Multi-dimensional Data Visualization via Segmented Heat Maps
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Solution Overview
Problem
The challenge lies in effectively analyzing and understanding multi-dimensional data, particularly in fields like science, engineering, and business, where large volumes of log data are generated, and existing methods fail to provide comprehensive visualization techniques for correlating attributes over time and across multiple dimensions.
Innovation Solution
The proposed solution combines three visualization techniques: heat maps, theme rivers, and histomatrix, to transform multi-dimensional log data into interactive views, enabling users to analyze correlations between single attributes and time, between two attributes, and among three attributes, through a progressive and interactive analysis methodology.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional data analysis methods are used, then data processing is simple, but the ability to understand and analyze multi-dimensional data relationships is insufficient
Solution Approach 1:
The patent segments the analysis of multi-dimensional data into three distinct visualization types, each handling a specific number of attributes (two attributes, three attributes, and thematic variations over time). This segmentation allows the system to manage complexity by dividing the overall analysis task into manageable parts, where each visualization type focuses on a specific aspect of data relationships without overwhelming the user with all dimensions simultaneously.
Solution Approach 2:
The patent introduces visual dimensions to represent data relationships that cannot be effectively displayed in traditional tabular formats. By mapping attribute relationships to spatial visualizations (heat maps, theme rivers, histomatrix), the system transforms abstract multi-dimensional data into perceptible visual forms, adding a visual dimension that enhances data understanding without increasing logical complexity.
2Loss of information
If comprehensive visualization techniques are implemented, then data analysis capability is improved, but visual clutter increases
Solution Approach 1:
The patent applies local quality by providing different visualization types for different analysis needs rather than using a single comprehensive visualization for all attributes. Each visualization type (heat map for two attributes, theme river for thematic variations, histomatrix for three attributes) is optimized for its specific purpose, allowing users to select the appropriate view for each analytical task, thereby reducing visual clutter while maintaining comprehensive analysis capability.
Solution Approach 2:
The patent implements dynamic visualization selection where the system can adaptively present different visualization types based on the analysis task and user needs. This dynamic approach allows the system to display only the relevant visualization for the current analytical context, reducing visual clutter by hiding irrelevant visualizations while maintaining the capability to access comprehensive data relationships when needed.
3Adaptability or versatility
If multiple visualization types are provided, then analysis flexibility is improved, but system complexity increases
Solution Approach 1:
The patent achieves universality by designing a unified visualization framework that handles multiple attribute relationships through three standardized visualization types. Rather than creating separate specialized tools for each analysis scenario, the system provides a universal set of visualizations that can address various analytical tasks (two-attribute relationships, three-attribute relationships, thematic variations) within a single integrated interface, reducing system complexity while maintaining flexibility.
Data Source
AI summary
A set of multidimensional data is obtained. At least a portion of the set of multidimensional data is processed to generate a set of formatted data, wherein the set of formatted data comprises at least one of attributes, attribute values and statistics on attribute values. A user is enabled to select, on a graphical user interface, an analysis task to be performed on at least a portion of the set of formatted data. One or more visualizations are generated from a set of visualization types for presentation on a graphical user interface to the user. The set of visualization types comprises a first visualization type representing a relationship between two attributes whereby attribute value pairs are represented by varying colors, a second visualization type representing thematic variations over time with respect to values of at least one attribute; and a third visualization type representing values of three attributes comprising one or more histograms. The one or more generated visualizations are based on the selected analysis task.