The document discusses challenges in visualizing large multidimensional datasets and proposes using coordinated multiple views and density preservation techniques to address these challenges. Specifically, it proposes using a radial visualization (Radviz) as the master view to select subsets and a parallel coordinates view as the slave view to show details. It also describes developing metrics to characterize clutter and preserve data density differences when sampling data to reduce clutter in visualizations.

1. Larger Screens for Larger Data Sets: Is it a Good Idea? Tiziana Catarci, Giuseppe Santucci Dipartimento di Informatica e Sistemistica Università di Roma ”La Sapienza”

5. Cars & Radviz MPG Cylind Horsep Weight Accel Year Origin P (1, 1, 5) p d1 d3 d2 1 1 5 P (1, 1, 1) p d1 d3 d2 1 1 1

6. Cars and parallel coordinates MPG Cylinders Horsepower Weight Acceleration Year Origin

12. Understanding Radviz MPG Cylind Weight Accel Year Origin Horsep MPG Cylind Horsep Weight Accel Year Origin

13. Selecting data subsets MPG Cylind Weight Accel Year Origin Horsep MPG Cylind Horsep Weight Accel Year Origin

14. Coloring data more red MPG Cylind Weight Accel Year Origin Horsep more blue

15. Selecting through colors MPG Cylind Horsep Weight Accel Year Origin MPG Cylind Weight Accel Year Origin Horsep powerful cars low power cars

21. Example Data density=189 Rep density=56 Data density=205 Rep density=56 The actual visualization hides this difference

22. Density metric 1-WPLDDr=0.392 i.e., 60% hidden 1-WPLDDr=0.738 i.e., 26% hidden Best uniform sampling wrt the density metric