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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Bibliographic Details
Published in:2011 IEEE Symposium on Large Data Analysis and Visualization pp. 117 - 118
Main Authors: Ozer, S., Silver, D., Bemis, K., Martin, P., Takle, J.
Format: Conference Proceeding
Language:English
Published: IEEE 01.10.2011
Subjects:
Action
Activity detection
Computational modeling
Data models
Data visualization
Event Detection
Feature extraction
Petri nets
Solid modeling
Three dimensional displays
ISBN:9781467301565, 1467301566
Online Access:Get full text
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Summary: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.
ISBN:9781467301565
1467301566
DOI:10.1109/LDAV.2011.6092327