Database Data Consistency Judgment via Property Definitions
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Solution Overview
Problem
Existing database integration techniques face challenges in verifying data integrity beyond data type conflicts, particularly in detecting semantic errors during data mapping, such as columns with the same name but different meanings, inconsistent units, and compilation range inconsistencies.
Innovation Solution
A computer-implemented method generates data properties from multiple databases, associates these properties with a data model to create data property definitions, and compares these definitions to judge data consistency, allowing for dynamic modification of property definitions to improve error detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data integration is performed between multiple databases, then data completeness and information coverage are improved, but data consistency and reliability deteriorate due to semantic errors in data mapping
Solution Approach 1:
The patent applies preliminary action by generating data property definitions from source databases before the actual data integration process. These definitions capture semantic information about data properties (such as units, formats, and constraints) in advance, allowing the system to verify and correct mapping errors before they propagate through the integrated database, thus maintaining data consistency while achieving comprehensive data integration
Solution Approach 2:
The patent implements feedback mechanisms by comparing data property definitions from multiple source databases and identifying inconsistencies in the mapping process. When semantic errors are detected (such as mismatched units or incompatible data formats), the system provides feedback to correct the mapping relationships, ensuring that data integration maintains reliability while achieving completeness
2Adaptability or versatility
If data mapping is performed between databases with different structures, then adaptability and integration capability are improved, but accuracy of data consistency verification deteriorates due to semantic errors
Solution Approach 1:
The patent applies parameter changes by transforming diverse data structures from different databases into a unified data property definition framework. By extracting and standardizing semantic parameters (such as data types, units, constraints, and relationships) from various source databases, the system enables accurate comparison and verification of data consistency across different structures, thereby maintaining high error detection accuracy while achieving versatile integration
3Ease of manufacture
If static data property definitions are used for data consistency verification, then implementation simplicity is maintained, but accuracy of error detection deteriorates due to inability to adapt to changing data characteristics
Solution Approach 1:
The patent implements dynamics by making data property definitions adaptable and modifiable based on actual data characteristics. The system allows data property definitions to be generated dynamically from source databases and updated when data characteristics change, enabling the verification mechanism to maintain high accuracy across different scenarios while preserving implementation simplicity through automated generation and management of these dynamic definitions
Data Source
AI summary
The present invention may be a method, a computer system, and a computer program product. An embodiment of the present invention provides a method for judging data consistency in a database. In one embodiment, the method comprises the following: generating a property of data obtained from a first database; associating the property with an attribute of a data model to generate a data property definition; judging whether data obtained from a second database satisfies the data property definition or not; and outputting a result of the judgment. In another embodiment, the method comprises the following: generating a property of data obtained from a database; associating the property with an attribute of a data model to generate a data property definition; judging whether data which is stored in the database satisfies the data property definition or not; and outputting a result of the judgment.