Table Data Visualization Using Spatial Region Clustering
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Solution Overview
Problem
Conventional table data representation in computing systems lacks intuitive visualization of row characteristics, making it difficult for users to quickly understand data patterns and relationships within large datasets.
Innovation Solution
A user interface is generated where rows are visually represented and positioned based on column values, allowing rows with matching characteristics to be clustered into regions, using attributes like color, size, and shape to convey further information, enabling intuitive knowledge of row characteristics through visual inspection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional table formats are used to display data, then data structure is maintained and easy to generate, but users cannot quickly understand data patterns and relationships
Solution Approach 1:
The patent transforms one-dimensional tabular data into two-dimensional spatial visualizations by mapping data points to positions on a display based on their attribute values. This dimensional transformation allows users to perceive data patterns, clusters, and relationships through spatial arrangement rather than linear scanning of table rows and columns.
Solution Approach 2:
The patent employs color encoding to represent different data attributes and their values. By assigning colors to different categories or value ranges, the visualization enables users to quickly identify patterns, outliers, and relationships in the data through visual perception rather than numerical comparison.
2Loss of information
If visual attributes like color and size are added to convey information, then data insight is improved, but system complexity increases
Solution Approach 1:
The patent uses a standardized set of visual attributes (color, size, shape) that serve multiple functions simultaneously. For example, color can indicate category membership while size can represent magnitude, allowing the same visual elements to convey multiple data dimensions without requiring separate visualization mechanisms for each attribute.
Solution Approach 2:
The patent dynamically adjusts visual parameters such as color hue, saturation, and object size based on the underlying data values. This parameter mapping transforms abstract numerical data into perceptible visual variations, enabling users to understand data characteristics through intuitive visual comparison rather than numerical analysis.
Data Source
AI summary
Generating a user interface visualizing a table data structure. A table data structure comprising a plurality of rows and a plurality of columns is accessed. A column to use for sorting each row into a different region of the user interface is identified. Region characteristics, usable for matching which row(s) is to be visualized within a corresponding region, are identified. Each region is associated with a different subset of row(s) that are to be visualized within the region. Each subset comprises rows having a value that matches the region characteristics of the associated region at an intersection between the column and the row in the table data structure. A visual representation of each row is displayed within the user interface. The display includes a visual arrangement of each visual representation into a corresponding region. Each visual representation is arranged within the region the row associated with the visual representation belongs.