Browsing by Author "Stitz, Holger"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    A Process Model for Dashboard Onboarding
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Dhanoa, Vaishali; Walchshofer, Conny; Hinterreiter, Andreas; Stitz, Holger; Gröller, Eduard; Streit, Marc; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
    Dashboards are used ubiquitously to gain and present insights into data by means of interactive visualizations. To bridge the gap between non-expert dashboard users and potentially complex datasets and/or visualizations, a variety of onboarding strategies are employed, including videos, narration, and interactive tutorials. We propose a process model for dashboard onboarding that formalizes and unifies such diverse onboarding strategies. Our model introduces the onboarding loop alongside the dashboard usage loop. Unpacking the onboarding loop reveals how each onboarding strategy combines selected building blocks of the dashboard with an onboarding narrative. Specific means are applied to this narration sequence for onboarding, which results in onboarding artifacts that are presented to the user via an interface. We concretize these concepts by showing how our process model can be used to describe a selection of real-world onboarding examples. Finally, we discuss how our model can serve as an actionable blueprint for developing new onboarding systems.
  • Thumbnail Image
    Item
    Visualization of Rubik's Cube Solution Algorithms
    (The Eurographics Association, 2019) Steinparz, Christian Alexander; Hinterreiter, Andreas; Stitz, Holger; Streit, Marc; Landesberger, Tatiana von and Turkay, Cagatay
    Rubik's Cube is among the world's most famous puzzle toys. Despite its relatively simple principle, it requires dedicated, carefully planned algorithms to be solved. In this paper, we present an approach to visualize how different solution algorithms navigate through the high-dimensional space of Rubik's Cube states. We use t-distributed stochastic neighbor embedding (t-SNE) to project feature vector representations of cube states to two dimensions. t-SNE preserves the similarity of cube states and leads to clusters of intermediate states and bundles of cube solution pathways in the projection. Our prototype implementation allows interactive exploration of differences between algorithms, showing detailed state information on demand.