QVis: Query-Based Visual Analysis of Multiscale Patterns in Spatiotemporal Ensembles

Area of Science:

• Fluid dynamics
• Data visualization
• Scientific computing

Background:

• Analyzing dynamic patterns in spatiotemporal ensembles is crucial across scientific fields.
• Droplet impact experiments in fluid dynamics generate large, complex datasets with variable patterns.
• Existing interactive visualization tools have limitations in handling multiscale, multi-pattern queries and variable-sized inputs.

Purpose of the Study:

• To develop a visual analysis approach for interactive exploration of spatiotemporal ensembles.
• To enable multiscale pattern querying supporting variable-sized patterns and multi-pattern analysis.
• To facilitate the relationship discovery between ensemble parameters and pattern occurrences.

Main Methods:

• An extended similarity model supporting variable-sized pattern queries.
• Interactive querying using coordinated views for pattern occurrence analysis.
• A guidance mechanism to identify underexplored regions within the dataset.
• Demonstration on synthetic and real-world fluid dynamics datasets.

Main Results:

• The approach successfully handles variable-sized patterns for querying.
• Coordinated views facilitate interactive comparison and analysis of pattern occurrences.
• The guidance mechanism aids in discovering novel parameter-pattern relationships.
• Demonstrated effectiveness on both synthetic and real-world data.

Conclusions:

• The developed visual analysis approach is intuitive and effective for exploring spatiotemporal ensembles.
• It overcomes limitations of previous methods by supporting multiscale, multi-pattern, and variable-sized queries.
• Domain experts confirmed the utility in revealing parameter-pattern relationships in fluid dynamics.