August 13 – 18 , 2017, Dagstuhl Seminar 17332

Scalable Set Visualizations


Yifan Hu (Yahoo! Research – New York, US)
Luana Micallef (Aalto University, FI)
Martin Nöllenburg (TU Wien, AT)
Peter Rodgers (University of Kent – Canterbury, GB)

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Sets are a fundamental way of organizing data. From mathematical logic to the results of automated gene analysis, sets are used because they intuitively represent the way data is structured. Information visualization is key to gaining insight into data, as the human perceptual system is an analytic system of enormous power. As a result, there has been a recent proliferation of automated visualization methods for set-based data. An important goal for researchers in set visualization is to develop visually and computationally scalable methods to address the challenge of interpreting large data sets.

The goal of this seminar is to bring together researchers with different backgrounds but a shared interest in set visualization. It will involve computer scientists with expertise, e.g., in visualization, algorithms, and human-computer interaction, but also users of set visualizations from domains outside computer science. Despite the large number of set visualization techniques, for which there is often a considerable practical and theoretical understanding of their capabilities, there has only been limited success in scaling these methods. This seminar starts with a few overview talks on the state of the art in set visualization, but then focuses on small hands-on working groups during most of the seminar week. We aim to accelerate the efforts to improve scalability of set visualizations by addressing open questions proposed by the seminar attendees, in order to produce concrete research outcomes, including new set visualization software and peer-reviewed research publications.

Topics to be addressed include:

Algorithms. Effective and efficient algorithms are required to generate accurate and comprehensible visualizations of large set-based data, and to allow smooth interaction with the data for analysis and exploration. Various algorithms have been devised, but more work is needed in areas including:

  • Scaling overlay techniques;
  • Exploring colouring and defragmentation in map-based techniques;
  • Developing new interaction techniques.

Theory. Theoretical findings on the drawability and readability of set visualizations help in selecting an appropriate technique and optimizing its visual design and layout. Findings are needed in subjects including:

  • Limits on the drawability of different set visualization techniques, starting off from Venn and Euler diagrams;
  • Measures and models predicting the readability of layouts as the number of sets and their intersections increase;
  • Measures and computational methods predicting user performance in completing data analysis tasks for different set visualization techniques with a large number of sets and set intersections.

Evaluation. Both laboratory experiments and crowd-sourced experiments have been used to evaluate the effectiveness of set visualizations. The methodologies for evaluation of set-based visualizations will be explored including:

  • Defining relevant tasks and mapping data and task to visualization;
  • Effective use of perceptual, cognitive and HCI theories;
  • Evaluation of new visual metrics quantifying the effectiveness of a set visualization for a specific task and user traits.

Application Areas and Users. Set visualizations are designed to both communicate key aspects of information, and to derive understanding from data. Hence, the visualizations are typically targeted at non-computer scientists. This means engagement with users is thus crucial. Example user focused topics include:

  • Examining improved visualizations for biosciences data;
  • Developing visualization techniques for social network and security applications;
  • Generating more accurate visualizations for the results of medical studies.

  Creative Commons BY 3.0 DE
  Yifan Hu, Luana Micallef, Martin Nöllenburg, and Peter Rodgers


  • Computer Graphics / Computer Vision
  • Data Structures / Algorithms / Complexity
  • Society / Human-computer Interaction


  • Information visualization
  • Set visualization
  • Visual analytics
  • Cognition and graphical perception
  • Geometric algorithms

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