Data Model Annotations for Automatic Conversion
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Solution Overview
Problem
Maintaining correspondence between conceptual and physical data models in database systems is problematic, leading to inaccuracies, complexity, and time-consuming manual processes, especially in denormalized databases where changes are difficult to propagate and errors can occur.
Innovation Solution
The use of annotations in conceptual data models to automatically generate physical data models or database artefacts, including inheritance, header/item, and one-to-one cardinality relationships, as well as adding versioning and data governance attributes, to simplify the transformation process and reduce errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual processes are used to maintain correspondence between conceptual and physical data models, then flexibility and control are maintained, but time consumption and error rates increase
Solution Approach 1:
The system enables automatic self-updating of physical data models by detecting changes in conceptual data models and propagating them through annotated entity types, relationships, and attributes. This eliminates manual intervention while maintaining accuracy through automated correspondence maintenance between conceptual and physical layers.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors changes in conceptual data models and automatically propagates these changes to physical data models. Annotations on entity types, relationships, and attributes serve as feedback rules that guide the automatic update process, ensuring correspondence is maintained without manual intervention.
2Productivity
If automated conversion is implemented using annotations, then productivity and accuracy improve, but system complexity increases
Solution Approach 1:
Annotations serve as intermediaries between conceptual and physical data models. These annotations on entity types, relationships, and attributes act as translation rules that enable automated conversion without requiring complex conversion logic. The annotations simplify the system by providing a standardized intermediary layer that manages the complexity of automated transformation.
Solution Approach 2:
The patent transforms data models by changing parameters through annotations that specify how entity types, relationships, and attributes should be converted. Annotations define parameters such as inheritance relationships, header/item relationships, and cardinality rules that automatically control the transformation process, enabling high productivity through parameter-driven conversion rather than complex procedural logic.
3Speed
If denormalized databases are used to improve query performance, then speed improves, but difficulty in propagating changes and maintaining accuracy increases
Solution Approach 1:
The system enables automatic self-updating of denormalized databases by detecting changes in conceptual data models and propagating them through annotated entity types and relationships. This eliminates the manual effort typically required to update denormalized structures, maintaining ease of operation despite the complexity of denormalization. The automated propagation ensures accuracy is maintained even in denormalized structures where changes are traditionally difficult to propagate.
Data Source
AI summary
Techniques and solutions are described for converting data models between formats, such as between a conceptual data model and a physical data model for a database system, or between a conceptual data model and artefacts to be implemented in the database system. The conceptual data model is annotated with annotations that allow the physical data model or database artefacts to be automatically generated from the conceptual data model. The annotations can reflect relationships between entity types in the physical data model, such as inheritance relationships, header/item relationships, or one-to-one cardinality relationships. Annotations can also indicate attributes that should be added to entity types in the conceptual data model, such as attributes for versioning or data governance, that may not be used in the conceptual data model. Annotations can be used to determine how entity types in the conceptual data model will be denormalized in the physical data model.