Monday, June 4th

08:00 – 09:00 Registration

09:00 – 09:10 Welcome to EuroRV³

(Location: Hall B)

09:10 – 10:40 Opening Keynote

(Location: Hall B, Chair: Kai Lawonn)

  • Making Uncertainties Explicit
    • Hans-Christian Hege – Head of the Visual Data Analysis Department at Zuse Institute Berlin (ZIB), Germany
    • Abstract: Data comes either from measurements that directly capture properties of reality, or from simulations that provide properties of models that represent the parts of reality. All data, with a few exceptions, is subject to uncertainties. In the computational processes during data analysis additional uncertainties might creep in. When drawing conclusions from data, e.g. when testing hypotheses or making decisions, significant uncertainties need to be considered. In visualizations, such uncertainties should therefore be indicated or, if desired by the user, presented in detail. This requires two basic capabilities: (i) quantification of uncertainties and (ii) visualization of quantified uncertainties. The presentation discusses the different types of uncertainties and provides a brief overview of formal means of representing and quantifying uncertainties. It will be explained, how uncertainties propagate along the visualization pipeline and where additional uncertainties might slip in. Examples will be presented of how data afflicted with uncertainties can be visualized. Finally, various challenges in visually supported analysis of uncertain data will be discussed.

10:40 – 11:10 Coffee Break

11:10 – 12:50 Session 1

(Location: Hall B, Chair: Robert Kosara)

  • Invited talk: Visualizing Temporal Uncertainty (slides)
    • Theresia Gschwandtner – Scientific researcher at the Visual Analytics group, Institute of Visual Computing and Human-Centered Technology, TU Wien. 
    • Abstract: Real world datasets often contain some amount of uncertainty. This is especially true for time series data which might contain uncertainties about the timing of past and future events. Simply neglecting these uncertainties when visualizing data might result in wrong interpretations and misjudgements of the viewer. However, it is still not clear which techniques are best suited to visualize temporal uncertainties, what representations are best understood and intuitive, and if the explicit visualization of temporal uncertainty information is beneficial at all. In this talk, I will present examples of past, present, and future work, and I will outline different research challenges in the field of temporal uncertainty visualization.​
  • Visual Analytics-enabled Bayesian Network Approach to Reasoning about Public Safety Data (slides)
    • Ekaterina Chuprikova, Alan MacEachren, Juliane Cron, and Liqiu Meng
  • Visualizing Uncertainty in Cultural Heritage Collections (slides)
    • Florian Windhager, Velitchko Andreev Filipov, Saminu Salisu, and Eva Mayr
  • Uncertainty Visualization: Recent Developments and Future Challenges in Prostate Cancer Radiotherapy Planning (slides)
    • Renata Raidou

12:50 – 14:20 Lunch Break

14:20 – 16:00 Session 2

(Location: Hall B, Chair: Noeska Smit)

  • Invited talk:  Perception, Comparison, and Models for Uncertainty (slides)
    • Michael Gleicher – Professor at the Department of Computer Sciences, University of Wisconsin, Madison, USA
  • Towards Visualizing Subjective Uncertainty: A Conceptual Framework Addressing Perceived Uncertainty through Action Randomness
    • Wei Li, Mathias Funk,  and Aarnout Brombacher
  • Uncertainty of Visualizations for SenseMaking in Criminal Intelligence Analysis (slides)
    • Junayed Islam, Kai Xu, and William Wong

16:30 – 18:00 Closing Keynote

(Location: Hall B, Chair: Lars Linsen)

  • Ensemble Visualization – Visualizing the uncertainty that is represented by an ensemble of fields (slides)
    • Rüdiger Westermann – Head of the chair for Computer Graphics and Visualization at Technische Universität München, Germany 
    • Abstract: Each member of an ensemble simulation shows a possible occurrence of one or several physical fields, and domain experts are concerned with analyzing the uncertainty that is represented by these fields. Due to the sheer volume of such ensembles, their inherent spatial and temporal aspects, as well as the complex spatio-temporal relations between features in these fields, classical data mining and statistical analysis techniques become increasingly limited. While simple analysis tasks, like finding commonalities or differences at fixed locations in space and time, can be realized in an automated way, a meaningful and intuitive depiction of the uncertainty that is carried by an ensemble is challenging. When directional quantities and spatio-temporal relations between ensemble members have to be analyzed, the limitations of available techniques become even more severe and new approaches are required. In this talk I will shed light on the relation between ensemble and uncertainty visualization, and I will discuss a variety of visualization techniques for scalar- and vector-valued ensemble fields. This is followed by a summary of current and future challenges in ensemble visualization.

18:00 – 18:10 Closing

(Location: Hall B)