EuroVisPosters2021

Permanent URI for this collection

https://diglib.eg.org/handle/10.2312/2633049

Browse

Browsing EuroVisPosters2021 by Subject "Visual analytics"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Elastic Tree Layouts for Interactive Exploration of Mentorship
    (The Eurographics Association, 2021) Yan, Xin Yuan; Ma, Yi Fang; Byška, Jan and Jänicke, Stefan and Schmidt, Johanna
    Mentorship is an important collaborative relationship among scholars. The existing tools to visualize it mainly suffer from a waste of space, lack of overview representation, and less displayed attribute information. To solve these problems, we propose a novel elastic tree layout based on node-link diagrams, in which nodes and edges are represented as elastic rectangles and bands respectively. By stretching, compressing, aggregating, and expanding nodes and edges, we can: get a compact tree layout with high space-efficiency, display both the detailed subtree and compressed context in a single view, use labeling, charts, and node opacity to show multiple attributes. Besides, we designed various animated interactions to facilitate the exploration.
  • Thumbnail Image
    Item
    SimBaTex: Similarity-based Text Exploration
    (The Eurographics Association, 2021) Witschard, Daniel; Jusufi, Ilir; Kerren, Andreas; Byška, Jan and Jänicke, Stefan and Schmidt, Johanna
    Natural language processing in combination with visualization can provide efficient ways to discover latent patterns of similarity which can be useful for exploring large sets of text documents. In this poster abstract, we describe the ongoing work on a visual analytics application, called SimBaTex, which is based on embedding technology, dynamic specification of similarity criteria, and a novel approach for similarity-based clustering. The goal of SimBaTex is to provide search-and-explore functionality to enable the user to identify items of interest in a large set of text documents by interactive assessment of both high-level similarity patterns and pairwise similarity of chosen texts.