38-Issue 3

Permanent URI for this collection

EuroVis 2019 - 21st EG/VGTC Conference on Visualization
Porto, Portugal 3-7 June 2019
Best Paper Award Nominees
V-Awake: A Visual Analytics Approach for Correcting Sleep Predictions from Deep Learning Models
Humberto Simon Garcia Caballero, Michel A. Westenberg, Binyam Gebre, and Jarke J. van Wijk
Optimizing Stepwise Animation in Dynamic Set Diagrams
Kazuyo Mizuno, Hsiang-Yun Wu, Shigeo Takahashi, and Takeo Igarashi
Interactive Visualization of Flood and Heavy Rain Simulations
Daniel Cornel, Andreas Buttinger-Kreuzhuber, Artem Konev, Zsolt Horváth, Michael Wimmer, Raimund Heidrich, and Jürgen Waser
Follow The Clicks: Learning and Anticipating Mouse Interactions During Exploratory Data Analysis
Alvitta Ottley, Roman Garnett, and Ran Wan
A Framework for GPU-accelerated Exploration of Massive Time-varying Rectilinear Scalar Volumes
Fabio Marton, Marco Agus, and Enrico Gobbetti
Analysis Applications and Systems
Latent Space Cartography: Visual Analysis of Vector Space Embeddings
Yang Liu, Eunice Jun, Qisheng Li, and Jeffrey Heer
Multiple Views: Different Meanings and Collocated Words
Jonathan C. Roberts, Hayder Mahdi Al-maneea, Peter W. S. Butcher, Robert Lew, Geraint Paul Rees, Nirwan Sharma, and Ana Frankenberg-Garcia
DIVA: Exploration and Validation of Hypothesized Drug-Drug Interactions
Tabassum Kakar, Xiao Qin, Elke A. Rundensteiner, Lane Harrison, Sanjay K. Sahoo, and Suranjan De
CV3: Visual Exploration, Assessment, and Comparison of CVs
Velitchko Andreev Filipov, Alessio Arleo, Paolo Federico, and Silvia Miksch
VIAN: A Visual Annotation Tool for Film Analysis
Gaudenz Halter, Rafael Ballester-Ripoll, Barbara Flueckiger, and Renato Pajarola
Analysis and Decision Making
An Ontological Framework for Supporting the Design and Evaluation of Visual Analytics Systems
Min Chen and David S. Ebert
Characterizing Exploratory Visual Analysis: A Literature Review and Evaluation of Analytic Provenance in Tableau
Leilani Battle and Jeffrey Heer
Investigating Effects of Visual Anchors on Decision-Making about Misinformation
Ryan Wesslen, Sashank Santhanam, Alireza Karduni, Isaac Cho, Samira Shaikh, and Wenwen Dou
An Exploratory User Study of Visual Causality Analysis
Chi-Hsien Eric Yen, Aditya Parameswaran, and Wai-Tat Fu
A User-based Visual Analytics Workflow for Exploratory Model Analysis
Dylan Cashman, Shah Rukh Humayoun, Florian Heimerl, Kendall Park, Subhajit Das, John R. Thompson, Bahador Saket, Abigail Mosca, John Stasko, Alex Endert, Michael Gleicher, and Remco Chang
Analysis Techniques
Toward Understanding Representation Methods in Visualization Recommendations through Scatterplot Construction Tasks
Sehi L'Yi, Youli Chang, DongHwa Shin, and Jinwook Seo
Oui! Outlier Interpretation on Multi-dimensional Data via Visual Analytics
Xun Zhao, Weiwei Cui, Yanhong Wu, Haidong Zhang, Huamin Qu, and Dongmei Zhang
ClustMe: A Visual Quality Measure for Ranking Monochrome Scatterplots based on Cluster Patterns
Mostafa M. Abbas, Michaël Aupetit, Michael Sedlmair, and Halima Bensmail
SurgeryCuts: Embedding Additional Information in Maps without Occluding Features
Marco Angelini, Juri Buchmüller, Daniel A. Keim, Philipp Meschenmoser, and Giuseppe Santucci
Visualizing for the Non-Visual: Enabling the Visually Impaired to Use Visualization
Jinho Choi, SangHun Jung, Deok Gun Park, Jaegul Choo, and Niklas Elmqvist
Vectors and Features
The Dependent Vectors Operator
Lutz Hofmann and Filip Sadlo
A Visual Tool for the Analysis of Algorithms for Tomographic Fiber Reconstruction in Materials Science
Bernhard Fröhler, Tim Elberfeld, Torsten Möller, Hans-Christian Hege, Johannes Weissenböck, Jan De Beenhouwer, Jan Sijbers, Johann Kastner, and Christoph Heinzl
Robust Reference Frame Extraction from Unsteady 2D Vector Fields with Convolutional Neural Networks
Byungsoo Kim and Tobias Günther
An Interactive Visualization System for Large Sets of Phase Space Trajectories
Tyson A. Neuroth, Franz Sauer, and Kwan-Liu Ma
Higher-Order Data Types
Visualization of Equivalence in 2D Bivariate Fields
Boyan Zheng, Bastian Rieck, Heike Leitte, and Filip Sadlo
Towards Glyphs for Uncertain Symmetric Second-Order Tensors
Tim Gerrits, Christian Rössl, and Holger Theisel
Robust Extraction and Simplification of 2D Symmetric Tensor Field Topology
Jochen Jankowai, Bei Wang, and Ingrid Hotz
Visualization Support for Developing a Matrix Calculus Algorithm: A Case Study
Joachim Giesen, Julien Klaus, Sören Laue, and Ferdinand Schreck
Examining Implicit Discretization in Spectral Schemes
P. Samuel Quinan, Lace M. K. Padilla, Sarah H. Creem-Regehr, and Miriah Meyer
