Group dynamics in scientific visualization

Sedat Ozer; Jishang Wei; Deborah Silver; Kwan-Liu Ma; Pino Martin

doi:10.1109/LDAV.2012.6378982

Group dynamics in scientific visualization

Sedat Ozer, Jishang Wei, Deborah Silver, Kwan-Liu Ma, Pino Martin

Computer Science

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

12 Scopus citations

Abstract

The ability to visually extract and track features is appealing to scientists in many simulations including flow fields. However, as the resolution of the simulation becomes higher, the number of features to track increases and so does the cost in large-scale simulations. Since many of these features act in groups, it seems more cost-effective to follow groups of features rather than individual ones. Very little work has been done for tracking groups of features. In this paper, we present the first full group tracking framework in which we track groups (clusters) of features in time-varying 3D fluid flow simulations. Our framework uses a clustering algorithm to group interacting features. We demonstrate the use of our framework on data output from a 3D simulation of wall bounded turbulent flow.

Original language	English (US)
Title of host publication	IEEE Symposium on Large Data Analysis and Visualization 2012, LDAV 2012 - Proceedings
Pages	97-104
Number of pages	8
DOIs	https://doi.org/10.1109/LDAV.2012.6378982
State	Published - Dec 1 2012
Event	2nd Symposium on Large-Scale Data Analysis and Visualization, LDAV 2012 - Seattle, WA, United States Duration: Oct 14 2012 → Oct 19 2012

Other

Other	2nd Symposium on Large-Scale Data Analysis and Visualization, LDAV 2012
Country/Territory	United States
City	Seattle, WA
Period	10/14/12 → 10/19/12

Keywords

clustering
Feature tracking
group tracking
grouping
packet identification
scientific visualization

ASJC Scopus subject areas

Computer Vision and Pattern Recognition
Information Systems

Access to Document

10.1109/LDAV.2012.6378982

Cite this

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title = "Group dynamics in scientific visualization",

abstract = "The ability to visually extract and track features is appealing to scientists in many simulations including flow fields. However, as the resolution of the simulation becomes higher, the number of features to track increases and so does the cost in large-scale simulations. Since many of these features act in groups, it seems more cost-effective to follow groups of features rather than individual ones. Very little work has been done for tracking groups of features. In this paper, we present the first full group tracking framework in which we track groups (clusters) of features in time-varying 3D fluid flow simulations. Our framework uses a clustering algorithm to group interacting features. We demonstrate the use of our framework on data output from a 3D simulation of wall bounded turbulent flow.",

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author = "Sedat Ozer and Jishang Wei and Deborah Silver and Kwan-Liu Ma and Pino Martin",

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AU - Wei, Jishang

AU - Silver, Deborah

AU - Ma, Kwan-Liu

AU - Martin, Pino

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Y1 - 2012/12/1

N2 - The ability to visually extract and track features is appealing to scientists in many simulations including flow fields. However, as the resolution of the simulation becomes higher, the number of features to track increases and so does the cost in large-scale simulations. Since many of these features act in groups, it seems more cost-effective to follow groups of features rather than individual ones. Very little work has been done for tracking groups of features. In this paper, we present the first full group tracking framework in which we track groups (clusters) of features in time-varying 3D fluid flow simulations. Our framework uses a clustering algorithm to group interacting features. We demonstrate the use of our framework on data output from a 3D simulation of wall bounded turbulent flow.

AB - The ability to visually extract and track features is appealing to scientists in many simulations including flow fields. However, as the resolution of the simulation becomes higher, the number of features to track increases and so does the cost in large-scale simulations. Since many of these features act in groups, it seems more cost-effective to follow groups of features rather than individual ones. Very little work has been done for tracking groups of features. In this paper, we present the first full group tracking framework in which we track groups (clusters) of features in time-varying 3D fluid flow simulations. Our framework uses a clustering algorithm to group interacting features. We demonstrate the use of our framework on data output from a 3D simulation of wall bounded turbulent flow.

KW - clustering

KW - Feature tracking

KW - group tracking

KW - grouping

KW - packet identification

KW - scientific visualization

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BT - IEEE Symposium on Large Data Analysis and Visualization 2012, LDAV 2012 - Proceedings

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Group dynamics in scientific visualization

Abstract

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Keywords

ASJC Scopus subject areas

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