Visualization and Visual Analysis of Multi-faceted Scientific Data: - - PowerPoint PPT Presentation

Visualization and Visual Analysis

• f Multi-faceted Scientific Data:

A Survey

Johannes Kehrer 1,2 and Helwig Hauser 1

1 Department of Informatics,

University of Bergen, Norway

2 VRVis Research Center, Vienna, Austria

• J. Kehrer & H. Hauser

Visualization and Analysis of Multi-faceted Scientific Data

 Spatiotemporal data  Multi-variate/multi-field data

(multiple data attributes, e.g., temperature or pressure)

 Multi-modal data

(CT, MRI, large-scale measurements, simulations, etc.)

 Multi-run/ensemble simulations (repeated with varied

parameter settings)

 Multi-model scenarios (e.g., coupled climate model)

Multi-faceted Scientific Data

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multi-run distribution per cell 3D time-dependent simulation data

• J. Kehrer & H. Hauser

Visualization and Analysis of Multi-faceted Scientific Data

[ Böttinger, ClimaVis08 ]

Land

Multi-faceted Scientific Data

Coupled climate models

• J. Kehrer & H. Hauser

Visualization and Analysis of Multi-faceted Scientific Data

 Literature review of 200+ papers on scientific data  How are vis., interaction, and comput. analysis combined?

Categorization

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[compare to Keim et al. 2009; Bertine & Lalanne 2009] what are main characteristics / features data abstraction & aggregation how to represent the data visual data fusion

visual mapping

• comput. analysis

relation & comparison navigation focus+context &

• verview+detailinteractive

feature spec. interaction concepts (linking & brushing, zooming, view reconfiguration, etc.)

interactive visual analysis

• J. Kehrer & H. Hauser

Visualization and Analysis of Multi-faceted Scientific Data

Fusion within a single visualization

 common frame of reference  layering techniques (e.g., glyphs, color, transparencey)  multi-volume rendering (coregistration, segmentation)

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Helix glyphs [Tominski et al. 05] Layering [Kirby et al. 99] Multi-volume rendering [Beyer et al. 07]

visual mapping

• comput. analysis

interactive visual analysis

visual data fusion

relation & comparison focus+context &

• verview+detail

navigation interactive feature spec. data abstraction & aggregation

spatiotemporal multi-modal multi-variate

• J. Kehrer & H. Hauser

Visualization and Analysis of Multi-faceted Scientific Data

Fusion of multiple simulation runs

 spaghetti plots [Diggle et al. 02]  summary statistics (box plots and glyphs)

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EnsembleVis [Potter et al. 09]

visual mapping

• comput. analysis

interactive visual analysis

visual data fusion

relation & comparison focus+context &

• verview+detail

navigation interactive feature spec. data abstraction & aggregation Glyph-based overview [Kehrer et al. 11]

multi-run multi-run

isocontours

• J. Kehrer & H. Hauser

Visualization and Analysis of Multi-faceted Scientific Data

Fusion of multiple simulation runs

 spaghetti plots [Diggle et al. 02]  summary statistics (box plots and glyphs)

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EnsembleVis [Potter et al. 09]

visual mapping

• comput. analysis

interactive visual analysis

visual data fusion

relation & comparison focus+context &

• verview+detail

navigation interactive feature spec. data abstraction & aggregation Glyph-based overview [Kehrer et al. 11]

multi-run multi-run q1 q2 q3

isocontours

• J. Kehrer & H. Hauser

Visualization and Analysis of Multi-faceted Scientific Data

Taxonomy [Gleicher et al. 2011]

 side-by-side comparison  overlay in same coordinate system  explicit encoding of differences / correlations

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visual mapping

• comput. analysis

interactive visual analysis

visual data fusion

relation & comparison

navigation focus+context &

• verview+detail

interactive feature spec. data abstraction & aggregation 2-tone coloring [Saito et al. 05] Nested surfaces [Buskin et al. 11] Difference views [Lampe et al.]

spatiotemporal multi-modal spatiotemporal

• J. Kehrer & H. Hauser

Visualization and Analysis of Multi-faceted Scientific Data

[Viola et al. 06]

segmented volume data

 Interactive search, zooming, and panning  Ranking/quality metrics

[Bertini et al. 2011]

 clustering, correlations,

• utliers, image quality, etc.

