Automatic Axis Break Creation for Bar and Waterfall Charts
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Solution Overview
Problem
Conventional methods for enhancing data visualization in charts are labor-intensive and time-consuming, especially when dealing with large datasets or frequent updates, as users must manually insert axis breaks to highlight important differences amidst large magnitudes or outliers.
Innovation Solution
Automatically creating axis breaks in bar and waterfall charts by determining maximum and minimum break points relative to selected data points, incorporating padding to prevent breaks from occurring at zero or bar ends, and recalculating breaks with updated data, allowing for dynamic visualization adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If manual insertion of chart axis breaks is used to enhance data visualization, then the ability to emphasize contrast with data is improved, but the time consumption and labor intensity increase significantly
Solution Approach 1:
The system performs self-service by automatically detecting outliers and calculating optimal axis break positions without requiring user intervention. The algorithm autonomously analyzes data distribution, identifies exceptional values, and inserts breaks at mathematically determined positions that maximize visualization effectiveness.
Solution Approach 2:
The system changes the parameter of axis break positioning from manual user-determined values to algorithmically calculated positions based on data statistics. By computing break positions as percentages of outlier distances and applying padding factors, the system transforms subjective manual adjustment into objective parameter-driven automation.
2Loss of information
If manual insertion of chart axis breaks is used to enhance data visualization, then the ability to emphasize contrast with data is improved, but the labor intensity increases
Solution Approach 1:
The system performs self-service by automatically detecting outliers and calculating optimal axis break positions without requiring user intervention. The algorithm autonomously analyzes data distribution, identifies exceptional values, and inserts breaks at mathematically determined positions that maximize visualization effectiveness.
Solution Approach 2:
The system introduces an intermediary algorithm that mediates between the raw data and the chart visualization. This intermediary layer automatically processes the data, identifies patterns and outliers, and determines optimal break positions, eliminating the need for direct user manipulation of chart elements.
3Loss of information
If axis breaks are inserted to handle outliers, then the visualization of small differences is improved, but the complexity of determining break positions increases
Solution Approach 1:
The algorithm segments the axis into distinct regions: the outlier region and the main data region. By calculating break positions as percentages of the outlier distance from zero and applying padding factors, the algorithm divides the axis in a way that isolates the outlier while preserving the relative scaling of the main dataset.
Solution Approach 2:
The system performs preliminary analysis of the data distribution, identifies outliers before chart rendering, and pre-calculates optimal break positions. This preliminary action allows the main chart rendering process to simply apply pre-determined break positions without complex real-time calculations.
Data Source
AI summary
Embodiments automatically create an axis break in a bar chart or waterfall chart to promote data visualization. For a bar chart with positive bars, a maximum axis break point is determined relative to a lowest datapoint (D1) in the selected bar(s). A minimum axis break point is found from a value (D2) outside selection which is between D1 and zero, and which is closest to D1. The inclusion of padding on either side of a break prevents the break from lying at zero or a bar end. A different procedure creates axis breaks in a waterfall chart. Certain embodiments store only bar selection metadata with a chart. The location of the axis break then is recalculated each time the chart is rendered (e.g., with updated data). Embodiments may retain axis break data for reference in various stages of a user interaction (e.g., filtering/expanding, drilling-down/up, etc.) with a rendered chart.