Data-Metric Visualization Selection Using User Characteristics
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Solution Overview
Problem
Existing dashboards struggle to provide personalized visualizations of data metrics that are optimally suited for individual users due to the mismatch between standard visualization types and user-specific needs, roles, and data sets.
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 data, to generate customized dashboards that dynamically adapt to user inputs and feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If standard visualization types are used for all users, then implementation complexity is reduced and development is simplified, but the suitability and effectiveness of visualizations for individual users deteriorates
Solution Approach 1:
The system automatically selects and configures visualization types based on user characteristics, data metric properties, and interaction history without requiring manual user configuration. The machine learning model performs self-service by autonomously determining the optimal visualization setup, eliminating the need for users to manually adjust visualization parameters while still providing personalized results.
Solution Approach 2:
The system dynamically changes visualization parameters (type, configuration, layout) based on user characteristics, data metric properties, and interaction feedback. The machine learning model adjusts these parameters automatically to optimize visualization effectiveness for each user context, transforming static standard visualizations into adaptive, user-specific visualizations without increasing implementation complexity.
2Adaptability or versatility
If customized visualizations are created for each user, then visualization effectiveness and user engagement improve, but system complexity and computational requirements increase
Solution Approach 1:
A single machine learning model performs multiple functions: it selects visualization types, configures visualization parameters, determines data metric relevance, and adapts to user feedback. This multi-functional approach consolidates what would otherwise require multiple separate systems into one unified model, providing personalized visualizations without proportionally increasing system complexity.
Solution Approach 2:
The system uses user interaction feedback to continuously refine and improve visualization selections. The machine learning model learns from user behavior patterns and adjusts future visualization recommendations accordingly, enabling progressive personalization that improves effectiveness over time without requiring proportional increases in system complexity.
3Loss of information
If machine learning models are employed to select visualizations, then visualization relevance and user engagement improve, but computational resources and processing time increase
Solution Approach 1:
The machine learning model is pre-trained on extensive datasets containing user characteristics, data metric properties, and visualization effectiveness patterns. This preliminary training allows the model to make accurate visualization selections during actual use without requiring intensive real-time computation, as the heavy computational work has already been performed during the offline training phase.
Solution Approach 2:
The system focuses computational resources on the most critical factors influencing visualization effectiveness, such as user role, data metric type, and key interaction patterns. By concentrating on the most impactful variables rather than analyzing every possible parameter, the system achieves high relevance with optimized computational resource usage.
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.