Visual Data Object Dependency Tracing in Relational Models
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Solution Overview
Problem
Current data visualization tools inadequately represent object dependencies in data models, particularly in big data contexts, failing to effectively visualize and manage relationships between objects and their parameters, and do not allow for real-time visualization of changes in these dependencies.
Innovation Solution
A method and system for visual data object dependency tracing, which loads a data model into memory, identifies and correlates parameters with objects, creates a relational table, and generates a visualization of these correlations, allowing for interactive selection and modification of object definitions, and dynamically updates the visualization in response to changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If current data visualization tools are used to represent object dependencies, then basic visualization capability is provided, but the visualization fails to effectively represent complex relationships and parameter correlations
Solution Approach 1:
The patent segments the data model into distinct objects and parameters, visualizing them as separate nodes in a graph. Each object is represented as a node, and each parameter is represented as a connected element, allowing complex relationships to be broken down into manageable visual units that can be systematically processed and displayed.
Solution Approach 2:
The patent introduces multiple dimensions to the visualization by creating a hierarchical structure with objects at one level and parameters at another level, connected through relationships. This multi-level dimensional arrangement allows the system to represent complex dependency relationships that cannot be captured in simple two-dimensional diagrams, thereby reducing information loss while managing visual complexity.
2Loss of information
If data models with multiple objects and parameters are visualized, then comprehensive relationship representation is achieved, but the system complexity increases
Solution Approach 1:
The patent introduces a parameter correlation table as an intermediary structure that mediates between objects and parameters. This table systematically stores and manages the relationships between parameters and objects, allowing the system to handle complex correlations without directly managing the complexity in the visualization layer. The intermediary structure organizes information in a way that reduces both information loss and system complexity.
3Loss of time
If real-time visualization updates are implemented, then dynamic change detection is improved, but processing time and computational resources increase
Solution Approach 1:
The patent pre-processes the data model by extracting objects, parameters, and their relationships into structured representations before visualization is needed. The parameter correlation table is pre-built and maintained, allowing rapid updates when changes occur. This preliminary organization of data structures enables fast change detection without requiring computationally intensive real-time processing, thus reducing both time loss and computational resource requirements.
Data Source
AI summary
Embodiments of the present disclosure provide a method and system for visual data object dependency tracing. In an embodiment of the disclosure, a method for visual data object dependency tracing includes issuing display instructions to display a visualization of a relational database table in a display in communication with the data processing hardware, the relational database table representing a data model for data of a database, the visualization of the relational database table including a network of nodes coupled, each node coupled to at least one other node by a connector and each node representing a corresponding object of the data model, each connector representing a corresponding correlation between objects.