Multi-Axis Chart Visualization With Selective Scale Hiding
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing charting technologies face difficulties in intuitively visualizing multiple datasets with different scales and data types within a single chart, leading to clutter and difficulty in identifying correlations between disparate data sets.
Innovation Solution
The system generates interactive charts that allow multiple datasets to be visualized with customizable scales, where each dataset has its own scale, and enables selective hiding and revealing of scales based on user input, improving readability and clarity by tailoring the visualization to fit within a common frame.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple datasets with different scales are presented simultaneously in a single chart, then the capability to visualize multiple data types is improved, but the chart becomes cluttered and difficult to interpret
Solution Approach 1:
The patent divides the chart into multiple independent visualization regions, each dedicated to a specific dataset with its own scale. This segmentation allows each dataset to be displayed with appropriate scaling while preventing the overall chart from becoming cluttered, as each region is self-contained and clearly demarcated.
Solution Approach 2:
The patent transitions from a traditional single-axis chart to a multi-region visualization where datasets are arranged in spatial dimensions. By organizing datasets across different regions with clear visual separators, the chart adds a dimensional organization that helps users distinguish between different scales and datasets without confusion.
2Adaptability or versatility
If multiple scales are presented simultaneously in a single chart, then the representation of different datasets is improved, but the difficulty of identifying which datasets correspond to which scales increases
Solution Approach 1:
The patent applies local quality by giving each visualization region its own dedicated scale and visual characteristics. Each region is locally optimized for its specific dataset type, with scales and formatting tailored to that data, making it easy to identify which scale applies to which dataset without cross-contamination or confusion.
Solution Approach 2:
The patent uses color coding and visual differentiation to associate specific datasets with their corresponding scales. By applying distinct visual properties to different regions and their associated scales, users can quickly identify correspondences between datasets and scales through visual cues rather than having to trace complex relationships.
3Ease of operation
If users manually manipulate charted visualizations to achieve intuitive format, then the readability is improved, but the time and computing resources are wasted
Solution Approach 1:
The patent performs preliminary action by automatically organizing datasets into appropriate visual regions with correct scales and formatting before the user views the chart. The system pre-processes the multiple datasets, determines optimal scaling for each, and arranges them in an intuitive layout, eliminating the need for users to manually manipulate the visualizations to achieve readability.
Solution Approach 2:
The chart visualization system performs self-service by automatically adapting its structure and scaling based on the input datasets. The system independently determines the optimal presentation format for multiple datasets with different scales, removing the need for user intervention to achieve an intuitive and readable visualization.
Data Source
AI summary
Systems and methods are provided for generating interactive chart visualizations that incorporate multiple datasets. The charts include different scales that correspond to the different datasets. At least one of the scales can also be selectively hidden. The hidden scale(s) will be rendered in response to user input selecting data/objects corresponding to the hidden scale(s).