Adaptive Dashboard Visualizations for User-Relevant 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 data, to generate customized dashboards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If standard visualization types are used for all data metrics, then implementation simplicity is maintained, but relevance to specific users and data sets deteriorates
Solution Approach 1:
The system dynamically changes visualization parameters (type, format, configuration) based on user characteristics and data set properties. Machine learning models predict optimal visualization parameters by analyzing user profiles, roles, and interactions with data, thereby resolving the contradiction between standardized implementation and customized relevance.
Solution Approach 2:
The visualization system transitions from static standard types to dynamic adaptive types that automatically adjust based on real-time user characteristics and data context. The system continuously learns from user interactions and modifies visualization presentations, enabling both ease of implementation through automation and high adaptability to specific needs.
2Adaptability or versatility
If customized visualizations are generated for each user, then relevance to user needs is improved, but system complexity increases
Solution Approach 1:
The system performs self-service by automatically generating customized visualizations without requiring manual configuration for each user. Machine learning models autonomously analyze user characteristics and data sets, then select and configure appropriate visualization types, eliminating the need for complex manual customization processes while maintaining high adaptability.
Solution Approach 2:
Machine learning models serve as intermediaries between raw user characteristics/data and visualization generation. These models process and translate complex user profiles and data set properties into optimized visualization parameters, simplifying the overall system architecture by centralizing the complexity in trainable models rather than distributed customization logic.
3Measurement precision
If machine learning models are employed to select visualization types, then customization accuracy is improved, but computational resources required increase
Solution Approach 1:
The system performs preliminary action by pre-training machine learning models on extensive user characteristic and visualization effectiveness data before deployment. This pre-training enables the models to make accurate visualization selections with minimal real-time computational overhead, as the heavy learning work is completed beforehand during model training phases.
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.