Porto, Portugal 3 June 2019


Visual Analytics Methods
Visual Analysis of Degree-of-Interest Functions to Support Selection Strategies for Instance Labeling
Jürgen Bernard, Marco Hutter, Christian Ritter, Markus Lehmann, Michael Sedlmair, and Matthias Zeppelzauer
TourDino: A Support View for Confirming Patterns in Tabular Data
Klaus Eckelt, Patrick Adelberger, Thomas Zichner, Andreas Wernitznig, and Marc Streit
Deep Learning Inverse Multidimensional Projections
Mateus Espadoto, Francisco Caio Maia Rodrigues, Nina S. T. Hirata, Roberto Hirata Jr., and Alexandru C. Telea
Visualization of Rubik's Cube Solution Algorithms
Christian Alexander Steinparz, Andreas P. Hinterreiter, Holger Stitz, and Marc Streit
On Quality Indicators for Progressive Visual Analytics
Marco Angelini, Thorsten May, Giuseppe Santucci, and Hans-Jörg Schulz
Quantifying Uncertainty in Multivariate Time Series Pre-Processing
Christian Bors, Jürgen Bernard, Markus Bögl, Theresia Gschwandtner, Jörn Kohlhammer, and Silvia Miksch
Analyzing Movement and Events
Moving Together: Towards a Formalization of Collective Movement
Juri Buchmüller, Eren Cakmak, Natalia Andrienko, Gennady Andrienko, Jolle W. Jolles, and Daniel A. Keim
Visually Analyzing Latent Accessibility Clusters of Urban POIs
Farah Kamw, Shamal AL-Dohuki, Ye Zhao, Jing Yang, Xinyue Ye, and Wei Chen
Contextualized Analysis of Movement Events
Siming Chen, Gennady Andrienko, Natalia Andrienko, Christos Doulkeridis, and Athanasios Koumparos
Interactive Pattern Analysis of Multiple T-Maze Data
Fabrizia Bechtold, Hrvoje Abraham, Rainer Splechtna, and Krešimir Matkovic
Visual Analytics of Event Data using Multiple Mining Methods
Muhammad Adnan, Phong H. Nguyen, Roy A. Ruddle, and Cagatay Turkay
Visualizing Event Sequences as Oscillating Streams
Chris Weaver and Ronak Etemadpour
Applications of Visual Analytics
SurviVIS: Visual Analytics for Interactive Survival Analysis
Alberto Corvò, Humberto Simon Garcia Caballero, and Michel A. Westenberg
Interactive Visual Analysis of Patient-Reported Outcomes for Improved Cancer Aftercare
Juliane Müller, Veit Zebralla, Susanne Wiegand, and Steffen Oeltze-Jafra
Visual Analytics of Conversational Dynamics
Daniel Seebacher, Maximilian T. Fischer, Rita Sevastjanova, Daniel A. Keim, and Mennatallah El-Assady

Recent Submissions

  • Visual Analytics of Conversational Dynamics 

    Seebacher, Daniel; Fischer, Maximilian T.; Sevastjanova, Rita; Keim, Daniel A.; El-Assady, Mennatallah (The Eurographics Association, 2019)
    Large-scale interaction networks of human communication are often modeled as complex graph structures, obscuring temporal patterns within individual conversations. To facilitate the understanding of such conversational ...
  • Interactive Visual Analysis of Patient-Reported Outcomes for Improved Cancer Aftercare 

    Müller, Juliane; Zebralla, Veit; Wiegand, Susanne; Oeltze-Jafra, Steffen (The Eurographics Association, 2019)
    The monitoring and planning of cancer aftercare are commonly based on clinical, physiological and caregiver-reported outcome measures. More recently, patient-reported outcome (PRO) measures, capturing social, psychological, ...
  • SurviVIS: Visual Analytics for Interactive Survival Analysis 

    Corvò, Alberto; Caballero, Humberto Simon Garcia; Westenberg, Michel A. (The Eurographics Association, 2019)
    The increasing quantity of data in biomedical informatics is leading towards better patient profiling and personalized medicine. Lab tests, medical images, and clinical data represent extraordinary sources for patient ...
  • Visualizing Event Sequences as Oscillating Streams 

    Weaver, Chris; Etemadpour, Ronak (The Eurographics Association, 2019)
    In this paper, we introduce a new method to visually represent sequence structure in data. Like other methods for visualizing temporal or ordinal data, the representation directly maps absolute time or relative ordering ...
  • Visual Analytics of Event Data using Multiple Mining Methods 

