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Activity detection in scientific visualization
Descriptive
TitleInfo
Title
Activity detection in scientific visualization
Name
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Ozer
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Sedat
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1979-
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Sedat Ozer
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author
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Silver
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Deborah
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Deborah Silver
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Advisory Committee
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chair
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Parashar
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Manish
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Manish Parashar
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Advisory Committee
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internal member
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(type = family)
Jha
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Shantenu
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Shantenu Jha
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Advisory Committee
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internal member
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Ma
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Kwan-Liu
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Kwan-Liu Ma
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Advisory Committee
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outside member
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Rutgers University
Role
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degree grantor
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Graduate School - New Brunswick
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school
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Text
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theses
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DateCreated
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2013
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2013-10
Place
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xx
Language
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eng
Abstract
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Today’s state of the art simulations generate high-resolution data at an ever-increasing rate. Such simulations produce data with billions of mesh points (or voxels) for each timestep and thousands of such timesteps with multiple variables. Time-varying data can easily reach peta- and exa-byte scale. Visualizing these massive data sets is still an on-going problem. Even after visualizing this data, viewing each variable at each timestep is practically impossible when there are thousands of timesteps. Simulations become too complex for the scientist to analyze manually. In such time-varying data sets, scientists want to know “where and when events happen” or “how long an event lasts”. Finding these events in thousands of timesteps is not possible with standard visualization tools. What scientists need are routines, procedures and visualizing techniques to help filter massive data and help focus on areas and events of interest automatically. The problems facing any attempt to localize complex events (activities) automatically in time-varying 3D scientific data can be summarized as: (1) provide an appropriate way for users to define an event of interest; (2) find an appropriate formalism to model this event; (3) apply the model to detect many instances of the event of interest in simulation data; and (4) present the detected events to users in an appropriate visual form. The contributions in this dissertation include introduction of the concept of activity detection for scientific visualization, the use of Petri Nets to model and detect activities in scientific visualization, an enhancement of Petri Nets to include the dynamics of scientific phenomena and demonstration of the use of activity detection on three different 3D time-varying data sets as case studies. In addition, a full 3D group-tracking model in which we extract and track groups as well as the individual features that form them is presented.
Subject
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Topic
Electrical and Computer Engineering
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Title
Rutgers University Electronic Theses and Dissertations
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ETD_5138
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electronic resource
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application/pdf
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text/xml
Extent
vii, 172 p. : ill.
Note
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Ph.D.
Note
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Includes bibliographical references
Note
(type = statement of responsibility)
by Sedat Ozer
Subject
(authority = ETD-LCSH)
Topic
Information visualization
Subject
(authority = ETD-LCSH)
Topic
Software visualization
Subject
(authority = ETD-LCSH)
Topic
Computer simulation
RelatedItem
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Title
Graduate School - New Brunswick Electronic Theses and Dissertations
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rucore19991600001
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doi:10.7282/T3NC5Z7D
Genre
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ETD doctoral
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Rights
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The author owns the copyright to this work.
RightsHolder
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Name
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Ozer
GivenName
Sedat
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Permission or license
DateTime
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2013-10-02 21:41:53
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Name
Sedat Ozer
Role
Copyright holder
Affiliation
Rutgers University. Graduate School - New Brunswick
AssociatedObject
Type
License
Name
Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
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DateTime
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2013-10-31
DateTime
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2014-10-31
Type
Embargo
Detail
Access to this PDF has been restricted at the author's request. It will be publicly available after October 31st, 2014.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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windows xp
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