January 21 – 26 , 2018, Dagstuhl Seminar 18041

Foundations of Data Visualization


Helwig Hauser (University of Bergen, NO)
Penny Rheingans (University of Maryland, Baltimore County, US)
Gerik Scheuermann (Universität Leipzig, DE)

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Michael Gerke for scientific matters

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Data visualization is the transformation of observed or simulated data into usually interactive images. It is an indispensable part of knowledge discovery processes in many fields of contemporary endeavor. The strength of data visualization is the effective and efficient utilization of the broad bandwidth of the human sensory system. This Dagstuhl Seminar focuses on the foundations of data visualization, contributing to the fundamental understanding of generic methodologies in data visualization, including theories, models, workflows, evaluation metrics, perceptual and usability studies. Experts from all areas of data visualization, including scientific visualization, information visualization, and visual analytics, engage in an in-depth discussion, based on their broad expertise.

Rapid advances in data visualization have resulted in many useful visual designs, algorithms, software tools, and development kits. There is also a substantial body of work on mathematical approaches in data visualizations such as topological methods, feature extraction, and information theoretical results. A unified description of theoretical and perceptual aspects of visualization would allow visualization practitioners to derive even better solutions using a sound theoretical basis. There are promising ideas but they need further discussion. Currently, visualization uses user studies to decide if a visual design is more effective, but a comprehensive theory would allow visualization researchers to answer why one visual design is more effective than another and how the visual design can be optimized. Furthermore, there is usually an understanding of the role of a specific visualization in a specific analytic workflow, but a formalization of the general role of visualization in the analytic workflow is needed. This may also allow for more quantitative measures of visualization quality. In addition, the community needs a deeper, general understanding of the most informative way to conduct perceptual and usability studies involving domain experts. The following four foundation aspects will be of special interest:

  • Theory of the visualization process. A theory of the entire visualization process needs to cover all parts of the visualization pipeline and should be applicable to a large variety of application domains. Such a theory would be the ultimate foundation, and there are a few attempts already that should be discussed. Such a theory should enable to find optimal visualizations and to quantify the value of visualizations. In addition, such a theory may cover the challenge of uncertainty, the processing including visual mapping and potential misinterpretation by humans.
  • Foundations of evaluation. Evaluation allows designers to select visualization approaches among different options for a specific problem. One method is a user study, usually with a larger number of subjects. If only a small set of experts is available, who understand the questions behind the data, we need new study design guidelines. In addition, evaluation needs to look at limits of the human visual system. In advanced analytic applications, it is also very important to study the relation between user interest and visualization.
  • Application viewpoint. Almost all visualizations address questions and needs from researchers, engineers, analysts, decision makers, or the general public. Therefore, visualization nearly always involves people outside the visualization community. The seminar will discuss methodologies for defining domain requirements and realistic roles of application researchers in evaluation.
  • Mathematical foundations of visual data analysis. There is a rich tradition of mathematical and computational methods used in visualization, such as topological approaches, feature extraction, numerical sampling and reconstruction methods, numerical integration, differential operators, filtering, dimension reduction, and applications of information theory, partly incorporating uncertainty. While all these methods have a solid mathematical foundation, a careful look at the relation between theories and their role in visual data analysis is needed.

  Creative Commons BY 3.0 DE
  Helwig Hauser, Penny Rheingans, and Gerik Scheuermann

Dagstuhl Seminar Series


  • Computer Graphics / Computer Vision
  • Society / Human-computer Interaction


  • Data Visualization
  • Scientific Visualization
  • Information Visualization
  • Visual Analytics

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