 Automated viewpoint selection

 information-theoretic measures

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visual mapping

• comput. analysis

interactive visual analysis

relation & comparison

navigation

focus+context &

• verview+detail

interactive feature spec. data abstraction & aggregation visual data fusion [Johansson & Johansson 09]

multi-variate

• J. Kehrer & H. Hauser

Visualization and Analysis of Multi-faceted Scientific Data 10

visual mapping

• comput. analysis

interactive visual analysis

relation & comparison

navigation

focus+context &

• verview+detail

interactive feature spec. data abstraction & aggregation visual data fusion

variations focal point input

• utput

variations

Parameter space navigation (multi-run data)

[Berger et al. 11]

focal point

• J. Kehrer & H. Hauser

Visualization and Analysis of Multi-faceted Scientific Data 11

visual mapping

• comput. analysis

interactive visual analysis

visual data fusion relation & comparison navigation

focus+context &

• verview+detail interactive

feature spec. data abstraction & aggregation

 Overview+detail representation of multi-run data

Brushing statistical moments [Kehrer et al. 10]

multi-run data summary statistics

quantile plot

• J. Kehrer & H. Hauser

Visualization and Analysis of Multi-faceted Scientific Data

 Brushing in multiple linked views  Tight integration with supervised machine learning

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visual mapping

• comput. analysis

interactive visual analysis

visual data fusion relation & comparison navigation focus+context &

• verview+detail interactive

feature spec.

data abstraction & aggregation Visual human+machine learning [Fuchs et al. 09]

multi-variate

alternative hypotheses

• J. Kehrer & H. Hauser

Visualization and Analysis of Multi-faceted Scientific Data

Fluid-structure interactions (multi-model data)

 heat exchange between fluid  structure  feature specification/transfer across data parts [Kehrer et al. 11]

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visual mapping

• comput. analysis

interactive visual analysis

visual data fusion relation & comparison navigation focus+context &

• verview+detail interactive

feature spec.

data abstraction & aggregation

feature transfer

cooler aluminum foam feature

• J. Kehrer & H. Hauser

Visualization and Analysis of Multi-faceted Scientific Data

Algorithmically extract values & patterns

 dimensionality reduction (PCA, SOM, MDS)  aggregation, summary statistics  clustering, outliers, etc.

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visual mapping

• comput. analysis

interactive visual analysis

visual data fusion relation & comparison navigation focus+context &

• verview+detail

interactive feature spec.

data abstraction & aggregation

clustering of multi-run simulations [Bruckner & Möller 10] [Andrienko & Andrienko 11]

multi-run spatiotemporal

• J. Kehrer & H. Hauser

Visualization and Analysis of Multi-faceted Scientific Data

Categorization of approaches

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• J. Kehrer & H. Hauser

Visualization and Analysis of Multi-faceted Scientific Data

Open Issues

 How to deal with data heterogeneity?

 most approaches only address one or two data facet  coordinated multiple views with linking & brushing  investigation of features across views, data facets, levels of abstraction, and data sets  fusion of heterogeneous data at feature/semantic level

 Combination of vis., interaction, and comput. analysis

 analytical methods can controll steps in visualization pipeline (e.g., visualization mapping or quality metrics)  interactive feature specification + machine learning

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• J. Kehrer & H. Hauser

Visualization and Analysis of Multi-faceted Scientific Data

Conclusions

 Scientific data are becomming multi-faceted  Categorization based on common visualization, interaction, and comput. analysis methods  Promising data facets, e.g., multi-run & multi-model data

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visual mapping

• comput. analysis

interactive visual analysis

visual data fusion relation & comparison focus+context &

• verview+detail

navigation interactive feature spec. data abstraction & aggregation

Thank you for your attention!

Acknowledgements

• H. Schumann, M. Chen, T. Nocke,

VisGroup in Bergen, H. Piringer, M.E. Gröller

Supported in part by the Austrian Funding Agency (FFG)