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Interactive Visual Analysis of Scientific Data

Steffen Oeltze, Helmut Doleisch, Helwig Hauser, Gunther Weber

MISC, October, 2012

Abstract

In a growing number of application areas, a subject or phenomenon is investigated by means of multiple datasets being acquired over time (spatiotemporal), comprising several attributes per data point (multi-variate), stemming from different data sources (multi-modal) or multiple simulation runs (multirun/ensemble). Interactive visual analysis (IVA) comprises concepts and techniques for a user-guided knowledge discovery in such complex data. Through a tight feedback loop of computation, visualization and user interaction, it provides new insight into the data and serves as a vehicle for hypotheses generation or validation. It is often implemented via a multiple coordinated view framework where each view is equipped with interactive drill-down operations for focusing on data features. Two classes of views are integrated: physical views show information in the context of the spatiotemporal observation space while attribute views show relationships between multiple data attributes. The user may drill-down the data by selecting interesting regions of the observation space or attribute ranges leading to a consistent highlighting of this selection in all other views (brushing-and-linking). In this tutorial, we discuss examples for successful applications of IVA to scientific data from various fields: automotive engineering, climate research, biology, and medicine. We base our discussions on a theoretical foundation of IVA which helps the tutorial attendees in transferring the subject matter to their own data and application area. This universally applicable knowledge is complemented in a tutorial part on IVA of very large data which accounts for the tera- and petabytes being generated by simulations and experiments in many areas of science, e.g., physics, astronomy, and climate research. The tutorial further provides an overview of off-the-shelf IVA solutions. It is concluded by a summary of the gained knowledge and a discussion of open problems in IVA of scientific data. The tutorial slides will be available before the conference start date at: www.vismd.de/doku.php?id=teaching_tutorials:start.

Published

Tutorial at the IEEE VisWeek 2012

Media

  • presentation
  • www
  • Click to view

BibTeX

@misc{Hauser12VisTutorial,
 author = {Steffen Oeltze and Helmut Doleisch and Helwig Hauser and Gunther Weber},
 title  ={Interactive Visual Analysis of Scientific Data},
 year = {2012},
 month = {October},
 howpublished = {Tutorial at the IEEE VisWeek 2012},
 location = {Seattle (WA), USA},
 abstract = {In a growing number of application areas, a subject or phenomenon is 
  investigated by means of multiple datasets being acquired over time (spatiotemporal), 
  comprising several attributes per data point (multi-variate), stemming from different 
  data sources (multi-modal) or multiple simulation runs (multirun/ensemble). Interactive 
  visual analysis (IVA) comprises concepts and techniques for a user-guided knowledge 
  discovery in such complex data. Through a tight feedback loop of computation, visualization 
  and user interaction, it provides new insight into the data and serves as a vehicle for 
  hypotheses generation or validation. It is often implemented via a multiple coordinated 
  view framework where each view is equipped with interactive drill-down operations for 
  focusing on data features. Two classes of views are integrated: physical views show 
  information in the context of the spatiotemporal observation space while attribute views 
  show relationships between multiple data attributes. The user may drill-down the data by 
  selecting interesting regions of the observation space or attribute ranges leading to a 
  consistent highlighting of this selection in all other views (brushing-and-linking).

  In this tutorial, we discuss examples for successful applications of IVA to scientific 
  data from various fields: automotive engineering, climate research, biology, and medicine.
  We base our discussions on a theoretical foundation of IVA which helps the tutorial 
  attendees in transferring the subject matter to their own data and application area. 
  This universally applicable knowledge is complemented in a tutorial part on IVA of 
  very large data which accounts for the tera- and petabytes being generated by simulations 
  and experiments in many areas of science, e.g., physics, astronomy, and climate research. 
  The tutorial further provides an overview of off-the-shelf IVA solutions. It is concluded 
  by a summary of the gained knowledge and a discussion of open problems in IVA of 
  scientific data.

  The tutorial slides will be available before the conference start date at: 
  www.vismd.de/doku.php?id=teaching_tutorials:start.},
 url ={http://visweek.org/visweek/2012/tutorial/interactive-visual-analysis-scientific-data},


}






 Last Modified: Jean-Paul Balabanian, 2013-11-18