Semantic UI Model Ranking for Data Visualization
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Solution Overview
Problem
Organizations face challenges in effectively utilizing their vast and diverse data sets to improve business practices due to the complexity of integrating data from various sources, requiring deep domain knowledge and customization for effective dashboard creation, which limits reuse across similar domains.
Innovation Solution
A system that maps data model fields to concepts and ranks user interface models based on their match with data sources, allowing for the automatic generation of user interfaces that adapt to different domains and data sources, enabling efficient data visualization and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If custom designed dashboards are created to effectively visualize data from various sources, then the capability to reason about data is improved, but the complexity of dashboard design and domain knowledge required increases prohibitively
Solution Approach 1:
The system enables dashboards to automatically generate user interfaces based on their own data models and semantic information, without requiring external designers. The dashboard metadata includes UI configuration that allows the system to self-configure appropriate visualizations and interactions based on the data being presented.
Solution Approach 2:
A library of reusable dashboard templates and UI models is provided that can serve multiple different data sources and domains. These universal templates can be adapted to various contexts through parameter binding, eliminating the need to create custom dashboards for each specific data source while maintaining effectiveness.
2Ease of operation
If dashboards are closely tailored to specific data sources to provide effective visualization, then data reasoning capability is improved, but reusability across similar domains is precluded
Solution Approach 1:
The dashboard is divided into modular components with distinct responsibilities: data model layer, semantic metadata layer, and UI presentation layer. This segmentation allows the underlying data source specifics to be isolated while maintaining reusable UI templates that can adapt to different data sources through configuration rather than redesign.
Solution Approach 2:
Reusable dashboard templates utilize parameter binding where specific data source characteristics are represented as parameters. By changing these parameters rather than the underlying template structure, the same dashboard design can be adapted to visualize different data sources effectively across similar domains.
3Measurement precision
If custom dashboards are designed for specific domains, then domain-specific data analysis is improved, but the time and expertise required for dashboard creation increases
Solution Approach 1:
Domain-specific dashboard templates and UI models are pre-configured with appropriate visualizations, interactions, and metadata structures for common analytical patterns. When a new dashboard is needed, the system selects and instantiates the appropriate pre-configured template, eliminating the need to design from scratch while maintaining domain-specific effectiveness.
Solution Approach 2:
Existing successful dashboard designs are captured as reusable templates that can be copied and instantiated for new data sources. The system maintains a library of proven dashboard patterns that can be replicated across similar domains, preserving the precision of domain-specific analysis while dramatically reducing creation time through template reuse.
Data Source
AI summary
Embodiments are directed to managing user interfaces. User interface (UI) models associated with concepts may be provided such that the UI models include visualizations. Other concepts may be associated with a data model based on fields of the data model. Characteristics of the concepts associated with each UI model and the other concepts associated with the data model may be compared to each other such that results of each comparison may be employed to generate a score for each UI model. The UI models may be ordered based on each score. A report that includes a rank ordered list of the UI models may be provided.