AI Data Transformation Pipeline for Heterogeneous Schema Integration
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Solution Overview
Problem
Existing data structure transformation methods require manual expert intervention and are not fully automated, leading to time-consuming and inefficient integration of diverse systems with different data structures and APIs.
Innovation Solution
A dynamic data structure transformation pipeline utilizing generative artificial intelligence (AI) to automate the transformation process, including specification, generation, integration, testing, and validation of transformation models, with feedback loops for continuous improvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual expert intervention is used for data structure transformation, then transformation accuracy is improved, but integration time and complexity increase
Solution Approach 1:
The system performs preliminary actions by automatically generating transformation models using AI/ML algorithms before manual expert review. The transformation models are pre-computed based on schema comparisons, data flow analysis, and mapping recommendations, reducing the time experts need to spend on actual transformation work while maintaining accuracy through automated validation.
Solution Approach 2:
The system enables self-service transformation by allowing automatic generation and validation of transformation models without requiring constant expert intervention. The AI-driven system can autonomously compare schemas, generate mapping rules, and validate transformations, freeing experts to focus only on complex cases that require human judgment.
2Manufacturing precision
If manual expert teams are used for transformation model integration, then transformation quality is improved, but process automation and efficiency worsen
Solution Approach 1:
The transformation process is segmented into distinct automated and manual phases. Automated components handle schema comparison, initial mapping generation, and basic validation, while manual expert review is reserved for complex mapping decisions and edge cases. This segmentation allows high automation in routine tasks while maintaining quality through targeted expert intervention.
Solution Approach 2:
An AI-driven intermediary system acts as a bridge between automated transformation processes and manual expert review. The intermediary automatically generates transformation models, validates them against quality criteria, and presents them to experts for approval or modification, thereby increasing overall process automation while preserving transformation quality through expert oversight.
3Measurement precision
If frequent exchanges between expert teams are conducted, then mapping accuracy is improved, but integration complexity and time consumption increase
Solution Approach 1:
The system implements automated feedback loops that continuously validate transformation mappings against test data and schema constraints. Mapping accuracy is improved through iterative automated testing and validation, reducing the need for frequent manual exchanges between expert teams. The feedback mechanism automatically identifies and corrects mapping errors, maintaining high accuracy with minimal human intervention.
Solution Approach 2:
The patent replaces the mechanical process of frequent manual exchanges between expert teams with an automated AI-driven system. The AI system automatically performs schema comparison, generates mapping rules, and validates transformations, substituting human-to-human communication with machine-to-machine automation while maintaining or improving mapping accuracy through consistent algorithmic application.
4Productivity
If standardized transformation processes are implemented, then integration efficiency is improved, but adaptability to heterogeneous systems worsens
Solution Approach 1:
The transformation process is designed to be dynamic rather than static. The AI-driven system automatically adapts transformation models based on the specific characteristics of source and target schemas, data flow patterns, and system requirements. This dynamic approach maintains integration efficiency through automated processes while achieving adaptability to heterogeneous systems by adjusting transformation rules to match specific system pairs.
Solution Approach 2:
The system achieves adaptability to heterogeneous systems by dynamically changing transformation parameters based on the specific systems being integrated. The AI-driven platform adjusts mapping rules, data type conversions, and validation criteria according to the characteristics of each source and target system, thereby maintaining integration efficiency through standardized processes while adapting to diverse system requirements through parameter modification.
Data Source
AI summary
A computerized method, system, and computer program providing a dynamic data structure transformation pipeline are presented. This is achieved by receiving transformation information relating to a source data structure and a target data structure, generating a transformation model for transforming data items from the source data structure to the target data structure based on the received transformation information, integrating, in a transaction environment, an automatic data structure transformation based on the transformation model for transforming data items from the source data structure into the target data structure, testing the automatic data structure transformation elementwise for elements included in the data items, validating the automatic data structure transformation in a sandbox of the transaction environment, and, in response to unexpected answers and/or errors during testing and/or validating of the automatic data structure transformation were received, enriching the transformation information, and repeating at least a part of the process.