Interactive Visual Analysis of Heterogeneous Scientific Data across an Interface
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1 Interactive Visual Analysis of Heterogeneous Scientific Data across an Interface Johannes Kehrer, 1 Philipp Muigg, 2,3 Helmut Doleisch, 2 and Helwig Hauser 1 1 Department of Informatics, University of Bergen, Norway 2 SimVis GmbH, Vienna, Austria 3 Institute of Computer Graphics & Algorithms, Vienna University of Technology, Austria
2 Heterogeneous Scientific Data Single-part scenarios given in a coherent form (spatio-temporal / multi-variate) Multi-part scenarios 2 or more related data parts different simulation models different data sources various data grids different dimensionality (2D/3D data) time-dependent 3D data coupled climate model Johannes Kehrer 1
3 Multi-physics Simulations Example: fluid-structure interactions (FSIs) movable or deformable structure fluid flexible roofs, bridges, blood flow in arteries, etc. [Bungartz & Schäfer 2006] cooler aluminum foam heat exchange Johannes Kehrer 2
4 Sample Analysis of Heat Transfer How is the heating (solid) influenced by surrounding vortical flow? feature transfer vortex feature Johannes Kehrer 3
5 Multi-part Scenarios (cont.) Coupled climate models Land [ Böttinger, ClimaVis08 ] Johannes Kehrer 4
6 Multi-part Scenarios (cont.) Data in scientific visualization data values f(x) (e.g., temperature, pressure values) measured/simulated wrt. a domain x (e.g., 2D/3D space, time, simulation input parameters) 3D time-dependent multi-run simulation data data distribution per cell Dimensionality reduction (e.g., computing statistics wrt. time / spatial axes) Reintegrate statistics into IVA through attribute derivation Johannes Kehrer 5
7 Visual Analysis across an Interface Multiple linked views framework [Doleisch et al. 03] Integrate 2 related data parts Common level of data abstraction degree-of-interest attribution (DOI i [0, 1]) data part 1 data part 2 feature transfer Johannes Kehrer 6
8 Interface Relate grid cells across data parts (no resampling necessary) DOI transfer Automatic update of feature spec. data part 1 data part 2 feature transfer Analysis strategies for 2-part scenarios data part 1 data part 2 feature refinement Johannes Kehrer 7
9 Interface (structural relation) relate grid cells weight values for DOI transfer many-to-many relation aggregated multi-run data one-to-many relation Johannes Kehrer 8
10 Degree-of-Interest (DOI) Transfer weighted sum maximum weighted DOI values maximum DOI value other model-based transfer Johannes Kehrer 9
11 Case Study: Multi-run climate Data Cooling event (8200 yrs ago) ocean simulation (2D sections) 10 x 10 = 100 runs time-dependent (250 time steps) Compute statistics wrt. the multiple runs median, quartiles, etc. billboard glyphs [Lie et al. 2009] year 100 size: IQR Johannes Kehrer
12 Case Study: Multi-run climate Data Cooling event (8200 yrs ago) ocean simulation q 3 (2D sections) q 2 10 x 10 = 100 runs q 1 time-dependent (250 time steps) Compute statistics wrt. the multiple runs median, quartiles, etc. billboard glyphs [Lie et al. 2009] year 100 size: IQR Johannes Kehrer
13 Visual Analysis of Multi-run Data aggreg. multi-run data Starts at aggregated level (summary statistics) specify features via brushing derive new attributes Refine features at multi-run level (details) Investigate further aggregated data multi-run data Johannes Kehrer 12
14 Visual Analysis of Multi-run Data aggreg. multi-run data Starts at aggregated level (summary statistics) specify features via brushing derive new attributes Refine features at multi-run level (details) Investigate further aggregated data multi-run data Johannes Kehrer 13
15 Outlier Analysis: Aggregated Data Derive total number of outlier Where are outlier located? (% outlier > 3 rd quartile IQR) (% outlier < 1 st quartile 1.5 IQR) multi-run data year 60 aggregated data aggregated data
16 Feature Refinement: Multi-run Data Derive measure of outlyingness [Kehrer et al. 2010] aggr. multi-run data Johannes Kehrer 15
17 Feature Refinement: Multi-run Data Derive measure of outlyingness [Kehrer et al. 2010] aggr. multi-run data Johannes Kehrer 16
18 Sensitivity Analysis aggregated data multi-run data multi-run data 17
19 Conclusions Joint visual analysis across two data parts Bidirectional feature transfer via interface Workflow for analyzing 2 data parts simultaneously multi-run data aggregated statistics fluid structure Aggregated statistics and measures of outlyingness Johannes Kehrer 18
20 Acknowledgements Thomas Nocke, Michael Flechsig Peter Filzmoser Andreas Lie, Stian Eikeland, Cagatay Turkay, Armin Pobitzer Matthew Parker, Brendan McNulty, David Horn FSI data courtesy of Innovative Computational Engineering GmbH VisGroup Bergen many others Johannes Kehrer 19
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