Information Visualization (InfoVis) can help humans gain insight from large volumes of data by providing graphical overviews as well as appropriate interfaces for accessing details. Sets, which are mathematically defined as collections of distinct objects, play an important role in InfoVis since reasoning based on grouped information can reduce the complexity of data analysis tasks. Most often, sets are defined by categories of objects; e.g., people can be categorized by country of residence, education, or gender to study influences on income. Often, the aim is to visualize statistics (e.g., number of elements, average income) for each set and, since an element can be member of multiple sets, the relationships between them (e.g., intersection and containment). Set visualization is traditionally done with Venn or Euler diagrams, yet a plethora of alternative visualization types for sets has been developed. A recent focus of research has been on developing scalable solutions (e.g., to create effective visualizations for very large set systems) and dealing with dynamics (e.g., changes of the elements’ set memberships over time). In this Dagstuhl Seminar we deal with a different issue: uncertainty. We aim to develop approaches to deal with uncertain information when visualizing sets. Although the importance of uncertainty visualization has been stressed by several researchers, only few studies exist that deal with it specifically in the context of sets and systems of sets.

Some existing uncertainty visualization techniques may directly apply to set visualizations. However, we argue that an interdisciplinary approach, considering uncertainty at any stage of dealing with sets, will lead to more useful and usable set visualizations. Key challenges include uncertainty-aware set visualizations, visualizing uncertainty in sets, modeling uncertainty for set visualizations, geographical set visualizations under relaxed geometrical constraints, and reasoning with such set visualizations including uncertainty. Addressing these challenges will require design and efficient generation of visualizations that are informative and reliable, in the sense that humans can use them for visual analysis tasks and that the uncertain information is recognizable.

The seminar aims to advance research into methods and techniques for set visualizations and uncertainty by fostering interdisciplinary and cross-domain collaboration. It aims to bring together researchers from the areas of information visualization, visual analytics, graph drawing, computational geometry, geoinformation science, uncertainty research, and cognitive science.

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Susanne Bleisch, Steven Chaplick, Jan-Henrik Haunert, Eva Mayr, and Marc van Kreveld