Dynamic Validation Rules for Non-Homogeneous Data Assets
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Solution Overview
Problem
Existing data exchanges struggle with validating non-homogeneous assets that have non-standardized data descriptions, as conventional systems lack the ability to apply validation rules effectively across diverse and non-uniform data assets, leading to challenges in data quality and accuracy.
Innovation Solution
The system generates dynamically tailored validation rules comprising a standardized and non-standardized portion for each asset type, allowing validation of both standardized and custom schemas, and executes operations using command strings that accommodate both types of schemas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional validation systems are used, then validation rules can be applied to homogeneous data, but they fail to validate non-homogeneous assets with non-standardized data descriptions
Solution Approach 1:
The validation rule is segmented into a standardized portion that applies to all asset types and a non-standardized portion that is customized for each specific asset type. This allows the system to maintain a unified validation framework while accommodating diverse data descriptions through selective application of asset-type-specific validation rules.
Solution Approach 2:
Different portions of the validation rule have different qualities: the standardized portion provides universal applicability, while the non-standardized portion provides tailored validation for each asset type's specific data description requirements. This local customization enables accurate validation across heterogeneous assets.
2Adaptability or versatility
If data descriptions are transformed to accommodate different schemas, then data can be shared between programs, but the data descriptions become non-uniform and non-standardized
Solution Approach 1:
The validation rule is segmented into standardized and non-standardized portions, allowing the system to accept transformed, non-uniform data descriptions while maintaining a structured validation approach. The standardized portion handles commonalities across data types, while the non-standardized portion manages schema-specific variations.
Solution Approach 2:
The system dynamically adjusts validation parameters based on the asset type and its specific data description schema. By changing validation parameters according to the non-standardized schema of each asset type, the system can accommodate transformed data descriptions without requiring uniformity.
3Manufacturing precision
If validation rules are developed for original or target schemas, then validation can be performed on standardized data, but they become ineffective or inaccurate for non-standardized schemas
Solution Approach 1:
The validation rule is divided into a standardized portion that maintains accuracy for standard schemas and a non-standardized portion that adapts to asset-type-specific schemas. This segmentation ensures that validation rules remain accurate across different schema types without requiring complete rule redesign for each schema.
Solution Approach 2:
The validation rule dynamically adapts its non-standardized portion based on the specific asset type and schema encountered. This dynamic adjustment allows the same validation framework to maintain high accuracy across diverse schemas by selecting and applying appropriate asset-type-specific validation rules.
Data Source
AI summary
Systems and methods use dynamically generated validation rules. These validation rules comprise a first validation rule portion that is generated using a standardized validation process (e.g., corresponding to a standardized schema) and a second validation rule portion that is generated using a validation process selected based on a non-standardized schema that is specific to a respective asset type of the plurality of respective asset types.