Extensions of Parallel Coordinates for Interactive Exploration of Large Multi-Timepoint Data Sets

Jorik Blaas, Charl Botha, Frits Post
Data Visualization Group, Delft University of Technology
IEEE Transactions on Visualization and Computer Graphics, Vol. 14, No. 6. (2008), pp. 1436-1451


   title={Extensions of parallel coordinates for interactive exploration of large multi-timepoint data sets},

   author={Blaas, J. and Botha, C. and Post, F.},

   journal={IEEE Transactions on Visualization and Computer Graphics},




   publisher={Published by the IEEE Computer Society}


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Parallel coordinate plots (PCPs) are commonly used in information visualization to provide insight into multi-variate data. These plots help to spot correlations between variables. PCPs have been successfullyapplied to unstructured datasets up to a few millions of points. In this paper, we present techniques to enhance the usability of PCPs forthe exploration of large, multi-timepoint volumetric data sets, containingtens of millions of points per timestep. The main difficulties that arise when applying PCPs to large numbers of data points are visual clutter and slow performance, making interactiveexploration infeasible. Moreover, the spatial context of the volumetric data is usually lost. We describe techniques for preprocessing using data quantization and compression, and for fast GPU-based rendering of PCPs using joint density distributions for each pair of consecutive variables, resulting in a smooth,continuous visualization. Also, fast brushing techniques are proposed for interactive data selection in multiple linked views, including a 3D spatial volume view. These techniques have been successfully applied to three large data sets: Hurricane Isabel (Vis’04 contest), the ionization front instability data set (Vis’08 design contest), and data from a large-eddy simulation of cumulus clouds. With these data, we show how PCPs can be extended to successfully visualize and interactively explore multi-timepoint volumetric datasets with an order of magnitude more data points.
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