The State-of-the-Art of Set Visualization

• Published in: Computer graphics forum 2016-02, Vol.35 (1), p.234-260
• Main Authors: Alsallakh, Bilal, Micallef, Luana, Aigner, Wolfgang, Hauser, Helwig, Miksch, Silvia, Rodgers, Peter
• Format: Article
• Language: English
• Subjects: Analysis; Categories; Classification; Collection; Data analysis; Data processing; F.4.1 [Theory of Computation]: Mathematical Logic-Set theory; H.5.2 [Information Interfaces and Presentation]: User Interfaces-Graphical user interfaces; Image processing systems; information visualization; Representations; State of the art; Studies; Tasks; Visual; Visualization
• ISSN: 0167-7055; 1467-8659
• DOI: 10.1111/cgf.12722

Sets comprise a generic data model that has been used in a variety of data analysis problems. Such problems involve analysing and visualizing set relations between multiple sets defined over the same collection of elements. However, visualizing sets is a non‐trivial problem due to the large number of possible relations between them. We provide a systematic overview of state‐of‐the‐art techniques for visualizing different kinds of set relations. We classify these techniques into six main categories according to the visual representations they use and the tasks they support. We compare the categories to provide guidance for choosing an appropriate technique for a given problem. Finally, we identify challenges in this area that need further research and propose possible directions to address these challenges. Further resources on set visualization are available at http://www.setviz.net. Sets comprise a generic data model that has been used in a variety of data analysis problems. Such problems involve analysing and visualizing set relations between multiple sets defined over the same collection of elements. However, visualizing sets is a non‐trivial problem due to the large number of possible relations between them. We provide a systematic overview of state‐of‐the‐art techniques for visualizing different kinds of set relations.We classify these techniques into six main categories according to the visual representations they use and the tasks they support. We compare the categories to provide guidance for choosing an appropriate technique for a given problem.