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DOI: 10.4230/DFU.Vol2.SciViz.2011.118

Information-theoretic Analysis of Unsteady Data

Authors: Heike Jänicke

Published in: Dagstuhl Follow-Ups, Volume 2, Scientific Visualization: Interactions, Features, Metaphors (2011)

Abstract

The temporal evolution of scientific data is of high relevance in many fields of application. Understanding the dynamics over time is a crucial step in understanding the underlying system. The availability of large scale parallel computers has led to a finer and finer resolution of simulation data, which makes it difficult to detect all relevant changes of the system by watching a video or a set of snapshots. In recent years, algorithms for the automatic detection of coherent temporal structures have been developed that allow for an identification of interesting areas and time steps in unsteady data. With such techniques, the user can be guided to interesting subsets of the data or a video can be automatically created that does not occlude relevant aspects of the simulation. In this paper, we give an overview over the different techniques, show how their combination helps to gain deeper insight and look at different directions for further improvement. Two CFD simulations are used to illustrate the different techniques.

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Heike Jänicke. Information-theoretic Analysis of Unsteady Data. In Scientific Visualization: Interactions, Features, Metaphors. Dagstuhl Follow-Ups, Volume 2, pp. 118-128, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2011)

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@InCollection{janicke:DFU.Vol2.SciViz.2011.118,
  author =	{J\"{a}nicke, Heike},
  title =	{{Information-theoretic Analysis of Unsteady Data}},
  booktitle =	{Scientific Visualization: Interactions, Features, Metaphors},
  pages =	{118--128},
  series =	{Dagstuhl Follow-Ups},
  ISBN =	{978-3-939897-26-2},
  ISSN =	{1868-8977},
  year =	{2011},
  volume =	{2},
  editor =	{Hagen, Hans},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DFU.Vol2.SciViz.2011.118},
  URN =		{urn:nbn:de:0030-drops-32908},
  doi =		{10.4230/DFU.Vol2.SciViz.2011.118},
  annote =	{Keywords: Information theory, unsteady data}
}

Document

DOI: 10.4230/DFU.SciViz.2010.62

Towards Automatic Feature-based Visualization

Authors: Heike Jänicke and Gerik Scheuermann

Published in: Dagstuhl Follow-Ups, Volume 1, Scientific Visualization: Advanced Concepts (2010)

Abstract

Visualizations are well suited to communicate large amounts of complex data. With increasing resolution in the spatial and temporal domain simple imaging techniques meet their limits, as it is quite difficult to display multiple variables in 3D or analyze long video sequences. Feature detection techniques reduce the data-set to the essential structures and allow for a highly abstracted representation of the data. However, current feature detection algorithms commonly rely on a detailed description of each individual feature. In this paper, we present a feature-based visualization technique that is solely based on the data. Using concepts from computational mechanics and information theory, a measure, local statistical complexity, is defined that extracts distinctive structures in the data-set. Local statistical complexity assigns each position in the (multivariate) data-set a scalar value indicating regions with extraordinary behavior. Local structures with high local statistical complexity form the features of the data-set. Volume-rendering and iso-surfacing are used to visualize the automatically extracted features of the data-set. To illustrate the ability of the technique, we use examples from diffusion, and flow simulations in two and three dimensions.

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Heike Jänicke and Gerik Scheuermann. Towards Automatic Feature-based Visualization. In Scientific Visualization: Advanced Concepts. Dagstuhl Follow-Ups, Volume 1, pp. 62-77, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2010)

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@InCollection{janicke_et_al:DFU.SciViz.2010.62,
  author =	{J\"{a}nicke, Heike and Scheuermann, Gerik},
  title =	{{Towards Automatic Feature-based Visualization}},
  booktitle =	{Scientific Visualization: Advanced Concepts},
  pages =	{62--77},
  series =	{Dagstuhl Follow-Ups},
  ISBN =	{978-3-939897-19-4},
  ISSN =	{1868-8977},
  year =	{2010},
  volume =	{1},
  editor =	{Hagen, Hans},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DFU.SciViz.2010.62},
  URN =		{urn:nbn:de:0030-drops-26976},
  doi =		{10.4230/DFU.SciViz.2010.62},
  annote =	{Keywords: Feature Detection Techniques, Feature-based Visualization, Local Statistical Complexity}
}

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Documents authored by Jänicke, Heike

Information-theoretic Analysis of Unsteady Data

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Towards Automatic Feature-based Visualization

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