Core Data Model Mapping for Automated Schema Integration
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Solution Overview
Problem
Integrating complex data models across enterprises or migrating between different software applications is challenging due to the complexity of schemas, leading to data silos, inconsistencies, and errors in manual schema matching processes, which become outdated quickly.
Innovation Solution
A core data model is created with elements that represent semantic concepts, allowing domain models to be mapped to it, facilitating comparison and integration by enforcing relations to core data model elements, and using model validators and adapters for deployment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual schema matching is performed to integrate complex data models, then data integration can be achieved, but the process becomes extraordinarily time-consuming and error-prone
Solution Approach 1:
The patent replaces the manual mechanical process of schema matching with an automated computer-based system that uses algorithms to match domain data model elements to core data model elements, thereby eliminating the time-consuming manual effort while maintaining or improving matching accuracy
Solution Approach 2:
The system enables self-service automated schema matching where the computer automatically performs the matching process without requiring manual intervention, allowing the system to service itself in terms of data model integration tasks
2Reliability
If manual schema matching is performed, then initial data model integration can be achieved, but mappings quickly become outdated when data models change
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously monitors changes in domain and core data models and automatically updates the mappings accordingly, ensuring that mappings remain current and accurate as schemas evolve over time
Solution Approach 2:
The system transitions from static manual mappings to dynamic automated mappings that can adapt and change in response to schema modifications, allowing the mapping relationships to evolve naturally as data models are updated
3Loss of information
If conceptual format output is produced from schema matching, then mapping information can be captured, but additional conversion effort is required for productive use
Solution Approach 1:
The patent replaces the manual conversion process from conceptual format to implementable format with an automated computer-based conversion system that directly produces deployment-ready artifacts, eliminating the intermediate manual conversion step and associated errors
4Reliability
If thousands of schema elements exist in both schemas, then comprehensive data integration is possible, but the matching process becomes extraordinarily complex
Solution Approach 1:
The patent applies segmentation by breaking down the complex task of matching thousands of schema elements into smaller, manageable units that can be processed individually by the computer system, reducing the perceived and actual complexity of the overall matching process
Data Source
AI summary
Techniques and solutions are provided for improved data modelling, including techniques that facilitate data model comparison, integration, or deployment. A core data model is created that has a plurality of elements, which can represent semantic concepts, including relationships between semantic concepts. A plurality of domain models are mapped to core data model elements. In particular, when a domain model element is to be created, the element is mapped to a core data model element. When a relation is to be established between two domain model elements, the relation is mapped to a relation type represented in the core data model. Mapping to a core data model can be enforced as part of domain model creation, allowing different domain models to be compared, since domain elements in different domain models that represent a common semantic will be mapped to the same core data model element.