Time Series
Bridging the Data Analysis Communication Gap Utilizing a Three-Component Summarized Line Graph
Calvin Yau, Morteza Karimzadeh, Chittayong Surakitbanharn, Niklas Elmqvist, and David S. Ebert
ChronoCorrelator: Enriching Events with Time Series
Martijn A.M.M. van Dortmont, Stef van den Elzen, and Jarke J. van Wijk
Visual-Interactive Preprocessing of Multivariate Time Series Data
Jürgen Bernard, Marco Hutter, Heiko Reinemuth, Hendrik Pfeifer, Christian Bors, and Jörn Kohlhammer
Biomedical Applications and Ray Tracing
A Geometric Optimization Approach for the Detection and Segmentation of Multiple Aneurysms
Kai Lawonn, Monique Meuschke, Ralph Wickenhöfer, Bernhard Preim, and Klaus Hildebrandt
Interactive Volumetric Visual Analysis of Glycogen-derived Energy Absorption in Nanometric Brain Structures
Marco Agus, Corrado Calì, Ali K. Al-Awami, Enrico Gobbetti, Pierre J. Magistretti, and Markus Hadwiger
Analysis of Long Molecular Dynamics Simulations Using Interactive Focus+Context Visualization
Jan Byška, Thomas Trautner, Sérgio M. Marques, Jiří Damborský, Barbora Kozlíková, and Manuela Waldner
Scalable Ray Tracing Using the Distributed FrameBuffer
Will Usher, Ingo Wald, Jefferson Amstutz, Johannes Günther, Carson Brownlee, and Valerio Pascucci
Ray Tracing Generalized Tube Primitives: Method and Applications
Mengjiao Han, Ingo Wald, Will Usher, Qi Wu, Feng Wang, Valerio Pascucci, Charles D. Hansen, and Chris R. Johnson
Spatial Data Applications
Visual Analysis of Charge Flow Networks for Complex Morphologies
Sathish Kottravel, Martin Falk, Talha Bin Masood, Mathieu Linares, and Ingrid Hotz
IGM-Vis: Analyzing Intergalactic and Circumgalactic Medium Absorption Using Quasar Sightlines in a Cosmic Web Context
Joseph N. Burchett, David Abramov, Jasmine Tan Otto, Cassia Artanegara, Jason Xavier Prochaska, and Angus G. Forbes
Analysis of Decadal Climate Predictions with User-guided Hierarchical Ensemble Clustering
Christopher P. Kappe, Michael Böttinger, and Heike Leitte
Evaluating Image Quality Measures to Assess the Impact of Lossy Data Compression Applied to Climate Simulation Data
Allison H. Baker, Dorit M. Hammerling, and Terece L. Turton
Interaction Techniques for Scalability
Kyrix: Interactive Pan/Zoom Visualizations at Scale
Wenbo Tao, Xiaoyu Liu, Yedi Wang, Leilani Battle, Çagatay Demiralp, Remco Chang, and Michael Stonebraker
Designing Animated Transitions to Convey Aggregate Operations
Younghoon Kim, Michael Correll, and Jeffrey Heer
Hybrid Touch/Tangible Spatial 3D Data Selection
Lonni Besançon, Mickael Sereno, Lingyun Yu, Mehdi Ammi, and Tobias Isenberg
Focus+Context Exploration of Hierarchical Embeddings
Thomas Höllt, Anna Vilanova, Nicola Pezzotti, Boudewijn P. F. Lelieveldt, and Helwig Hauser
Geospatial and Social Data
Route-Aware Edge Bundling for Visualizing Origin-Destination Trails in Urban Traffic
Wei Zeng, Qiaomu Shen, Yuzhe Jiang, and Alexandru Telea
Bird's-Eye - Large-Scale Visual Analytics of City Dynamics using Social Location Data
Robert Krueger, Qi Han, Nikolay Ivanov, Sanae Mahtal, Dennis Thom, Hanspeter Pfister, and Thomas Ertl
Topic Tomographies (TopTom): a Visual Approach to Distill Information From Media Streams
Beatrice Gobbo, Duilio Balsamo, Michele Mauri, Paolo Bajardi, André Panisson, and Paolo Ciuccarelli
Segmentifier: Interactive Refinement of Clickstream Data
Kimberly Dextras-Romagnino and Tamara Munzner
Interaction Techniques
Augmenting Tactile 3D Data Navigation With Pressure Sensing
Xiyao Wang, Lonni Besançon, Mehdi Ammi, and Tobias Isenberg
InsideInsights: Integrating Data-Driven Reporting in Collaborative Visual Analytics
Andreas Mathisen, Tom Horak, Clemens Nylandsted Klokmose, Kaj Grønbæk, and Niklas Elmqvist
Investigating the Manual View Specification and Visualization by Demonstration Paradigms for Visualization Construction
Bahador Saket and Alex Endert
Linking and Layout: Exploring the Integration of Text and Visualization in Storytelling
Qiyu Zhi, Alvitta Ottley, and Ronald Metoyer
Capture & Analysis of Active Reading Behaviors for Interactive Articles on the Web
Matthew Conlen, Alex Kale, and Jeffrey Heer
Graphs and Networks
netflower: Dynamic Network Visualization for Data Journalists
Christina Stoiber, Alexander Rind, Florian Grassinger, Robert Gutounig, Eva Goldgruber, Michael Sedlmair, Štefan Emrich, and Wolfgang Aigner
Efficient Optimal Overlap Removal: Algorithms and Experiments
Wouter Meulemans
A Stable Graph Layout Algorithm for Processes
Robin J. P. Mennens, Roeland Scheepens, and Michel A. Westenberg
A Random Sampling O(n) Force-calculation Algorithm for Graph Layouts
Robert Gove

