Dynamic Relational Data Models for Scalable Query and 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, especially when integrating external data objects.
Innovation Solution
The introduction of dynamic data models that process relationships between data objects as dynamic associations based on attributes, using relational awareness scores and absorption scores to record and absorb relationships, enabling more significant relationships to be distinguished and integrated effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional graph-based databases are used to store data relationships, then data structure stability is maintained, but data retrieval efficiency and relational awareness deteriorate
Solution Approach 1:
The patent implements dynamic data models where relationships between data objects are not statically defined but dynamically determined through machine learning algorithms. The system continuously learns and adapts relationship patterns from operational data, allowing the data model to evolve over time while maintaining structural integrity through version control and incremental updates.
Solution Approach 2:
The system changes the parameters of relationship representation from fixed schema definitions to flexible, learned embeddings. By transforming relationship attributes into continuous vector spaces and using absorption scores to weight different relationship types, the system achieves both stability through standardized parameter interfaces and efficiency through optimized parameter values derived from data patterns.
2Stability of the object's composition
If rigid semantic structures are used in graph databases, then data model consistency is maintained, but adaptability to external data objects and scalability deteriorate
Solution Approach 1:
The patent creates a universal data model framework that can handle multiple types of data objects and relationships through a common absorption score mechanism. The system uses type-agnostic embedding layers and unified relationship processing pipelines that work consistently across diverse data sources, enabling the same infrastructure to serve both internal and external data integration needs.
Solution Approach 2:
The system segments the data integration process into modular components: data ingestion modules, relationship learning modules, absorption score calculation modules, and visualization modules. Each segment handles specific aspects of data processing independently, allowing the system to maintain consistency in the core data model while adapting to various external data formats and structures through specialized interface layers.
3Loss of information
If comprehensive relationship tracking is implemented in traditional databases, then relational awareness is improved, but system complexity and computational overhead increase
Solution Approach 1:
The patent replaces mechanical relationship tracking mechanisms (explicit graph edges and relationship tables) with neural network-based relationship inference. The system uses learned embeddings and attention mechanisms to implicitly capture relationships, eliminating the need for complex explicit relationship storage while maintaining comprehensive relational awareness through the model's internal representations.
Solution Approach 2:
The system introduces absorption scores as an intermediary mechanism that mediates between raw relationship data and the data model. These scores act as a compressed representation of relationship strength and relevance, allowing the system to track comprehensive relationships without storing every detail explicitly, thereby reducing complexity while preserving relational information.
4Ease of manufacture
If static data models are used, then implementation simplicity is maintained, but ability to dynamically adapt to operational environments and user needs deteriorates
Solution Approach 1:
The system performs preliminary relationship learning and absorption score calculation during data ingestion and preprocessing phases. By pre-computing relationship embeddings and absorption scores before queries are executed, the system maintains simple query processing logic while incorporating dynamic adaptation capabilities through pre-learned patterns that capture operational environment characteristics.
Solution Approach 2:
The patent implements feedback loops where user interactions and query patterns are continuously monitored and used to retrain the relationship learning models. The system adjusts absorption scores and relationship weights based on actual usage patterns, enabling dynamic adaptation to changing operational environments while maintaining a relatively simple static schema structure that is periodically updated through automated retraining 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.