TY - GEN
T1 - Analysis of large-scale scalar data using hixels
AU - Thompson, David
AU - Levine, Joshua A.
AU - Bennett, Janine C.
AU - Bremer, Peer Timo
AU - Gyulassy, Attila
AU - Pascucci, Valerio
AU - Pébay, Philippe P.
PY - 2011
Y1 - 2011
N2 - 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 paper introduces a 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". Based on this new representation we propose new feature detection algorithms using a combination of topological and statistical methods. In particular, we show how to approximate topological structures from hixel data, extract structures from multi-modal distributions, and render uncertain isosurfaces. In all three cases we demonstrate how using hixels compares to traditional techniques and provide new capabilities to recover prominent features that would otherwise be either infeasible to compute or ambiguous to infer. We use a collection of computer tomography data and large scale combustion simulations to illustrate our techniques.
AB - 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 paper introduces a 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". Based on this new representation we propose new feature detection algorithms using a combination of topological and statistical methods. In particular, we show how to approximate topological structures from hixel data, extract structures from multi-modal distributions, and render uncertain isosurfaces. In all three cases we demonstrate how using hixels compares to traditional techniques and provide new capabilities to recover prominent features that would otherwise be either infeasible to compute or ambiguous to infer. We use a collection of computer tomography data and large scale combustion simulations to illustrate our techniques.
KW - Ensemble Visualization
KW - Petascale Visualization
KW - Scalar Field Data
KW - Topology-Based Techniques
UR - http://www.scopus.com/inward/record.url?scp=84055192862&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84055192862&partnerID=8YFLogxK
U2 - 10.1109/LDAV.2011.6092313
DO - 10.1109/LDAV.2011.6092313
M3 - Conference contribution
AN - SCOPUS:84055192862
SN - 9781467301541
T3 - 1st IEEE Symposium on Large-Scale Data Analysis and Visualization 2011, LDAV 2011 - Proceedings
SP - 23
EP - 30
BT - 1st IEEE Symposium on Large-Scale Data Analysis and Visualization 2011, LDAV 2011 - Proceedings
T2 - 1st IEEE Symposium on Large-Scale Data Analysis and Visualization 2011, LDAV 2011
Y2 - 23 October 2011 through 24 October 2011
ER -