BibTeX (38-Issue 3)
                
@article{
10.1111:cgf.13667,
journal = {Computer Graphics Forum}, title = {{
V-Awake: A Visual Analytics Approach for Correcting Sleep Predictions from Deep Learning Models}},
author = {
Garcia Caballero, Humberto
and
Westenberg, Michel
and
Gebre, Binyam
and
Wijk, Jarke J. van
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13667}
}
                
@article{
10.1111:cgf.13668,
journal = {Computer Graphics Forum}, title = {{
Optimizing Stepwise Animation in Dynamic Set Diagrams}},
author = {
Mizuno, Kazuyo
and
WU, Hsiang-Yun
and
Takahashi, Shigeo
and
Igarashi, Takeo
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13668}
}
                
@article{
10.1111:cgf.13669,
journal = {Computer Graphics Forum}, title = {{
Interactive Visualization of Flood and Heavy Rain Simulations}},
author = {
Cornel, Daniel
and
Buttinger-Kreuzhuber, Andreas
and
Konev, Artem
and
Horváth, Zsolt
and
Wimmer, Michael
and
Heidrich, Raimund
and
Waser, Jürgen
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13669}
}
                
@article{
10.1111:cgf.13670,
journal = {Computer Graphics Forum}, title = {{
Follow The Clicks: Learning and Anticipating Mouse Interactions During Exploratory Data Analysis}},
author = {
Ottley, Alvitta
and
Garnett, Roman
and
Wan, Ran
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13670}
}
                
@article{
10.1111:cgf.13671,
journal = {Computer Graphics Forum}, title = {{
A Framework for GPU-accelerated Exploration of Massive Time-varying Rectilinear Scalar Volumes}},
author = {
Marton, Fabio
and
Agus, Marco
and
Gobbetti, Enrico
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13671}
}
                
@article{
10.1111:cgf.13672,
journal = {Computer Graphics Forum}, title = {{
Latent Space Cartography: Visual Analysis of Vector Space Embeddings}},
author = {
Liu, Yang
and
Jun, Eunice
and
Li, Qisheng
and
Heer, Jeffrey
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13672}
}
                
