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A Taxonomy of Uncertainty Events in Visual Analytics
IEEE Computer Graphics and Applications
|August 18, 2023
Summary
This study introduces a taxonomy to categorize uncertainty events within the visual analytics (VA) process. Understanding these events is crucial for reliable data analysis and insight generation.
Area of Science:
- Computer Science
- Information Visualization
Background:
- Visual analytics (VA) is essential for data analysis and insight generation.
- Uncertainty can arise from various components within the VA process.
- Differentiating these uncertainty events is critical for accurate interpretation.
Purpose of the Study:
- To propose a comprehensive taxonomy of uncertainty events in the visual analytics cycle.
- To structure the taxonomy based on the components of the VA cycle.
- To identify dependencies between different uncertainty events.
Main Methods:
- Developed a taxonomy of potential uncertainty events.
- Structured the taxonomy along the components of the visual analytics cycle.
- Identified and listed dependencies between uncertainty events.
Main Results:
- A structured taxonomy of uncertainty events in visual analytics is presented.
- Dependencies between various uncertainty events have been identified.
- A real-world example demonstrates the practical application of the taxonomy.
Conclusions:
- The proposed taxonomy provides a framework for understanding and managing uncertainty in visual analytics.
- Identifying dependencies aids in mitigating the impact of uncertainty.
- The taxonomy serves as a practical tool for researchers and practitioners in the field.
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