Interactive Visual Exploration and Analysis

  • Gunther H. Weber
  • Helwig Hauser
Part of the Mathematics and Visualization book series (MATHVISUAL)


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.


Data Item Data Subset Feature Definition Data Derivation Interactive Exploration 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



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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Copyright information

© Springer International Publishing Switzerland (outside the USA) 2014 2014

Authors and Affiliations

  1. 1.Computational Research DivisionLawrence Berkeley National LaboratoryBerkeleyUSA
  2. 2.Department of Computer ScienceUniversity of CaliforniaDavisUSA
  3. 3.Department of InformaticsUniversity of BergenBergenNorway

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