@article{
10.1111:cgf.13673,
journal = {Computer Graphics Forum}, title = {{
Multiple Views: Different Meanings and Collocated Words}},
author = {
Roberts, Jonathan
and
Al-Maneea, Hayder
and
Butcher, Peter
and
Lew, Robert
and
Rees, Geraint Paul
and
Sharma, Nirwan
and
Frankenberg-Garcia, Ana
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13673}
}
                
@article{
10.1111:cgf.13674,
journal = {Computer Graphics Forum}, title = {{
DIVA: Exploration and Validation of Hypothesized Drug-Drug Interactions}},
author = {
Kakar, Tabassum
and
Qin, Xiao
and
Rundensteiner, Elke A.
and
Harrison, Lane
and
Sahoo, Sanjay K.
and
De, Suranjan
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13674}
}
                
@article{
10.1111:cgf.13675,
journal = {Computer Graphics Forum}, title = {{
CV3: Visual Exploration, Assessment, and Comparison of CVs}},
author = {
Filipov, Velitchko
and
Arleo, Alessio
and
Federico, Paolo
and
Miksch, Silvia
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13675}
}
                
@article{
10.1111:cgf.13676,
journal = {Computer Graphics Forum}, title = {{
VIAN: A Visual Annotation Tool for Film Analysis}},
author = {
Halter, Gaudenz
and
Ballester-Ripoll, Rafael
and
Flueckiger, Barbara
and
Pajarola, Renato
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13676}
}
                
@article{
10.1111:cgf.13678,
journal = {Computer Graphics Forum}, title = {{
Characterizing Exploratory Visual Analysis: A Literature Review and Evaluation of Analytic Provenance in Tableau}},
author = {
Battle, Leilani
and
Heer, Jeffrey
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13678}
}
                
@article{
10.1111:cgf.13677,
journal = {Computer Graphics Forum}, title = {{
An Ontological Framework for Supporting the Design and Evaluation of Visual Analytics Systems}},
author = {
Chen, Min
and
Ebert, David
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13677}
}
                
@article{
10.1111:cgf.13679,
journal = {Computer Graphics Forum}, title = {{
Investigating Effects of Visual Anchors on Decision-Making about Misinformation}},
author = {
Wesslen, Ryan
and
Santhanam, Sashank
and
Karduni, Alireza
and
Cho, Isaac
and
Shaikh, Samira
and
Dou, Wenwen
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13679}
}
                
@article{
10.1111:cgf.13681,
journal = {Computer Graphics Forum}, title = {{
A User-based Visual Analytics Workflow for Exploratory Model Analysis}},
author = {
Cashman, Dylan
and
Humayoun, Shah Rukh
and
Gleicher, Michael
and
Chang, Remco
and
Heimerl, Florian
and
Park, Kendall
and
Das, Subhajit
and
Thompson, John
and
Saket, Bahador
and
Mosca, Abigail
and
Stasko, John
and
Endert, Alex
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13681}
}
                
@article{
10.1111:cgf.13680,
journal = {Computer Graphics Forum}, title = {{
An Exploratory User Study of Visual Causality Analysis}},
author = {
Yen, Chi-Hsien Eric
and
Parameswaran, Aditya
and
Fu, Wai-Tat
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13680}
}
                
@article{
10.1111:cgf.13682,
journal = {Computer Graphics Forum}, title = {{
Toward Understanding Representation Methods in Visualization Recommendations through Scatterplot Construction Tasks}},
author = {
L'Yi, Sehi
and
Chang, Youli
and
Shin, DongHwa
and
Seo, Jinwook
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13682}
}
                
@article{
10.1111:cgf.13684,
journal = {Computer Graphics Forum}, title = {{
ClustMe: A Visual Quality Measure for Ranking Monochrome Scatterplots based on Cluster Patterns}},
author = {
Abbas, Mostafa M.
and
Aupetit, Michaël
and
Sedlmair, Michael
and
Bensmail, Halima
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13684}
}
                
@article{
10.1111:cgf.13683,
journal = {Computer Graphics Forum}, title = {{
Oui! Outlier Interpretation on Multi-dimensional Data via Visual Analytics}},
author = {
Zhao, Xun
and
Cui, Weiwei
and
Wu, Yanhong
and
Zhang, Haidong
and
Qu, Huamin
and
Zhang, Dongmei
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13683}
}
                
@article{
10.1111:cgf.13685,
journal = {Computer Graphics Forum}, title = {{
SurgeryCuts: Embedding Additional Information in Maps without Occluding Features}},
author = {
Angelini, Marco
and
Buchmüller, Juri
and
Keim, Daniel A.
and
Meschenmoser, Philipp
and
Santucci, Giuseppe
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13685}
}
                
