Automated Geometric Plot Generation for Categorical Data
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Solution Overview
Problem
Existing graphing systems require manual creation and selection of graph types and variables for data sets, making the process labor-intensive and inefficient for data analysis.
Innovation Solution
A computer system and method for automated generation of graphs with a processor and storage medium that receives requests to generate geometric plots with specified datasets, including categorical index values and offset values to determine shape positions, allowing for the creation of various graph types such as heat maps, pie charts, and hygrometer plots.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated graph generation is implemented, then productivity and efficiency are improved, but device complexity increases
Solution Approach 1:
The system automatically selects graph types and variables based on dataset characteristics without requiring manual user intervention. The graph generation engine autonomously analyzes the dataset structure, determines appropriate visualization methods, and generates graphs automatically, enabling the system to serve itself rather than relying on manual operations.
Solution Approach 2:
The graph generation engine is designed to handle multiple graph types (bar charts, line graphs, pie charts, scatter plots, etc.) and various dataset formats through a single unified system. This multi-functional capability allows the same system to perform diverse graphing tasks, reducing the need for multiple specialized tools while maintaining high productivity.
2Loss of time
If manual graph creation is required, then ease of operation is maintained, but loss of time increases
Solution Approach 1:
The system performs preliminary analysis of the dataset structure, variables, and characteristics before graph generation. By pre-processing the data and determining optimal graph configurations in advance, the system eliminates the need for manual exploration and selection during the actual graph creation process, significantly reducing time loss while maintaining operational simplicity.
Solution Approach 2:
The system incorporates feedback mechanisms that analyze the dataset characteristics and automatically adjust graph type selections and variable mappings based on the data structure. This feedback loop enables the system to learn from data patterns and make intelligent decisions, reducing both time and user effort by eliminating trial-and-error manual configuration.
Data Source
AI summary
A geometric plot is generated having at least two axes, wherein a dataset from which the plot will be generated specifies at least one shape for the geometric plot and wherein the plot includes at least one axis having a plurality of discrete, categorical index values. At least one offset value is specified that determines a mapping of one or more shape-defining vertices of the at least one shape to a location that is a fractional distance between two of the discrete, categorical index values, such that a generated set of data specifies a pixel location for each of the shape-defining vertices of the at least one shape.