Cell-Based Visualization with Significance Indicators
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Solution Overview
Problem
Traditional data visualization techniques struggle to effectively represent large datasets with numerous categorical attributes, resulting in cluttered visualizations that obscure significant relationships and make it difficult for users to identify important events.
Innovation Solution
A cell-based visualization approach that uses significance visual indicators, such as rings or color coding, to highlight groups of cells based on statistical significance, allowing users to distinguish between more and less significant events by plotting cells representing events at different positions and sorting them according to a third attribute, thereby reducing occlusion and enhancing clarity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional data visualization techniques are used to display all categorical values of attributes, then complete information is presented, but the visualization becomes cluttered and difficult to understand
Solution Approach 1:
The patent segments the data visualization by dividing cells into different regions based on statistical significance. Significant cells are separated from non-significant cells through spatial segmentation, allowing users to focus on important data while maintaining access to complete information. This resolves the contradiction by organizing information hierarchically rather than presenting all data uniformly.
Solution Approach 2:
The patent applies local quality by assigning different visual properties to different regions of the visualization. Significant cells receive distinctive visual treatment (such as being placed in separate regions or highlighted), while non-significant cells are presented differently. This allows the visualization to convey both complete information and clear distinction of important elements simultaneously.
2Quantity of substance
If a large number of data records with numerous categorical values are visualized, then comprehensive data coverage is achieved, but the result is a cluttered visualization where users have difficulty understanding the information
Solution Approach 1:
The patent introduces an additional visual dimension by spatially separating cells based on statistical significance. Instead of representing all data in a single flat space, the visualization adds a significance dimension through regional separation. This allows comprehensive data coverage while reducing perceived complexity by organizing information along the significance dimension.
3Loss of information
If all events are displayed without differentiation, then complete event coverage is provided, but significant events cannot be distinguished from less significant ones
Solution Approach 1:
The patent uses visual differentiation through color or shading changes to distinguish significant events from less significant ones. Cells representing significant events are displayed with distinct visual properties compared to non-significant cells, enabling users to quickly identify important events while maintaining complete event coverage in the visualization.
Data Source
AI summary
Using a contingency calculation based on a number of events sharing a collection of values of plural attributes, a discriminative metric is computed representing a statistical significance of the events that share the collection of values of the plural attributes. A visualization is generated that includes cells representing respective events, the visualization including a region containing a subset of the cells corresponding to the collection of values of the plural attributes, and the visualization including a significance visual indicator associated with the region to indicate the statistical significance of the events sharing the collection of values of the plural attributes.