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A Survey on Progressive Visualization
IEEE Transactions on Visualization and Computer Graphics
|December 25, 2023
Summary
Progressive visualization (PV) offers immediate interaction with large datasets by using partial results. This study introduces a new taxonomy for PV systems, categorizing them by data processing, domain, and visual updates.
Area of Science:
- Computer Science
- Information Visualization
Background:
- Growing data and complex algorithms challenge user interaction in visual analytics.
- Progressive visualization (PV) and visual analytics (PVA) enable immediate feedback and interaction with large datasets using incrementally improving partial results.
Purpose of the Study:
- To provide a comprehensive overview of the design space for progressive visualization systems.
- To systematically categorize existing progressive visualization publications and derive a new taxonomy.
Main Methods:
- Conducted a systematic survey of related work in progressive visualization.
- Categorized publications based on their progressive features, data processing, data domain, and visual update strategies.
Main Results:
- Progressive visualizations can be classified using existing visualization taxonomies.
- A new taxonomy is proposed, distinguishing PV systems by data management and visual update mechanisms.
- Key properties like uncertainty, steering, visual stability, and real-time processing are identified as distinct features of progressive applications.
Conclusions:
- The study establishes a novel taxonomy for progressive visualization systems.
- Identified key properties and evaluation methodologies, highlighting research gaps and future challenges in the field.
- The findings aim to guide the design and development of more effective progressive visualization tools.
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