Problem
Research questions and friction points this paper is trying to address.
visualization composition
task-aware
composite visualization
design space
perceptual clarity
VisPuzzle: Task-Aware Composite Visualization Construction
🤖 AI Summary
This work addresses the challenge of navigating the vast design space in multi-view composite visualization, where balancing task relevance, perceptual clarity, and aesthetic quality remains difficult. The paper introduces a novel formulation that casts task-aware composition construction as a sequential search problem over a composition graph. Leveraging Monte Carlo graph search guided by a multi-objective reward function—integrating task relevance, perceptual effectiveness, and aesthetic consistency—the approach efficiently explores high-quality visualization compositions. Experimental results demonstrate that the top-ranked generated composites align closely with human judgments of visualization quality, confirming the method’s effectiveness, scalability, and practical utility in complex design spaces.
📝 Abstract
Compositing multiple visualizations into a coherent whole remains challenging due to the vast design space and the need to balance the coverage of task-relevant data insights (e.g., trends and outliers), perceptual clarity, and aesthetic quality. In this paper, we present VisPuzzle, a task-aware method that formulates visualization composition as a stepwise search problem over a composition graph. In this graph, nodes represent either data composition operations (e.g., union, join) or visual composition operations that determine component relationships, spatial arrangements, or component proportions, and edges encode feasible transitions between operations. We employ Monte Carlo Graph Search to efficiently identify high-quality composition candidates from this graph, guided by a reward function that balances task relevance, perceptual effectiveness, and aesthetic coherence. A use case and a user study show that the top-ranked candidates produced by VisPuzzle align closely with human judgments of composition quality, demonstrating its utility in supporting principled and scalable visualization composition.
Innovation
Methods, ideas, or system contributions that make the work stand out.
task-aware visualization
composition graph
Monte Carlo Graph Search
visual composition
automated design
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2024-09-02arXiv.orgCitations: 1
