Automated Dashboard Graph Selection via ML Effectiveness Matrix
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Solution Overview
Problem
Traditional data analytics applications face challenges in visualizing complex and voluminous data sets, limiting user interpretation and collaboration, as they often rely on manual dashboard configuration and are not well-suited for collaborative workflows.
Innovation Solution
A process that uses machine learning models to automatically select and generate graphical representations of data in dashboards based on data features, user preferences, and behavior patterns, incorporating a graph-effectiveness matrix to determine the most effective visualizations and facilitate collaborative data analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual dashboard configuration is used, then users can customize visualizations according to their preferences, but the process is time-consuming and complex
Solution Approach 1:
The system automatically generates dashboard visualizations by analyzing data features and selecting appropriate graph types without requiring manual user configuration. The machine learning model autonomously performs the dashboard creation task, freeing users from the time-consuming manual setup process while still delivering customized, context-appropriate visualizations.
Solution Approach 2:
The system pre-trains machine learning models with effectiveness scores for different graph types across various data features before actual dashboard generation. This preliminary training enables the system to rapidly select optimal visualizations during runtime without requiring users to manually configure dashboard elements, thus reducing configuration time while maintaining customization quality.
2Adaptability or versatility
If traditional data analytics applications are used, then basic data visualization is possible, but they are not well-suited for collaborative workflows
Solution Approach 1:
The system enhances traditional data analytics applications by integrating collaborative workflow capabilities into the existing visualization framework. The machine learning model serves multiple functions: analyzing data features, selecting graph types, and supporting collaborative interactions, thereby increasing adaptability without proportionally increasing system complexity.
Solution Approach 2:
The machine learning model acts as an intermediary between raw data and visualization output, automatically translating data features into appropriate graph representations. This intermediary layer simplifies the interaction for collaborative users by eliminating the need for manual configuration while maintaining the underlying system's architecture and avoiding excessive complexity.
3Loss of information
If users process large data sets manually, then they can examine data in detail, but the cognitive load is high and information extraction is difficult
Solution Approach 1:
The system replaces manual cognitive processing with machine learning-based automated analysis. The machine learning model performs the intellectually demanding task of analyzing data features and selecting appropriate visualizations, thereby reducing the cognitive load on users while improving information extraction efficiency through algorithmic pattern recognition.
Solution Approach 2:
The machine learning model serves as an intermediary between large complex data sets and users, automatically analyzing data features and translating them into appropriate visual representations. This intermediary processing reduces cognitive complexity for users by handling the complex analysis tasks algorithmically while preserving all relevant information through intelligent visualization selection.
Data Source
AI summary
Provided is a process of configuring a dashboard of a graphical user interface, the process including: obtaining identifiers of metrics; obtaining features that are properties of the metrics; accessing in a graph-effectiveness matrix effectiveness scores corresponding to the features; selecting a plurality of graphs to graphically represent the metrics in a dashboard; and instructing a computing device to display the dashboard.