Dynamic Relational Data Modeling for Scalable Query Visualization
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Solution Overview
Problem
Traditional graph-based databases are inefficient and ineffective in data retrieval and visualization due to their rigid semantic structure, which limits relational awareness and scalability, failing to effectively model dynamic relationships between data objects.
Innovation Solution
The introduction of dynamic relational awareness concepts that record relationships as absorbed by data objects based on their attributes, using absorption scores to indicate relational significance, enabling efficient data retrieval and visualization, and integrating external data objects into a relationally aware data model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional graph-based databases are used, then data storage structure is established, but data retrieval efficiency deteriorates and visualization effectiveness worsens due to rigid semantic structure
Solution Approach 1:
The patent applies dynamics by transforming the static graph-based database structure into a dynamic absorption-based model where relationships are not fixed but continuously absorbed and updated based on operational environments. The absorption scores allow the data model to adapt dynamically, improving retrieval efficiency while maintaining flexibility.
Solution Approach 2:
The patent changes parameters by introducing absorption scores as new parameters that quantify relational significance. These parameters replace the rigid binary relationships of traditional graphs with continuous, adjustable values that can be modified based on operational context, thereby improving both efficiency and adaptability.
2Adaptability or versatility
If traditional graph-based databases are used, then basic data storage is achieved, but scalability deteriorates due to inability to effectively model dynamic relationships
Solution Approach 1:
The absorption-based data model enables dynamic relationship modeling where relationships between data objects can be absorbed, updated, and modified based on operational environments. This dynamic capability allows the system to scale effectively as new relationships and data objects are continuously integrated without rigid structural constraints.
3Productivity
If absorption scores are used to indicate relational significance, then data retrieval efficiency is improved, but computational complexity increases
Solution Approach 1:
The absorption-based model enables self-service by allowing the system to automatically compute and update absorption scores based on operational environments without requiring manual intervention. The machine learning component autonomously adjusts relevance parameters, improving retrieval efficiency while managing computational complexity through automated processes.
Solution Approach 2:
The system implements feedback by continuously monitoring operational environments and using absorption scores to adjust data retrieval and visualization. The machine learning component provides feedback loops that refine absorption scores based on usage patterns, improving efficiency while distributing computational load through iterative optimization.
4Ease of operation
If machine learning is used to set relevance parameters, then user-friendliness is improved, but system complexity increases
Solution Approach 1:
The machine learning component provides self-service by automatically learning and adjusting relevance parameters based on user interactions and operational environments. This eliminates the need for manual parameter tuning, improving user-friendliness while the system autonomously manages its own complexity through automated learning processes.
Data Source
AI summary
There is a need for more effective and efficient data modeling and/or data visualization solutions. This need can be addressed by, for example, solutions for performing data modeling and/or data visualization in an effective and efficient manner. In one example, solutions for generating a data model with dynamic relational awareness are disclosed. In another example, solutions for processing data retrieval queries using data models with dynamic relational awareness are disclosed. In yet another example, solutions for generating data visualizations using data models with dynamic relational awareness are disclosed. In a further example, solutions for integrating external data objects into data models with dynamic relational awareness are disclosed.