Coaxial interactive viewer: a multi-dimensional data visualization with spatial distortional views
Proceedings of the 2011 Visual Information Communication - International Symposium
The product explorer: decision making with ease
Proceedings of the International Working Conference on Advanced Visual Interfaces
Visual coherence for large-scale line-plot visualizations
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
Progressive splatting of continuous scatterplots and parallel coordinates
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
Visualization of time-series data in parameter space for understanding facial dynamics
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
Uncertainty-aware exploration of continuous parameter spaces using multivariate prediction
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
User-driven feature space transformation
EuroVis '13 Proceedings of the 15th Eurographics Conference on Visualization
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Typical scientific data is represented on a grid with appropriate interpolation or approximation schemes,defined on a continuous domain. The visualization of such data in parallel coordinates may reveal patterns latently contained in the data and thus can improve the understanding of multidimensional relations. In this paper, we adopt the concept of continuous scatterplots for the visualization of spatially continuous input data to derive a density model for parallel coordinates. Based on the point-line duality between scatterplots and parallel coordinates, we propose a mathematical model that maps density from a continuous scatterplot to parallel coordinates and present different algorithms for both numerical and analytical computation of the resulting density field. In addition, we show how the 2-D model can be used to successively construct continuous parallel coordinates with an arbitrary number of dimensions. Since continuous parallel coordinates interpolate data values within grid cells, a scalable and dense visualization is achieved, which will be demonstrated for typical multi-variate scientific data.