Multivariate Data Visualization Grid Layout
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Solution Overview
Problem
Devices face challenges in efficiently visualizing and analyzing large quantities of metrics from various fields, such as telecommunications and biostatistics, due to excessive processing resource utilization and the need for specialized knowledge, especially with the increase in data from IoT and wearable devices.
Innovation Solution
A method that maps key performance indicators (KPIs) to a set of coordinates, generating a graphical representation and utilizing image analysis techniques to compare and understand KPIs across different times, locations, or people, without requiring specialized analysis solutions, thereby reducing processing resource utilization and simplifying user understanding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If a complex visualization of multiple metrics is generated, then the information representation completeness is improved, but the processing resource utilization increases excessively
Solution Approach 1:
The patent segments the complex multivariate data into multiple individual metric visualizations displayed in a grid layout. Each metric is represented separately with its own visualization elements, allowing the system to process and render simpler individual components rather than attempting to render a single complex integrated visualization, thereby reducing processing resource utilization while maintaining information completeness.
Solution Approach 2:
The patent introduces a spatial dimension by arranging multiple metric visualizations in a grid layout across the display area. This dimensional transformation allows the system to present comprehensive multivariate information by distributing visualizations across space rather than concentrating all metrics in a single complex visualization, reducing the processing burden on any single rendering operation.
2Loss of information
If a custom visualization solution is created for multiple metrics, then the information representation completeness is improved, but the device complexity increases
Solution Approach 1:
The patent implements a universal visualization template that can represent multiple different metrics using the same structural framework and visual elements. The grid-based layout and standardized metric representation methods allow the system to handle diverse multivariate data without requiring custom visualization solutions for each metric, thereby reducing device complexity while maintaining the ability to comprehensively represent information.
Solution Approach 2:
The patent changes the representation parameters by displaying metrics in a standardized grid format with consistent visual properties. Instead of creating complex custom visualizations for each metric type, the system uses uniform parameters for layout, scaling, and presentation across all metrics, simplifying the visualization solution while preserving information completeness.
3Measurement precision
If specialized knowledge is used to generate custom visualization, then the measurement precision is improved, but the ease of operation deteriorates
Solution Approach 1:
The patent enables the system to automatically generate and display multiple metric visualizations without requiring user intervention or specialized knowledge. The automated grid-based presentation method allows the system to self-organize and display comprehensive metric information in an easily interpretable format, maintaining analysis precision while significantly improving ease of operation for users without specialized expertise.
Data Source
AI summary
A device may receive input data regarding a particular field of analysis. The device may determine a mapping of a set of metrics of the input data to a set of coordinates. The device may generate a representation of the set of metrics based on the mapping of the set of metrics. A group of pixels may be caused to provide a particular visualization corresponding to a value of a particular metric mapped to a particular coordinate based on the mapping of the set of metrics. The device may provide a plurality of versions of the representation. The particular metric may map to the particular coordinate in each of the plurality of versions of the representation. The plurality of versions of the representation being associated with one or more changes to a particular parameter of the input data.