Heatmap Breakpoint Calculation Using Logarithmic Scaling
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Solution Overview
Problem
Existing data visualization systems, particularly heatmaps, struggle to accurately represent skewed data sets, often overwhelming important data points by using too many color transitions, which can lead to these points being undetectable and inefficient computational processes due to inaccurate maximum value estimations.
Innovation Solution
A method and system that determine transition boundaries for heatmaps using a logarithmic base value calculated from the data set, employing an exponential function with incrementally increased exponent values to represent data values, allowing for more accurate and clear visualization of skewed data sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a linear color transition method is used to represent data values in a heatmap, then the implementation is simple, but important data points in skewed data sets become undetectable due to overwhelming color transitions
Solution Approach 1:
The patent changes the parameter of color transition calculation from linear to logarithmic. Instead of evenly distributing colors across the full data range, the system calculates transition values using logarithmic functions (e.g., log10) to create non-uniform breakpoints that concentrate color transitions in regions where data points actually exist. This resolves the contradiction by maintaining implementation simplicity while dramatically improving data point detectability in skewed datasets.
2Adaptability or versatility
If the full range of data values is covered with many color transitions, then all data values are represented, but the heatmap becomes complex and important data points are lost
Solution Approach 1:
The patent extracts and focuses on the relevant portion of the data range by using logarithmic scaling to identify where actual data points concentrate. Instead of creating color transitions across the entire theoretical range of possible values, the system calculates breakpoints based on the actual distribution of data, extracting only the necessary transition points. This reduces heatmap complexity while maintaining adaptability to represent all actual data values present in the dataset.
3Productivity
If maximum value estimation is used to determine color transitions, then computational effort is reduced, but inaccurate estimations lead to poor data representation
Solution Approach 1:
The patent applies preliminary action by first analyzing the actual data distribution to determine appropriate logarithmic breakpoints before generating the heatmap. Instead of relying on rough maximum value estimations, the system performs a preliminary scan of the data to identify the range and distribution of actual values, then uses this information to calculate optimal color transition points using logarithmic functions. This approach maintains computational efficiency while significantly improving data representation accuracy.
Data Source
AI summary
In a data visualization computing system, a computer implemented method of determining transition boundaries from data values in a data set for the generation of a graphical heatmap representation of the data values, the method including the steps of the data visualization computing system: retrieving the data values in the data set; determining a logarithmic base value, wherein the base value is calculated based on the retrieved data values; and generating transition boundaries for the heatmap representation by calculating transition boundary values, wherein the transition boundary values are calculated using an exponential function with a base value equal to the determined logarithmic base value and an exponent value that is incrementally increased from a value of one to a maximum exponent value to represent the retrieved data values.