Dashboard Visualization Selection Using User-Specific Metric Models
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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 customizable 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 relevance and engagement are reduced
Solution Approach 1:
The system automatically selects visualization types by analyzing user characteristics and data metric properties without requiring manual user configuration. The machine learning model performs self-service dashboard generation, adapting visualizations to each user's preferences and role automatically.
Solution Approach 2:
The system changes the parameters of visualization selection based on user characteristics (persona roles, interaction data) and data metric properties. Different parameter combinations lead to different visualization type selections, enabling dynamic adaptation without increasing perceived complexity for users.
2Loss of information
If customized visualizations tailored to each user are generated, then user engagement and data relevance are enhanced, but system complexity and computational resources increase
Solution Approach 1:
User characteristics and preferences are collected and stored in advance through user profiles and interaction tracking. This preliminary data collection enables the machine learning model to make informed visualization selections without real-time complexity, as the analysis work is prepared beforehand through continuous user interaction monitoring.
Solution Approach 2:
The system continuously gathers feedback from user interactions with dashboards and uses this feedback to refine visualization selections. The machine learning model learns from user behavior patterns, improving its ability to predict preferred visualization types while managing complexity through iterative learning rather than complex rule-based systems.
3Measurement precision
If machine learning models analyze user characteristics to select visualization types, then visualization relevance is improved, but processing time and computational resources are increased
Solution Approach 1:
The system applies partial analysis by focusing on the most influential user characteristics and data metric properties rather than analyzing all possible factors. This selective approach maintains high matching accuracy while reducing computational overhead and generation time by concentrating resources on the most critical selection criteria.
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.