Browsing EuroVA2020 by Title

Now showing items 1-15 of 15

    • Congnostics: Visual Features for Doubly Time Series Plots 

      Nguyen, Bao Dien Quoc; Hewett, Rattikorn; Dang, Tommy (The Eurographics Association, 2020)
      In this paper, we propose an analytical approach to automatically extract visual features from doubly time series capturing the unusual associations which are not otherwise possible by investigating individual time series ...

    • Dual Radial Set 

      Matkovic, Kresimir; Gracanin, Denis; Bardun, Matea; Splechtna, Rainer; Hauser, Helwig (The Eurographics Association, 2020)
      Set-typed data visualizations require novel interactive representations, especially when visualizing multiple set-typed data attributes. The challenge is how to effectively analyze relations between data elements from ...

    • DualNetView: Dual Views for Visualizing the Dynamics of Networks 

      Pham, Vung; Nguyen, V. T. Ngan; Dang, Tommy (The Eurographics Association, 2020)
      The force-directed layout is a popular visual method for revealing network structures, such as clusters and important vertices. However, it is not capable of representing temporal patterns, such as how clusters/communities ...

    • Enhanced Attribute-Based Explanations of Multidimensional Projections 

      Driel, Daan van; Zhai, Xiaorui; Tian, Zonglin; Telea, Alexandru (The Eurographics Association, 2020)
      Multidimensional projections (MPs) are established tools for exploring the structure of high-dimensional datasets to reveal groups of similar observations. For optimal usage, MPs can be augmented with mechanisms that explain ...

    • EuroVa 2020: Frontmatter 

      Turkay, Cagatay; Vrotsou, Katerina (The Eurographics Association, 2020)

    • An Exploratory Visual Analytics Tool for Multivariate Dynamic Networks 

      Boz, Hasan Alp; Bahrami, Mohsen; Suhara, Yoshihiko; Bozkaya, Burcin; Balcisoy, Selim (The Eurographics Association, 2020)
      Visualizing multivariate dynamic networks is a challenging task. The evolution of the dynamic network within the temporal axis must be depicted in conjunction with the associated multivariate attributes. In this paper, an ...

    • A Generic Model for Projection Alignment Applied to Neural Network Visualization 

      Cantareira, Gabriel Dias; Paulovich, Fernando V. (The Eurographics Association, 2020)
      Dimensionality reduction techniques are popular tools for the visualization of neural network models due to their ability to display hidden layer activations and aiding the understanding of how abstract representations are ...

    • Interactive Visualization of AI-based Speech Recognition Texts 

      Wu, Tsung Heng; Zhao, Ye; Amiruzzaman, Md (The Eurographics Association, 2020)
      Speech recognition technology has achieved impressive success recently with AI techniques of deep learning networks. Speechto- text tools are becoming prevalent in many social applications such as field surveys. However, ...

    • Learning and Teaching in Co-Adaptive Guidance for Mixed-Initiative Visual Analytics 

      Sperrle, Fabian; Jeitler, Astrik; Bernard, Jürgen; Keim, Daniel A.; El-Assady, Mennatallah (The Eurographics Association, 2020)
      Guidance processes in visual analytics applications often lack adaptivity. In this position paper, we contribute the concept of co-adaptive guidance, building on the principles of initiation and adaptation. We argue that ...

    • Progressive Parameter Space Visualization for Task-Driven SAX Configuration 

      Loeschcke, Sebastian; Hogräfer, Marius; Schulz, Hans-Jörg (The Eurographics Association, 2020)
      As time series datasets are growing in size, data reduction approaches like PAA and SAX are used to keep them storable and analyzable. Yet, finding the right trade-off between data reduction and remaining utility of the ...

    • Quality Metrics to Guide Visual Analysis of High Dimensional Genomics Data 

      Fernstad, Sara Johansson; Macquisten, Alexander; Berrington, Janet; Embleton, Nicholas; Stewart, Christopher (The Eurographics Association, 2020)
      Studies of genome sequenced data are increasingly common in many domains. Technological advances enable detection of hundreds of thousands of biological entities in samples, resulting in extremely high dimensional data. ...

    • SepEx: Visual Analysis of Class Separation Measures 

      Bernard, Jürgen; Hutter, Marco; Zeppelzauer, Matthias; Sedlmair, Michael; Munzner, Tamara (The Eurographics Association, 2020)
      Class separation is an important concept in machine learning and visual analytics. However, the comparison of class separation for datasets with varying dimensionality is non-trivial, given a) the various possible structural ...

    • SpatialRugs: Enhancing Spatial Awareness of Movement in Dense Pixel Visualizations 

      Buchmüller, Juri F.; Schlegel, Udo; Cakmak, Eren; Keim, Daniel A.; Dimara, Evanthia (The Eurographics Association, 2020)
      Compact visual summaries of spatio-temporal movement data often strive to express accurate positions of movers. We present SpatialRugs, a technique to enhance the spatial awareness of movements in dense pixel visualizations. ...

    • Visual Analysis for Hospital Infection Control using a RNN Model 

      Müller, Martin; Petzold, Markus; Wunderlich, Marcel; Baumgartl, Tom; Höhn, Markus; Eichel, Vanessa; Mutters, Nico T.; Scheithauer, Simone; Marschollek, Michael; Landesberger, Tatiana von (The Eurographics Association, 2020)
      Bacteria and viruses are transmitted among patients in the hospital. Infection control experts develop strategies for infection control. Currently, this is done mostly manually, which is time-consuming and error-prone. ...

    • A Window-based Approach for Mining Long Duration Event-sequences 

      Vrotsou, Katerina; Nordman, Aida (The Eurographics Association, 2020)
      This paper presents an interactive sequence mining approach for exploring long duration event-sequences and identifying interesting patterns within them. The approach extends previous work on exploratory sequence mining ...