Automated Schema Matching for Accurate Data Conversion

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Solution Overview

Problem

System migration from one information management system to another is labor-intensive, slow, costly, and error-prone due to differences in data schemas, requiring extensive domain knowledge and manual data transformation.

Innovation Solution

A data conversion engine that utilizes a data dictionary, semantic models, and machine learning techniques to automate data matching and transformation, including classification, clustering, and similarity measurement between data fields and tables, with context-aware matching and report generation for system migration.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual data transformation is used to migrate systems, then domain knowledge can be applied to ensure accuracy, but the process becomes labor-intensive and slow

Engineering Contradiction:

Improvedata conversion accuracyVSAvoiddata conversion speed

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The system performs self-service through automated schema matching and data transformation. The source and target system schemas are automatically compared, and the data conversion engine generates transformation rules without requiring manual intervention for each data element, thus maintaining accuracy while improving speed

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

Manual mechanical data transformation processes are replaced with an automated data conversion engine that uses computational algorithms to match schemas and transform data. This substitution of manual mechanical work with automated computational processes resolves the contradiction between accuracy and speed

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Adaptability or versatility

If manual data transformation is used to handle schema differences, then complex data mappings can be managed, but the process becomes costly and error-prone

Engineering Contradiction:

Improveschema compatibilityVSAvoidconversion error rate

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The data conversion engine incorporates feedback mechanisms where conversion results are validated against target schema requirements. If conversion errors are detected, the system automatically adjusts transformation rules and re-attempts conversion, thereby reducing error rates while maintaining adaptability to different schemas

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system performs preliminary schema analysis and compatibility checking before actual data conversion. By pre-identifying schema differences and preparing transformation rules in advance, the system reduces errors during the actual conversion process while maintaining versatility across different data schemas

Inventive Principle:

Principle #10Preliminary action

3Ease of manufacture

If extensive manual effort is applied to data conversion, then complex transformations can be achieved, but the migration process becomes time-consuming

Engineering Contradiction:

Improvetransformation complexityVSAvoidmigration duration

Core Design Contradiction:

Ease of manufactureVSLoss of time

Solution Approach 1:

The data conversion process is segmented into distinct automated stages: schema analysis, compatibility assessment, transformation rule generation, and validation. This segmentation allows complex transformations to be handled systematically by the automated engine, reducing the time required compared to manual processing while maintaining transformation capability

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS20260017274A1Systems and methods for data conversion

Publication Date: 2026.01.15 ZENGINES INC

AI summary

Systems and methods for data conversion from a source system to a target system. In some embodiments, the source system may comprise a plurality of source data structures, and the target system may comprise a target data structure. For each source data structure, a respective conversion score may be computed between the source data structure and the target data structure. The target data structure may be matched, based on the conversion scores, to a source data structure of the plurality of source data structures.