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Interactive Visual Exploration and Analysis

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Scientific Visualization

Part of the book series: Mathematics and Visualization ((MATHVISUAL))

Abstract

Interactive exploration and analysis of multi-field data utilizes a tight feedback loop of computation/visualization and user interaction to facilitate knowledge discovery in complex datasets. It does so by providing both overview visualizations, as well as support for focusing on features utilizing iterative drill-down operations. When exploring multi-field data, interactive exploration and analysis relies on a combination of the following concepts: (i) physical views that show information in the context of the spatiotemporal domain (domain perspective), (ii) range views show relationships between multiple fields (range perspective), and (iii) selecting/marking data subsets in one view (e.g., regions in a physical view) leading to a consistent highlighting of this subset in all other views (brushing and linking). Based on these principles, interactive exploration and analysis supports building complex feature definitions, e.g., using Boolean operations to combine multiple selections. Utilizing derived fields, statistical methods, etc., adds a further layer of flexibility to this approach. Using these concepts, it is also possible to integrate feature detection methods from the other chapters of this part, as well as application-specific feature extraction methods into an joint framework. This methodology of interactive visual data exploration and analysis has proven its potential in a larger number of successful applications. It has been implemented in a larger number of systems and is already available for a wide spectrum of different application domains.

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Notes

  1. 1.

    We note that the visualization community uses the order “linking-and-brushing” more commonly, while the database community uses the order “brushing-and-linking”. We use the term “brushing-and-linking” here as brushing is usually performed before linking.

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Acknowledgments

This work was supported by the Director, Office of Advanced Scientific Computing Research, Office of Science, of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. We thank the members of the LBNL visualization group and the Berkeley Drosophila Transcription Network. Special thanks are extended to Helmut Doleisch and colleagues from SimVis GmbH in Vienna, Austria, for the cooperation over many years. We thank Johannes Kehrer and Peter Filzmoser as well as Thomas Nocke, Michael Flechsig, and colleagues from the Potsdam Institute for Climate Impact Research in Germany for the collaboration on the climate data analysis. We also thank Krešimir Matković and colleagues from the VRVis Research Center in Vienna, Austria, for many years of fruitful collaboration on IVA research, as well as many others from Vienna, Bratislava, Magdeburg, Zürich, and Bergen.

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Authors and Affiliations

  1. Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, USA

    Gunther H. Weber

  2. Department of Computer Science, University of California, Davis, USA

    Gunther H. Weber

  3. Department of Informatics, University of Bergen, Bergen, Norway

    Helwig Hauser

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  1. Gunther H. Weber
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Correspondence to Gunther H. Weber .

Editor information

Editors and Affiliations

  1. School of Computing Scientific Computing and Imaging Institu, University of Utah, Salt Lake City, Utah, USA

    Charles D. Hansen

  2. e-Research Centre, University of Oxford, Oxford, United Kingdom

    Min Chen

  3. School of Computing Scientific Computing and Imaging Institu, University of Utah, Salt Lake City, Utah, USA

    Christopher R. Johnson

  4. Department of Computer Science, Stony Brook University, New York, New York, USA

    Arie E. Kaufman

  5. Fachbereich Informatik, Technische Universität Kaiserslautern, Kaiserslautern, Germany

    Hans Hagen

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Weber, G.H., Hauser, H. (2014). Interactive Visual Exploration and Analysis. In: Hansen, C., Chen, M., Johnson, C., Kaufman, A., Hagen, H. (eds) Scientific Visualization. Mathematics and Visualization. Springer, London. https://doi.org/10.1007/978-1-4471-6497-5_15

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