Visualizing extreme-scale data

Kwan-Liu Ma

doi:10.1117/12.786401

Visualizing extreme-scale data

Kwan-Liu Ma

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

The ability to extract meaning from the huge amounts of data obtained from simulations, experiments, sensors, or the world wide web gives one tremendous advantage over others in the respective area of business or study. Visualization becomes a hot topic because it enables that ability. As data size is growing from terascale to petascale and exascale, new visualization techniques must be developed and integrated into data analysis tools and problem solving environments so the collected data can be fully exploited. In this talk, I will point out a few important directions for advancing the visualization technology, which include parallel visualization, knowledge-assisted visualization, intelligent visualization, and in situ visualization. I will use some of the projects we have done at UC Davis in my discussion.

Original language	English (US)
Title of host publication	Proceedings of SPIE-IS and T Electronic Imaging - Visualization and Data Analysis 2008
Volume	6809
DOIs	https://doi.org/10.1117/12.786401
State	Published - May 15 2008
Event	Visualization and Data Analysis 2008 - San Jose, CA, United States Duration: Jan 28 2008 → Jan 29 2008

Other

Other	Visualization and Data Analysis 2008
Country	United States
City	San Jose, CA
Period	1/28/08 → 1/29/08

Keywords

Data analysis
Data compression
Feature extraction and tracking
Machine learning
Parallel computing
Visualization

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Condensed Matter Physics
Computer Science Applications
Applied Mathematics
Electrical and Electronic Engineering

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title = "Visualizing extreme-scale data",

abstract = "The ability to extract meaning from the huge amounts of data obtained from simulations, experiments, sensors, or the world wide web gives one tremendous advantage over others in the respective area of business or study. Visualization becomes a hot topic because it enables that ability. As data size is growing from terascale to petascale and exascale, new visualization techniques must be developed and integrated into data analysis tools and problem solving environments so the collected data can be fully exploited. In this talk, I will point out a few important directions for advancing the visualization technology, which include parallel visualization, knowledge-assisted visualization, intelligent visualization, and in situ visualization. I will use some of the projects we have done at UC Davis in my discussion.",

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language = "English (US)",

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N2 - The ability to extract meaning from the huge amounts of data obtained from simulations, experiments, sensors, or the world wide web gives one tremendous advantage over others in the respective area of business or study. Visualization becomes a hot topic because it enables that ability. As data size is growing from terascale to petascale and exascale, new visualization techniques must be developed and integrated into data analysis tools and problem solving environments so the collected data can be fully exploited. In this talk, I will point out a few important directions for advancing the visualization technology, which include parallel visualization, knowledge-assisted visualization, intelligent visualization, and in situ visualization. I will use some of the projects we have done at UC Davis in my discussion.

AB - The ability to extract meaning from the huge amounts of data obtained from simulations, experiments, sensors, or the world wide web gives one tremendous advantage over others in the respective area of business or study. Visualization becomes a hot topic because it enables that ability. As data size is growing from terascale to petascale and exascale, new visualization techniques must be developed and integrated into data analysis tools and problem solving environments so the collected data can be fully exploited. In this talk, I will point out a few important directions for advancing the visualization technology, which include parallel visualization, knowledge-assisted visualization, intelligent visualization, and in situ visualization. I will use some of the projects we have done at UC Davis in my discussion.

KW - Data analysis

KW - Data compression

KW - Feature extraction and tracking

KW - Machine learning

KW - Parallel computing

KW - Visualization

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DO - 10.1117/12.786401

M3 - Conference contribution

AN - SCOPUS:43249101397

SN - 9780819469816

VL - 6809

BT - Proceedings of SPIE-IS and T Electronic Imaging - Visualization and Data Analysis 2008

T2 - Visualization and Data Analysis 2008

Y2 - 28 January 2008 through 29 January 2008

ER -

Visualizing extreme-scale data

Abstract

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Keywords

ASJC Scopus subject areas

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