ML Visual Code Generation for Automated Chart Selection
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Solution Overview
Problem
Current data visualization processes are complex and require manual effort to select appropriate charts for data representation, especially when metadata characteristics are unknown, leading to inefficiencies in data analysis.
Innovation Solution
A machine learning-based system that automates the generation of visual code and action for selecting visual presentations by receiving digital description data, generating code-based classes, executing self-learning software, and selecting visual presentations based on features, resulting in operational modifications to server hardware and software, thereby presenting optimized visualizations to users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processes are used to select charts and perform column-to-slot bindings, then flexibility and control are maintained, but the process becomes complex and time-consuming
Solution Approach 1:
The system employs self-learning software code that automatically performs chart selection and column-to-slot binding without requiring manual intervention. The machine learning model learns from data characteristics and autonomously makes decisions about visualization selection, eliminating the need for manual process steps while maintaining high accuracy.
Solution Approach 2:
The system changes the approach from manual parameter selection to automated parameter determination by using machine learning models that analyze data characteristics and automatically select appropriate charts and bindings. This transforms the process from human-driven parameter selection to algorithm-driven parameter optimization.
2Reliability
If automated machine learning systems are used to select visual presentations, then productivity and consistency are improved, but the system complexity increases
Solution Approach 1:
The system segments the complex task of visualization selection into distinct components: digital description data reception, code-based class generation, self-learning software execution, visual presentation type selection, and final presentation delivery. This modular architecture manages system complexity by breaking down the automated process into manageable, independent modules.
Solution Approach 2:
The system introduces code-based classes as an intermediary layer between the raw digital description data and the final visual presentation selection. These classes serve as structured representations that facilitate the machine learning process and maintain system organization, acting as mediators that simplify the interaction between different system components.
3Loss of time
If hand-crafted code is required for data visualization, then precision and control are maintained, but the effort and time required increase significantly
Solution Approach 1:
The system replaces the mechanical process of hand-crafting code with an automated machine learning-based system. Instead of manually writing and adjusting code for each visualization task, the system uses trained models that automatically generate appropriate visual presentations based on data characteristics, dramatically reducing the time and effort required.
Solution Approach 2:
The system performs preliminary actions by pre-training machine learning models on diverse datasets and visualization scenarios. This preliminary training enables the system to quickly and accurately select appropriate visual presentations without requiring manual code crafting at the time of use, saving significant time during actual data analysis tasks.
Data Source
AI summary
A method, system, and computer program product for implementing machine learning visual code and action generation is provided. The method includes receiving from a plurality of hardware and software sources, digital description data associated with visual presentations and an action for execution. A resulting code-based class for each portion of the digital description data is generated with respect to the visual presentation. Self learning software code is executed and a type of visual presentation is selected with respect to associated visual features and the code-based class. Additionally, a visual presentation is selected and an action is executed resulting in hardware and software of a server hardware device being operationally modified. The visual presentation is presented to a user.