Automatic Infographic Generation via Data Model Role Assignment
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Solution Overview
Problem
Current statistical visualization tools lack the ability to automatically draw infographics that effectively visualize datasets, often relying on user experience and knowledge to select appropriate charts, which can lead to ineffective data representation.
Innovation Solution
A computer-implemented method that automatically draws infographics by receiving selected variables from a dataset, determining their association with a data model, and using this model to assign roles and levels of measurement, thereby selecting the appropriate infographic type for visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automatic infographic generation is implemented, then productivity is improved, but device complexity increases
Solution Approach 1:
The system segments the infographic generation process into distinct modules: data model determination, role assignment, level of measurement classification, and infographic type selection. Each module handles a specific aspect of the visualization process, allowing the complex task to be divided into manageable, independent components that can be processed systematically.
Solution Approach 2:
The system performs preliminary actions by pre-defining data models, roles, and levels of measurement for variables before the actual infographic generation. This preparatory work includes classifying variables according to established statistical frameworks, which streamlines the subsequent visualization process and enables automatic selection of appropriate infographic types without requiring complex real-time decision-making.
2Ease of operation
If automatic role assignment is implemented, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The system incorporates feedback mechanisms that allow users to review and correct automatically assigned roles and levels of measurement. The system provides information about its classification decisions and enables user intervention when the automatic assignment does not accurately reflect the intended variable relationships, thus maintaining measurement precision while preserving ease of operation.
Solution Approach 2:
The system performs self-service by automatically determining data models, assigning roles, and classifying levels of measurement based on the input data characteristics. This automatic self-determination reduces the need for manual user input while maintaining accuracy through algorithmic analysis of variable relationships and data patterns.
Data Source
AI summary
A computer-implemented method, system and computer program product for automatically drawing infographics. Variables of a dataset are received from a computing device that were selected by the user of the computing device. For those selected variables that are associated with a data model, a procedure to draw infographics for variables assigned or not assigned the role of a target using the data model associated with each of the variables assigned or not assigned the role of target, respectively, is implemented. Alternatively, if the selected variables are not associated with a data model, then such variables are assigned a level of measurement as well as assigned the role of input. Such assignments become the data model which, along with the metadata (e.g., values of the variable) obtained by parsing the original data, are used to implement the procedure to draw infographics for variables not assigned the role of a target.