PerVis

Visualization Window

Marker Size: 2.0

[ Awaiting Data ]

References:

[1] Park, Y., et al., "Visualization-aware sampling for very large databases", ICDE 2016.

[2] Chen, Z., et al., "Variational blue noise sampling", IEEE TVCG, 18(10).

[3] Eldar, Y., et al., "The farthest point strategy for progressive image sampling", IEEE TIP, 6(9).

[4] Moumoulidou, Zafeiria, et al. "Perception-aware Sampling for Scatterplot Visualizations." arXiv preprint arXiv:2504.20369 (2025).

Application of PAWS

1 Dataset Selection

Select a dataset to view its complete data pattern

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Trisa

Data Scientist

Let's observe how Perception-Aware Sampling (PAWS) performs against other sampling techniques on different datasets.

Use the controls to switch datasets, observe the saliency weights, and test various sampling algorithms.

First, let's start by selecting a dataset and plotting its complete data to understand its inherent visual patterns.