Activity Detection for scientific visualization

Understanding the science behind ultra-scale simulations requires extracting meaning from data sets of hundreds of terabytes or more. At extreme scales, the data sets are so huge, there is not even enough time to view the data, let alone explore it with basic visualization methods. Automated tools a...

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Veröffentlicht in:2011 IEEE Symposium on Large Data Analysis and Visualization S. 117 - 118
Hauptverfasser: Ozer, S., Silver, D., Bemis, K., Martin, P., Takle, J.
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.10.2011
Schlagworte:
Action
Activity detection
Computational modeling
Data models
Data visualization
Event Detection
Feature extraction
Petri nets
Solid modeling
Three dimensional displays
ISBN:9781467301565, 1467301566
Online-Zugang:Volltext
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Abstract Understanding the science behind ultra-scale simulations requires extracting meaning from data sets of hundreds of terabytes or more. At extreme scales, the data sets are so huge, there is not even enough time to view the data, let alone explore it with basic visualization methods. Automated tools are necessary for knowledge discovery to help sift through the information and isolate characteristic patterns, thereby enabling the scientist to study local interactions, the origin of features, and their evolution, i.e. activity detection in large volumes of 3D data. Defining and modelling such activities in 3D scientific data sets remains an open research problem, though it has been widely studied in the computer vision community. In this work we demonstrate how utilizing activity detection can help us model and detect complex events (activities) in large 3D scientific data sets. We employ Petri nets which support distributed and discrete graphical modelling of spatio-temporal patterns to model activities in time-varying 3D scientific data sets. We demonstrate the use of Petri nets on three different data sets.
AbstractList Understanding the science behind ultra-scale simulations requires extracting meaning from data sets of hundreds of terabytes or more. At extreme scales, the data sets are so huge, there is not even enough time to view the data, let alone explore it with basic visualization methods. Automated tools are necessary for knowledge discovery to help sift through the information and isolate characteristic patterns, thereby enabling the scientist to study local interactions, the origin of features, and their evolution, i.e. activity detection in large volumes of 3D data. Defining and modelling such activities in 3D scientific data sets remains an open research problem, though it has been widely studied in the computer vision community. In this work we demonstrate how utilizing activity detection can help us model and detect complex events (activities) in large 3D scientific data sets. We employ Petri nets which support distributed and discrete graphical modelling of spatio-temporal patterns to model activities in time-varying 3D scientific data sets. We demonstrate the use of Petri nets on three different data sets.
Author Martin, P.
Silver, D.
Bemis, K.
Ozer, S.
Takle, J.
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  surname: Silver
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  organization: Dept. of Electr. & Comput. Eng., Rutgers Univ., Piscataway, NJ, USA
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  surname: Bemis
  fullname: Bemis, K.
  organization: Dept. of Earth & Planetary Sci., Rutgers Univ., Piscataway, NJ, USA
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  surname: Martin
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  organization: Dept. of Aerosp. Eng., Univ. Maryland, College Park, MD, USA
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  givenname: J.
  surname: Takle
  fullname: Takle, J.
  organization: Dept. of Electr. & Comput. Eng., Rutgers Univ., Piscataway, NJ, USA
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Snippet Understanding the science behind ultra-scale simulations requires extracting meaning from data sets of hundreds of terabytes or more. At extreme scales, the...
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StartPage 117
SubjectTerms Action
Activity detection
Computational modeling
Data models
Data visualization
Event Detection
Feature extraction
Petri nets
Solid modeling
Three dimensional displays
Title Activity Detection for scientific visualization
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