Data Context Map for Multi-Dimensional Relationship Visualization
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Solution Overview
Problem
Current visualization methods for data matrices fail to effectively present complex relationships between data samples and attributes, limiting users' understanding by either focusing on spatial preservation of sample relations or attribute relations, but not both, and are difficult to interpret for non-experts.
Innovation Solution
A framework that generates a data context map by fusing similarity matrices of data samples and attributes, creating a composite distance matrix to visualize each data sample's distance relative to attributes, allowing users to intuitively grasp multi-dimensional relationships in a single context map.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional visualization methods focus on spatial preservation of sample relations, then sample similarity is maintained, but attribute relationships become difficult to interpret
Solution Approach 1:
The patent merges sample similarity information and attribute relationship information into a single unified visualization framework. The system simultaneously displays samples and attributes in the same spatial context, allowing users to see both sample-cluster relationships and attribute-correlation relationships in one view, thereby resolving the contradiction between preserving sample similarity and enabling attribute interpretation.
Solution Approach 2:
The patent introduces an additional visualization dimension by representing both samples and attributes as spatial entities with defined positions and distances. By mapping abstract similarity and correlation metrics into spatial distances in a unified coordinate system, the system enables intuitive geometric interpretation of both sample relationships and attribute relationships simultaneously.
2Measurement precision
If traditional visualization methods focus on attribute relations, then attribute correlations are preserved, but sample relationships become difficult to understand
Solution Approach 1:
The system combines attribute correlation preservation with sample relationship visualization by placing both samples and attributes in a unified spatial framework. Attribute correlations are preserved through the spatial arrangement of attribute nodes, while sample relationships are maintained through the positioning of sample points relative to both other samples and attributes, enabling simultaneous understanding of both relationship types.
Solution Approach 2:
The patent adds a dual-reference dimension to the visualization, where samples can be understood in relation to both other samples and to attributes simultaneously. This is achieved by defining sample positions based on fused similarity matrices that incorporate both sample-sample and sample-attribute relationships, allowing intuitive geometric interpretation of both types of relationships in the same space.
3Loss of information
If multiple separate visualizations are used to show sample and attribute relationships, then comprehensive information is provided, but user understanding becomes complex and time-consuming
Solution Approach 1:
The patent merges multiple separate visualization functions into a single unified data context map that simultaneously displays sample relationships, attribute relationships, and their interactions. This unified view eliminates the need for users to switch between multiple separate visualizations, providing comprehensive information in a single intuitive interface that reduces interpretation time and cognitive load.
4Loss of information
If detailed multi-dimensional data is presented, then complete relationships are shown, but intuitiveness and ease of interpretation decrease
Solution Approach 1:
The patent transforms complex multi-dimensional similarity and correlation data into intuitive spatial distances and geometric relationships in a unified 2D visualization space. By mapping high-dimensional relationships into spatial positions, distances, and angular relationships that humans can naturally perceive, the system maintains complete multi-dimensional information while dramatically improving intuitiveness and ease of interpretation.
Data Source
AI summary
The system, method, and computer readable medium described herein provide improvements in the ways that user interfaces present multi-dimensional relationships between data samples to a user. The disclosed user interface framework provides users with a visualization of the complex relationships between data samples having multi-dimensional attributes which allows the users to quickly and intuitively grasp the relationships between data samples for a large number of attributes at a glance and in a single data map visualization.