Analysis of Uncertain Scalar Data with Hixels
One of the greatest challenges for today’s visualization and analysis communities is the massive amounts of data generated from state of the art simulations. Traditionally, the increase in spatial resolution has driven most of the data explosion, but more recently ensembles of simulations with multiple results per data point and stochastic simulations storing individual probability distributions are increasingly common. This chapter describes a relatively new data representation for scalar data, called hixels, that stores a histogram of values for each sample point of a domain. The histograms may be created by spatial down-sampling, binning ensemble values, or polling values from a given distribution. In this manner, hixels form a compact yet information rich approximation of large scale data. In essence, hixels trade off data size and complexity for scalar-value “uncertainty”.
- 3.Feller, W.: An Introduction to Probability Theory and its Applications, vol. 1. Wiley, New York (1968)Google Scholar
- 4.Forman, R.: A user’s guide to discrete Morse theory. In: Proceedings of the 2001 Internat. Conference on Formal Power Series and Algebraic Combinatorics. A Special Volume of Advances in Applied Mathematics, p. 48 (2001)Google Scholar
- 8.Matsumoto, Y.: An Introduction to Morse Theory. Translations of Mathematical Monographs, vol. 208. American Mathematical Society, Providence (2002)Google Scholar
- 10.Thompson, D., Levine, J., Bennett, J., Bremer, P.T., Gyulassy, A., Pascucci, V., Pebay, P.: Analysis of large-scale scalar data using hixels. In: IEEE Symposium on Large-Scale Data Analysis and Visualization (LDAV) (2011)Google Scholar