Logical Data Model for Runtime Schema Extensibility
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Solution Overview
Problem
Traditional Object-Relational Mapping (ORM) tools are limited by static modeling, requiring complete data model knowledge at compile-time, lacking advanced query capabilities, and not providing data set comparison or change history, which restricts their runtime extensibility and flexibility in managing complex database relationships.
Innovation Solution
A logical data model that dynamically extends schema at runtime, allowing for dynamic data modeling, schema management, data set comparison, and merge capabilities, along with historical tracking, by separating the interface from implementation and using semantic relationships to manage data, enabling efficient storage and querying.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional ORM tools use static modeling with compile-time data model knowledge, then the system structure is simple and easy to implement, but the runtime extensibility and flexibility are limited
Solution Approach 1:
The patent implements dynamic data modeling by allowing the logical data model to be modified at runtime through metadata updates. The system transitions from static compile-time modeling to dynamic runtime modeling, enabling addition, modification, and deletion of entities and attributes without recompilation. This is achieved through a metadata-driven architecture where the logical data model can adapt to changing requirements during execution.
Solution Approach 2:
The patent separates the logical data model from the physical database schema through an intermediate metadata layer. This segmentation allows the logical model to be independently modified without affecting the physical database structure. The metadata acts as a buffer between the application layer and the database layer, enabling runtime extensibility while maintaining system stability.
2Adaptability or versatility
If the logical data model is dynamically extended at runtime, then the adaptability and flexibility improve, but the complexity of managing schema evolution and metadata increases
Solution Approach 1:
The patent introduces a metadata layer as an intermediary between the logical data model and the physical database schema. This metadata layer manages schema evolution by capturing changes in the logical model and translating them into appropriate database operations. The metadata acts as a mediator that handles the complexity of schema management, allowing runtime extensibility without directly managing database schema changes.
Solution Approach 2:
The patent performs preliminary actions by maintaining a logical data model that can be fully defined and validated before runtime operations. The metadata is prepared and structured in advance, allowing the system to handle schema changes through pre-defined mechanisms rather than ad-hoc modifications during execution.
3Loss of information
If traditional ORM tools require complete data model knowledge at compile-time, then the modeling process is straightforward, but advanced query capabilities and data set comparison are limited
Solution Approach 1:
The patent implements feedback mechanisms by tracking changes to the logical data model and maintaining historical versions. The system monitors modifications to entities and attributes, storing change history that can be queried and analyzed. This feedback loop enables data set comparison and evolution tracking without requiring complex manual processes.
Solution Approach 2:
The patent adds a temporal dimension to the data model by maintaining historical versions and change history. This allows queries to access not only current state data but also historical states and evolution patterns. The additional dimension of time enables advanced query capabilities for data set comparison and analysis without increasing base model complexity.
4Ease of operation
If the system allows runtime modification of the logical data model, then flexibility improves, but the risk of schema inconsistencies and data integrity issues increases
Solution Approach 1:
The patent implements self-service mechanisms through automated validation and consistency checking of the logical data model. The system automatically verifies that runtime modifications to the logical model maintain data integrity constraints and schema consistency. This self-validation reduces the risk of manual errors while preserving runtime flexibility.
Solution Approach 2:
The patent applies beforehand cushioning by implementing validation rules and constraints that prevent invalid schema modifications before they can cause data integrity issues. The system checks proposed changes against predefined integrity constraints and rejects modifications that would compromise data reliability, thus cushioning against potential errors before they occur.
Data Source
AI summary
Systems and methods for using a logical data model to at least partially address the deficiencies with existing ORM solutions are provided. In certain embodiments, the logical data model includes a layer that hides the underlying physical layout of tables in a database. The logical data model can act as a data management component that supports any subset of the following: 1) dynamic data modeling and schema management; 2) data set comparison and merge with historical tracking; and/or 3) data query.