    Adnan, Muhammad; Nguyen, Phong H.; Ruddle, Roy A.; Turkay, Cagatay (The Eurographics Association, 2019)
    Most researchers use a single method of mining to analyze event data. This paper uses case studies from two very different domains (electronic health records and cybersecurity) to investigate how researchers can gain ...
  • Interactive Pattern Analysis of Multiple T-Maze Data 

    Bechtold, Fabrizia; Abraham, Hrvoje; Splechtna, Rainer; Matkovic, Krešimir (The Eurographics Association, 2019)
    The Multiple T-Maze study is one of the standard methods used in ethology and behaviourism. In this paper we extend the current state of the art in analysis of Multiple T-Maze data for animal cohorts. We focus on pattern ...
  • Moving Together: Towards a Formalization of Collective Movement 

    Buchmüller, Juri; Cakmak, Eren; Andrienko, Natalia; Andrienko, Gennady; Jolles, Jolle W.; Keim, Daniel A. (The Eurographics Association, 2019)
    While conventional applications for spatiotemporal datasets mostly focus on the relation between movers and environment, research questions in the analysis of collective movement typically focus more on relationships and ...
  • Contextualized Analysis of Movement Events 

    Chen, Siming; Andrienko, Gennady; Andrienko, Natalia; Doulkeridis, Christos; Koumparos, Athanasios (The Eurographics Association, 2019)
    For understanding the circumstances, causes, and consequences of events that may happen during movement (e.g., harsh brake, sharp turn), it is necessary to analyze event context. The context includes dynamic attributes of ...
  • Visually Analyzing Latent Accessibility Clusters of Urban POIs 

    Kamw, Farah; AL-Dohuki, Shamal; Zhao, Ye; Yang, Jing; Ye, Xinyue; Chen, Wei (The Eurographics Association, 2019)
    Accessibility of urban POIs (Points of Interest) is a key topic in a variety of urban sciences and applications as it reflects inherent city design, transportation, and population flow features. Isochrone maps and other ...
  • Quantifying Uncertainty in Multivariate Time Series Pre-Processing 

    Bors, Christian; Bernard, Jürgen; Bögl, Markus; Gschwandtner, Theresia; Kohlhammer, Jörn; Miksch, Silvia (The Eurographics Association, 2019)
    In multivariate time series analysis, pre-processing is integral for enabling analysis, but inevitably introduces uncertainty into the data. Enabling the assessment of the uncertainty and allowing uncertainty-aware analysis, ...
  • Visualization of Rubik's Cube Solution Algorithms 

    Steinparz, Christian Alexander; Hinterreiter, Andreas P.; Stitz, Holger; Streit, Marc (The Eurographics Association, 2019)
    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 ...
  • On Quality Indicators for Progressive Visual Analytics 

    Angelini, Marco; May, Thorsten; Santucci, Giuseppe; Schulz, Hans-Jörg (The Eurographics Association, 2019)
    A key component in using Progressive Visual Analytics (PVA) is to be able to gauge the quality of intermediate analysis outcomes. This is necessary in order to decide whether a current partial outcome is already good enough ...
  • Deep Learning Inverse Multidimensional Projections 

    Espadoto, Mateus; Rodrigues, Francisco Caio Maia; Hirata, Nina S. T.; Hirata Jr., Roberto; Telea, Alexandru C. (The Eurographics Association, 2019)
    We present a new method for computing inverse projections from 2D spaces to arbitrary high-dimensional spaces. Given any projection technique, we train a deep neural network to learn a low-to-high dimensional mapping based ...
  • TourDino: A Support View for Confirming Patterns in Tabular Data 

    Eckelt, Klaus; Adelberger, Patrick; Zichner, Thomas; Wernitznig, Andreas; Streit, Marc (The Eurographics Association, 2019)
    Seeking relationships and patterns in tabular data is a common data exploration task. To confirm hypotheses that are based on visual patterns observed during exploratory data analysis, users need to be able to quickly ...
  • Visual Analysis of Degree-of-Interest Functions to Support Selection Strategies for Instance Labeling 

    Bernard, Jürgen; Hutter, Marco; Ritter, Christian; Lehmann, Markus; Sedlmair, Michael; Zeppelzauer, Matthias (The Eurographics Association, 2019)
    Manually labeling data sets is a time-consuming and expensive task that can be accelerated by interactive machine learning and visual analytics approaches. At the core of these approaches are strategies for the selection ...
  • EuroVa 2019: Frontmatter 

    Landesberger, Tatiana; Turkay, Cagatay (The Eurographics Association, 2019)