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

Visualization and Visual Analysis of 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

Multi-faceted Scientific Data  Spatiotemporal data  Multi-variate/multi-field data (multiple data attributes, e.g., multi-run distribution temperature or pressure) per cell  Multi-modal data (CT, MRI, large-scale measurements, simulations, etc.)  Multi-run/ensemble simulations (repeated with varied parameter settings)  Multi-model scenarios 3D time-dependent (e.g., coupled climate model) simulation data 2 J. Kehrer & H. Hauser Visualization and Analysis of Multi-faceted Scientific Data

Multi-faceted Scientific Data Coupled climate models [ Böttinger, ClimaVis08 ] Land J. Kehrer & H. Hauser Visualization and Analysis of Multi-faceted Scientific Data

Categorization  Literature review of 200+ papers on scientific data  How are vis., interaction, and comput. analysis combined? what are main interaction concepts how to represent characteristics / (linking & brushing, zooming, the data features view reconfiguration, etc.) interactive visual analysis visual mapping comput. analysis relation & focus+context & data abstraction comparison overview+detailinteractive & aggregation visual data fusion navigation feature spec. [compare to Keim et al. 2009; Bertine & Lalanne 2009] 4 J. Kehrer & H. Hauser Visualization and Analysis of Multi-faceted Scientific Data

visual mapping interactive visual analysis comput. analysis relation & focus+context & data abstraction comparison overview+detail interactive & aggregation visual data fusion navigation feature spec. Fusion within a single visualization  common frame of reference  layering techniques (e.g., glyphs, color, transparencey)  multi-volume rendering (coregistration, segmentation) spatiotemporal multi-variate multi-modal Helix glyphs [Tominski et al. 05] Layering [Kirby et al. 99] Multi-volume rendering [Beyer et al. 07] 5 J. Kehrer & H. Hauser Visualization and Analysis of Multi-faceted Scientific Data

visual mapping interactive visual analysis comput. analysis relation & focus+context & data abstraction comparison overview+detail interactive & aggregation visual data fusion navigation feature spec. Fusion of multiple simulation runs  spaghetti plots [Diggle et al. 02]  summary statistics (box plots and glyphs) multi-run multi-run isocontours EnsembleVis [Potter et al. 09] Glyph-based overview [Kehrer et al. 11] 6 J. Kehrer & H. Hauser Visualization and Analysis of Multi-faceted Scientific Data

visual mapping interactive visual analysis comput. analysis relation & focus+context & data abstraction comparison overview+detail interactive & aggregation visual data fusion navigation feature spec. Fusion of multiple simulation runs q 3  spaghetti plots [Diggle et al. 02] q 2 q 1  summary statistics (box plots and glyphs) multi-run multi-run isocontours EnsembleVis [Potter et al. 09] Glyph-based overview [Kehrer et al. 11] 7 J. Kehrer & H. Hauser Visualization and Analysis of Multi-faceted Scientific Data

visual mapping interactive visual analysis comput. analysis relation & focus+context & data abstraction overview+detail comparison interactive & aggregation visual data fusion navigation feature spec. spatiotemporal Taxonomy [Gleicher et al. 2011]  side-by-side comparison  overlay in same coordinate system  explicit encoding of differences / correlations spatiotemporal multi-modal 2-tone coloring [Saito et al. 05] Nested surfaces [Buskin et al. 11] Difference views [Lampe et al.] 8 J. Kehrer & H. Hauser Visualization and Analysis of Multi-faceted Scientific Data

visual mapping interactive visual analysis comput. analysis relation & focus+context & data abstraction comparison overview+detail interactive & aggregation navigation visual data fusion feature spec. [Johansson & Johansson 09] multi-variate  Interactive search, zooming, and panning  Ranking/quality metrics [Bertini et al. 2011]  clustering, correlations, outliers, image quality, etc.  Automated viewpoint selection  information-theoretic measures [Viola et al. 06] segmented volume data 9 J. Kehrer & H. Hauser Visualization and Analysis of Multi-faceted Scientific Data

visual mapping interactive visual analysis comput. analysis relation & focus+context & data abstraction comparison overview+detail interactive & aggregation navigation visual data fusion feature spec. Parameter space navigation (multi-run data) input output focal variations focal point variations point [Berger et al. 11] 10 J. Kehrer & H. Hauser Visualization and Analysis of Multi-faceted Scientific Data

visual mapping interactive visual analysis comput. analysis focus+context & relation & data abstraction overview+detail interactive comparison & aggregation navigation visual data fusion feature spec.  Overview+detail representation of multi-run data summary statistics multi-run data quantile plot Brushing statistical moments [Kehrer et al. 10] 11 J. Kehrer & H. Hauser Visualization and Analysis of Multi-faceted Scientific Data

visual mapping interactive visual analysis comput. analysis relation & focus+context & data abstraction overview+detail interactive comparison & aggregation navigation feature spec. visual data fusion  Brushing in multiple linked views  Tight integration with supervised machine learning multi-variate alternative hypotheses Visual human+machine learning [Fuchs et al. 09] 12 J. Kehrer & H. Hauser Visualization and Analysis of Multi-faceted Scientific Data

visual mapping interactive visual analysis comput. analysis relation & focus+context & data abstraction overview+detail interactive comparison & aggregation navigation feature spec. visual data fusion Fluid-structure interactions (multi-model data)  heat exchange between fluid  structure  feature specification/transfer across data parts [Kehrer et al. 11] cooler aluminum foam feature transfer feature 13 J. Kehrer & H. Hauser Visualization and Analysis of Multi-faceted Scientific Data

visual mapping interactive visual analysis comput. analysis relation & focus+context & data abstraction comparison overview+detail interactive & aggregation navigation visual data fusion feature spec. spatiotemporal Algorithmically extract values & patterns  dimensionality reduction (PCA, SOM, MDS)  aggregation, summary statistics  clustering, outliers, etc. multi-run clustering of multi-run simulations [Bruckner & Möller 10] [Andrienko & Andrienko 11] 14 J. Kehrer & H. Hauser Visualization and Analysis of Multi-faceted Scientific Data

Categorization of approaches 15 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 16 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 visual mapping interactive visual analysis comput. analysis relation & focus+context & data abstraction comparison overview+detail interactive & aggregation visual data fusion navigation feature spec.  Promising data facets, e.g., multi-run & multi-model data 17 J. Kehrer & H. Hauser Visualization and Analysis of Multi-faceted Scientific Data

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)