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 dynamic extensibility, and not supporting advanced queries or data set comparison/merge capabilities.
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, decoupling the application from the physical database structure, and enabling object-oriented access to data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional ORM tools use static modeling with compile-time data model definition, then the system structure is simple and reliable, but the system lacks runtime extensibility and adaptability
Solution Approach 1:
The system separates the data model definition from the physical database schema by introducing a logical data model layer. This logical model can be extended at runtime independently of the underlying database structure, allowing adaptability without requiring complex schema modifications. The segmentation enables the application layer to evolve separately from the database layer.
Solution Approach 2:
The patent introduces an intermediary layer (logical data model) between the application and the physical database. This intermediary allows runtime extensibility by capturing new attributes in the logical model before they are persisted to the database, eliminating the need for direct schema modifications and reducing system complexity.
2Adaptability or versatility
If the data model schema is modified to include new attributes, then the system gains adaptability, but recompilation of the database schema is required which reduces productivity
Solution Approach 1:
The system implements dynamic schema evolution by allowing the logical data model to be extended at runtime without recompilation. New attributes can be added to the logical model and immediately captured by the data collector, enabling continuous adaptation without deployment interruptions.
Solution Approach 2:
The system performs preliminary actions by defining the logical data model structure in advance, which serves as a template for future data collection. When new attributes are needed, they can be added to the existing logical model framework without requiring database schema changes, maintaining productivity while enabling extensibility.
3Adaptability or versatility
If traditional ORM tools require complete data model knowledge at compile-time, then the system has high reliability and stability, but it lacks flexibility for dynamic data requirements
Solution Approach 1:
The system creates a logical copy of the data model that mirrors the physical database structure but exists independently. This logical model can be extended at runtime to capture new attributes before they are persisted to the database, maintaining system stability while enabling dynamic data modeling capabilities.
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.