Dashboard Visualization Selection for User-Specific Data Metrics
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Solution Overview
Problem
Existing dashboards often visualize data metrics using standard types that may not be optimally suited for every data set or user, failing to account for individual user preferences and needs.
Innovation Solution
Employing trained machine learning models to determine relevant data metrics and select visualization types based on user characteristics, such as persona roles and interaction patterns, enabling dynamic customization of dashboards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If standard visualization types are used for all data metrics, then implementation simplicity is maintained, but user-specific optimization and data set suitability are compromised
Solution Approach 1:
The system automatically selects visualization types by analyzing user characteristics and data set properties without requiring manual user configuration. The machine learning model performs self-service by autonomously determining the most suitable visualization type based on learned patterns from user interactions and data characteristics.
Solution Approach 2:
The system dynamically changes visualization parameters by selecting different visualization types based on varying user characteristics and data set properties. The machine learning model adjusts the visualization type parameter according to the specific combination of user profile and data characteristics, enabling adaptive optimization without fixed configurations.
2Productivity
If personalized dashboards are generated using machine learning models, then user engagement and data interpretation are enhanced, but computational resources and processing time are increased
Solution Approach 1:
The machine learning model is pre-trained on extensive user interaction data and visualization effectiveness metrics before deployment. This preliminary training action enables the model to make rapid predictions during actual dashboard generation, reducing the computational resources required at runtime while maintaining high user engagement through personalized visualizations.
Solution Approach 2:
The system implements feedback loops where user interactions with personalized dashboards are continuously collected and used to retrain and refine the machine learning model. This feedback mechanism improves the accuracy of personalized recommendations over time, enhancing user engagement while optimizing computational efficiency by learning from actual user behavior patterns.
Data Source
AI summary
Techniques for generating a dashboard are disclosed. The system may obtain a set of one or more characteristics of a target user. A set of candidate data metrics that are relevant to the target user may be determined by applying a metric selection model to the set of characteristics. The set of candidate data metrics may be presented as a set of recommend data metrics. Input may be received from a user selecting a particular data metric from the set of recommended data metrics. A visualization selection model may be applied to the particular data metric and/or the set of user characteristics to select a visualization type for the particular data metric. A visualization of the particular data metric that accords to the selected visualization type may be generated based on a set of values associated with the particular data set. The visualization may be presented in the user dashboard.