2017
Dutta, Soumya; Woodring, Jon; Shen, Han-Wei; Chen, Jen-Ping; Ahrens, James
Homogeneity guided probabilistic data summaries for analysis and visualization of large-scale data sets Proceedings Article
In: 2017 IEEE Pacific Visualization Symposium (PacificVis), pp. 111-120, 2017, ISSN: 2165-8773, (LA-UR-18-27370).
Abstract | Links | BibTeX | Tags: data visualization, picture/image generation, statistical computing
@inproceedings{8031585,
title = {Homogeneity guided probabilistic data summaries for analysis and visualization of large-scale data sets},
author = {Soumya Dutta and Jon Woodring and Han-Wei Shen and Jen-Ping Chen and James Ahrens},
url = {https://datascience.dsscale.org/wp-content/uploads/2018/08/la-ur_18-27370.pdf},
doi = {10.1109/PACIFICVIS.2017.8031585},
issn = {2165-8773},
year = {2017},
date = {2017-04-01},
booktitle = {2017 IEEE Pacific Visualization Symposium (PacificVis)},
pages = {111-120},
abstract = {High-resolution simulation data sets provide plethora of information, which needs to be explored by application scientists to gain enhanced understanding about various phenomena. Visual-analytics techniques using raw data sets are often expensive due to the data sets' extreme sizes. But, interactive analysis and visualization is crucial for big data analytics, because scientists can then focus on the important data and make critical decisions quickly. To assist efficient exploration and visualization, we propose a new region-based statistical data summarization scheme. Our method is superior in quality, as compared to the existing statistical summarization techniques, with a more compact representation, reducing the overall storage cost. The quantitative and visual efficacy of our proposed method is demonstrated using several data sets along with an in situ application study for an extreme-scale flow simulation.},
note = {LA-UR-18-27370},
keywords = {data visualization, picture/image generation, statistical computing},
pubstate = {published},
tppubtype = {inproceedings}
}
High-resolution simulation data sets provide plethora of information, which needs to be explored by application scientists to gain enhanced understanding about various phenomena. Visual-analytics techniques using raw data sets are often expensive due to the data sets' extreme sizes. But, interactive analysis and visualization is crucial for big data analytics, because scientists can then focus on the important data and make critical decisions quickly. To assist efficient exploration and visualization, we propose a new region-based statistical data summarization scheme. Our method is superior in quality, as compared to the existing statistical summarization techniques, with a more compact representation, reducing the overall storage cost. The quantitative and visual efficacy of our proposed method is demonstrated using several data sets along with an in situ application study for an extreme-scale flow simulation.
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1.
Dutta, Soumya; Woodring, Jon; Shen, Han-Wei; Chen, Jen-Ping; Ahrens, James
Homogeneity guided probabilistic data summaries for analysis and visualization of large-scale data sets Proceedings Article
In: 2017 IEEE Pacific Visualization Symposium (PacificVis), pp. 111-120, 2017, ISSN: 2165-8773, (LA-UR-18-27370).
@inproceedings{8031585,
title = {Homogeneity guided probabilistic data summaries for analysis and visualization of large-scale data sets},
author = {Soumya Dutta and Jon Woodring and Han-Wei Shen and Jen-Ping Chen and James Ahrens},
url = {https://datascience.dsscale.org/wp-content/uploads/2018/08/la-ur_18-27370.pdf},
doi = {10.1109/PACIFICVIS.2017.8031585},
issn = {2165-8773},
year = {2017},
date = {2017-04-01},
booktitle = {2017 IEEE Pacific Visualization Symposium (PacificVis)},
pages = {111-120},
abstract = {High-resolution simulation data sets provide plethora of information, which needs to be explored by application scientists to gain enhanced understanding about various phenomena. Visual-analytics techniques using raw data sets are often expensive due to the data sets' extreme sizes. But, interactive analysis and visualization is crucial for big data analytics, because scientists can then focus on the important data and make critical decisions quickly. To assist efficient exploration and visualization, we propose a new region-based statistical data summarization scheme. Our method is superior in quality, as compared to the existing statistical summarization techniques, with a more compact representation, reducing the overall storage cost. The quantitative and visual efficacy of our proposed method is demonstrated using several data sets along with an in situ application study for an extreme-scale flow simulation.},
note = {LA-UR-18-27370},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
High-resolution simulation data sets provide plethora of information, which needs to be explored by application scientists to gain enhanced understanding about various phenomena. Visual-analytics techniques using raw data sets are often expensive due to the data sets' extreme sizes. But, interactive analysis and visualization is crucial for big data analytics, because scientists can then focus on the important data and make critical decisions quickly. To assist efficient exploration and visualization, we propose a new region-based statistical data summarization scheme. Our method is superior in quality, as compared to the existing statistical summarization techniques, with a more compact representation, reducing the overall storage cost. The quantitative and visual efficacy of our proposed method is demonstrated using several data sets along with an in situ application study for an extreme-scale flow simulation.