Integrated Data Modeling for Direct Cross-System Interoperability
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Solution Overview
Problem
Existing data modeling methods fail to integrate diverse data types and systems effectively, leading to incompatible databases and information silos, and existing integration methods like transformative data consolidation and data federation are inefficient or incomplete.
Innovation Solution
Enhanced data modeling methods are used to design integrated data systems, incorporating master reference data standards (MRDS) that support interoperability and data compatibility across all types of data content, including documents, spreadsheets, and videos, using integrated data models and data system access paths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If prior art data modeling methods are used to configure databases, then database configuration is achieved, but data structure incompatibility and data set incompatibility occur between databases
Solution Approach 1:
The patent applies universality by creating a unified data model framework that can represent multiple database types and non-database data content (documents, spreadsheets, videos) through common data entities and relationships. This allows the same modeling methods to configure diverse data systems while maintaining compatibility, resolving the contradiction between ease of configuration and data compatibility.
Solution Approach 2:
The patent changes the parameters of data modeling by introducing enhanced entity-relationship diagrams that include data content type parameters, integration parameters, and interoperability parameters. These parameter changes enable the model to adapt to different data types and systems while maintaining a consistent configuration approach, thus resolving the compatibility issue.
2Adaptability or versatility
If transformative data consolidation methods are used, then data compatibility is improved, but data integration efficiency decreases due to extraction, transformation, and loading processes
Solution Approach 1:
The patent applies preliminary action by pre-defining standardized data entities, relationships, and integration rules in the unified data model before actual data integration occurs. This preliminary structuring eliminates the need for complex extraction, transformation, and loading processes during integration, as data can be directly mapped to predefined structures, thus improving efficiency while maintaining compatibility.
Solution Approach 2:
The patent uses copying by creating virtual representations of data entities and relationships in the unified model without physically moving or transforming the actual data. This allows multiple data systems to be integrated through reference to common data models, eliminating the need for data transformation and loading processes, thereby improving integration efficiency.
3Productivity
If data federation methods are used, then data integration speed is improved, but data integration completeness deteriorates due to inability to properly combine incompatible data values
Solution Approach 1:
The patent applies local quality by allowing different data entities and relationships to have specialized properties and rules tailored to their specific data types and source systems, while still being part of a unified overall model. This enables fast integration through the unified framework while maintaining completeness through localized adaptations for incompatible data values.
4Adaptability or versatility
If enhanced data modeling methods are used to model all data content types, then data system integration capability is improved, but device complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the complex task of modeling all data content types into manageable components: common data entities, data content-specific entities, relationships, and integration rules. This segmentation allows the enhanced modeling methods to handle diverse data types through modular components, reducing overall system complexity while maintaining integration capability.
Data Source
AI summary
A method, an apparatus, and a system for configuring, designing, and/or implementing integrated data modeling methods for configuring direct dataset interoperability between multiple data systems based upon compliance with data integration standards. Data model federation and consolidation methods are detailed, along with peer entity relationships that link multiple data models. When the data models are data system instantiated, the resulting databases are also directly interoperable.