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User-based visual-interactive similarity definition for mixed data objects - concept and first implementation

: Bernard, Jürgen; Sessler, David; Ruppert, Tobias; Davey, James; Kuijper, Arjan; Kohlhammer, Jörn

Skala, Vaclav (Ed.) ; European Association for Computer Graphics -EUROGRAPHICS-:
22nd International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, WSCG 2014. Communication Papers Proceedings : June 2 - June 5, 2015, Plzen
Pilsen: University of West Bohemia, 2014
ISBN: 978-80-86943-71-8
International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG) <22, 2014, Plzen, Czech Republic>
Fraunhofer IGD ()
similarity measures; user-centered design; feature selection; information visualization; Visual analytics; mixed data sets; user/machine systems; graphical interactive user interfaces; graphical user interfaces (GUI); Human-computer interaction (HCI); user guidance; visual feedback; multidimensional data visualization; Business Field: Digital society; Business Field: Visual decision support; Research Area: (Interactive) Simulation (SIM); Research Area: Human Computer Interaction (HCI); Research Area: Modeling (MOD)

The definition of similarity between data objects plays a key role in many analytical systems. The process of similarity definition comprises several challenges as three main problems occur: different stakeholders, mixed data, and changing requirements. Firstly, in many applications the developers of the analytical system (data scientists) model the similarity, while the users (domain experts) have distinct (mental) similarity notions. Secondly, the definition of similarity for mixed data types is challenging. Thirdly, many systems use static similarity models that cannot adapt to changing data or user needs. We present a concept for the development of systems that support the visual-interactive similarity definition for mixed data objects emphasizing 15 crucial steps. For each step different design considerations and implementation variants are presented, revealing a large design space. Moreover, we present a first implementation of our concept, enabling domain experts to express mental similarity notions through a visual-interactive system. The provided implementation tackles the different-stakeholders problem, the mixed data problem, and the changing requirements problem. The implementation is not limited to a specific mixed data set. However, we show the applicability of our implementation in a case study where a functional similarity model is trained for countries as objects.