models to identify mappings and generate transformation rules before any manual review or execution, preparing the transformation logic in advance and reducing the time required for rule development
Solution Approach 2:
The automated system performs self-service rule generation and validation, eliminating the need for time-consuming manual rule building while maintaining reliability through automated validation against the data models
3Productivity
If automated transformation rule generation is implemented, then productivity and efficiency are improved, but the complexity of the system increases
Solution Approach 1:
The system implements a universal automated transformation rule generation platform that handles multiple data models, mapping scenarios, and transformation types through a single integrated system, improving productivity while managing complexity through consolidation rather than proliferation of separate tools
Data Source
AI summary
Transformation rule generation and validation functionality is provided herein. Transformation rules can be generated for one or more mappings in an alignment between a source database and a target database. The transformation rules can transform instance data from the source data model to a form matching the target data model. One or more transformation rules can be generated for a mapping between fields in a source database and a field in a target database. The transformation rules can be generated based on one or more source fields and a target field of a mapping, and one or more identified functions. Evaluating the transformation rules can include generating test data based on the transformation rules applied to instance data from the source database. The test data can be evaluated against instance data from the target database. The transformation rules and the evaluation results can be provided in a user interface.