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Knowledge-Assisted Visualization and Guidance

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Foundations of Data Visualization

Abstract

Visualization envisions to intertwine the strengths of humans and computers for effective interactive visual and analytic data analysis and exploration. To this end, humans’ tacit/implicit knowledge from prior experience is an important asset that can be leveraged by both human and computer to improve the visual and analytic exploration processes. However, acquiring, structuring, formalizing, storing, and utilizing implicit and explicit knowledge within the whole visualization process are provocative and widely-discussed research challenge. This chapter elaborates on (1) knowledge-assisted visualization, which aims to incorporate implicit and explicit knowledge as well as information-theoretical considerations into the visualization process to support users for decision making and (2) guidance, which is a computer-assisted process that aims to actively resolve a knowledge gap encountered by users during an interactive visualization session. This chapter ends with critical reflections about applicability, usability, and utility of the proposed knowledge enhanced visualization processes.

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Acknowledgements

The authors would like to thank Davide Ceneda, Theresia Gschwandtner, Thorsten May, Hans-Jörg Schulz, Marc Streit, Christian Tominski, and all participants of the WG group at the Dagstuhl Seminar for constructive and fruitful discussions and thank the reviewers for their invaluable feedback. This work was funded by the “Austrian Science Fund (FWF), grant P31419-N31”.

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Authors and Affiliations

  1. TU Wien, Vienna, Austria

    Silvia Miksch

  2. TU Kaiserslautern, Kaiserslautern, Germany

    Heike Leitte

  3. University of Oxford, Oxford, UK

    Min Chen

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  1. Silvia Miksch
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  2. Heike Leitte
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  3. Min Chen
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Correspondence to Silvia Miksch .

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Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Min Chen

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

    Helwig Hauser

  3. University of Maryland, Baltimore County, Baltimore, MD, USA

    Penny Rheingans

  4. Institut für Informatik, Universität Leipzig, Leipzig, Germany

    Gerik Scheuermann

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Miksch, S., Leitte, H., Chen, M. (2020). Knowledge-Assisted Visualization and Guidance. In: Chen, M., Hauser, H., Rheingans, P., Scheuermann, G. (eds) Foundations of Data Visualization. Springer, Cham. https://doi.org/10.1007/978-3-030-34444-3_4

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  • DOI: https://doi.org/10.1007/978-3-030-34444-3_4

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