Analysis of Uncertain Scalar Data with Hixels

  • Joshua A. LevineEmail author
  • David Thompson
  • Janine C. Bennett
  • Peer-Timo Bremer
  • Attila Gyulassy
  • Valerio Pascucci
  • Philippe P. Pébay
Part of the Mathematics and Visualization book series (MATHVISUAL)


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”.


Morse Theory Discrete Random Variable Basin Boundary Pointwise Mutual Information Discrete Morse Theory 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  • Joshua A. Levine
    • 1
    Email author
  • David Thompson
    • 2
  • Janine C. Bennett
    • 3
  • Peer-Timo Bremer
    • 4
  • Attila Gyulassy
    • 5
  • Valerio Pascucci
    • 5
  • Philippe P. Pébay
    • 3
  1. 1.School of ComputingClemson UniveristyClemsonUSA
  2. 2.Kitware, Inc.CarrboroUSA
  3. 3.Sandia National LaboratoriesLivermoreUSA
  4. 4.Lawrence Livermore National LaboratoryLivermoreUSA
  5. 5.Scientific Computing and Imaging InstituteUniversity of UtahSalt Lake CityUSA

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