Activity detection for scientific visualization

Sedat Ozer, Deborah Silver, Karen Bemis, Pino Martin, Jay Takle

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

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.

Original languageEnglish (US)
Title of host publication1st IEEE Symposium on Large-Scale Data Analysis and Visualization 2011, LDAV 2011 - Proceedings
Pages117-118
Number of pages2
DOIs
StatePublished - 2011
Event1st IEEE Symposium on Large-Scale Data Analysis and Visualization 2011, LDAV 2011 - Providence, RI, United States
Duration: Oct 23 2011Oct 24 2011

Publication series

Name1st IEEE Symposium on Large-Scale Data Analysis and Visualization 2011, LDAV 2011 - Proceedings

Other

Other1st IEEE Symposium on Large-Scale Data Analysis and Visualization 2011, LDAV 2011
CountryUnited States
CityProvidence, RI
Period10/23/1110/24/11

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Computer Vision and Pattern Recognition

Keywords

  • Action
  • Activity detection
  • Event Detection
  • Petri Nets

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