Data Model Dualization for SQL NoSQL Coexistence
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Solution Overview
Problem
Current data models, such as relational and non-relational databases, face challenges in scalability and consistency, with SQL databases prioritizing consistency over scalability and NoSQL databases sacrificing consistency for scalability, leading to a need for a more harmonious coexistence and transformation between these models.
Innovation Solution
The concept of data model dualization, where a first data model is transformed into a mathematical dual, allowing for the generation of a non-relational data model from a relational model, demonstrating that NoSQL data models are a dual of SQL data models, and vice versa, using a transformation component that generates dual entities and relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If SQL databases prioritize consistency, then data consistency is improved, but scalability deteriorates
Solution Approach 1:
The invention segments the data model into dual components: a relational data model for consistent querying and a dual non-relational data model for scalable storage. This segmentation allows each model to operate in its optimal regime while maintaining consistency through the mathematical duality relationship between them.
Solution Approach 2:
The patent creates a universal data management system that performs multiple functions: it can query data using SQL for consistency-critical operations and simultaneously manage data storage using the dual non-relational model for scalability. The transformation component enables the system to switch between these functions based on operational needs.
2Productivity
If NoSQL databases prioritize scalability, then scalability is improved, but consistency deteriorates
Solution Approach 1:
The invention introduces a transformation component as an intermediary that maintains the mathematical duality relationship between the relational and non-relational data models. This intermediary ensures that operations on one model are correctly reflected in the other, thereby maintaining consistency while enabling scalability.
Solution Approach 2:
The patent changes the fundamental parameter of data model representation by creating a mathematical dual of the original relational model. This parameter change transforms the data structure from a consistency-oriented relational model to a scalability-oriented non-relational model, while the duality relationship preserves the semantic meaning and consistency guarantees.
3Adaptability or versatility
If data models are transformed between relational and non-relational, then adaptability is improved, but system complexity increases
Solution Approach 1:
The invention replaces complex mechanical transformation processes with a mathematical duality framework. Instead of implementing intricate data conversion logic, the system uses the mathematical relationship between dual spaces to automatically transform data models, significantly reducing the complexity of the transformation mechanism.
Data Source
AI summary
A data model can be generated by dualizing another data model. In other words, a first data model can be transformed into a second data model, wherein the second data model is a mathematical dual of the first data model. For example, a non-relational data model can be generated by dualizing a relational data model.