@article{
10.1111:cgf.13686,
journal = {Computer Graphics Forum}, title = {{
Visualizing for the Non-Visual: Enabling the Visually Impaired to Use Visualization}},
author = {
Choi, Jinho
and
Jung, Sanghun
and
Park, Deok Gun
and
Choo, Jaegul
and
Elmqvist, Niklas
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13686}
}
                
@article{
10.1111:cgf.13687,
journal = {Computer Graphics Forum}, title = {{
The Dependent Vectors Operator}},
author = {
Hofmann, Lutz
and
Sadlo, Filip
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13687}
}
                
@article{
10.1111:cgf.13688,
journal = {Computer Graphics Forum}, title = {{
A Visual Tool for the Analysis of Algorithms for Tomographic Fiber Reconstruction in Materials Science}},
author = {
Fröhler, Bernhard
and
Elberfeld, Tim
and
Möller, Torsten
and
Hege, Hans-Christian
and
Weissenböck, Johannes
and
De Beenhouwer, Jan
and
Sijbers, Jan
and
Kastner, Johann
and
Heinzl, Christoph
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13688}
}
                
@article{
10.1111:cgf.13689,
journal = {Computer Graphics Forum}, title = {{
Robust Reference Frame Extraction from Unsteady 2D Vector Fields with Convolutional Neural Networks}},
author = {
Kim, Byungsoo
and
Günther, Tobias
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13689}
}
                
@article{
10.1111:cgf.13690,
journal = {Computer Graphics Forum}, title = {{
An Interactive Visualization System for Large Sets of Phase Space Trajectories}},
author = {
Neuroth, Tyson
and
Sauer, Franz
and
Ma, Kwan-Liu
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13690}
}
                
@article{
10.1111:cgf.13691,
journal = {Computer Graphics Forum}, title = {{
Visualization of Equivalence in 2D Bivariate Fields}},
author = {
Zheng, Boyan
and
Rieck, Bastian
and
Leitte, Heike
and
Sadlo, Filip
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13691}
}
                
@article{
10.1111:cgf.13692,
journal = {Computer Graphics Forum}, title = {{
Towards Glyphs for Uncertain Symmetric Second-Order Tensors}},
author = {
Gerrits, Tim
and
Rössl, Christian
and
Theisel, Holger
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13692}
}
                
@article{
10.1111:cgf.13693,
journal = {Computer Graphics Forum}, title = {{
Robust Extraction and Simplification of 2D Symmetric Tensor Field Topology}},
author = {
Jankowai, Jochen
and
Wang, Bei
and
Hotz, Ingrid
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13693}
}
                
@article{
10.1111:cgf.13694,
journal = {Computer Graphics Forum}, title = {{
Visualization Support for Developing a Matrix Calculus Algorithm: A Case Study}},
author = {
Giesen, Joachim
and
Klaus, Julien
and
Laue, Sören
and
Schreck, Ferdinand
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13694}
}
                
@article{
10.1111:cgf.13695,
journal = {Computer Graphics Forum}, title = {{
Examining Implicit Discretization in Spectral Schemes}},
author = {
Quinan, P. Samuel
and
Padilla, Lace M. K.
and
Creem-Regehr, Sarah H.
and
Meyer, Miriah
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13695}
}
                
@article{
10.1111:cgf.13696,
journal = {Computer Graphics Forum}, title = {{
Bridging the Data Analysis Communication Gap Utilizing a Three-Component Summarized Line Graph}},
author = {
Yau, Calvin
and
Karimzadeh, Morteza
and
Surakitbanharn, Chittayong
and
Elmqvist, Niklas
and
Ebert, David
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13696}
}
                
@article{
10.1111:cgf.13697,
journal = {Computer Graphics Forum}, title = {{
ChronoCorrelator: Enriching Events with Time Series}},
author = {
van Dortmont, Martijn
and
Elzen, Stef van den
and
Wijk, Jarke J. van
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13697}
}
                
@article{
10.1111:cgf.13698,
journal = {Computer Graphics Forum}, title = {{
Visual-Interactive Preprocessing of Multivariate Time Series Data}},
author = {
Bernard, Jürgen
and
Hutter, Marco
and
Reinemuth, Heiko
and
Pfeifer, Hendrik
and
Bors, Christian
and
Kohlhammer, Jörn
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13698}
}
                
@article{
10.1111:cgf.13699,
journal = {Computer Graphics Forum}, title = {{
A Geometric Optimization Approach for the Detection and Segmentation of Multiple Aneurysms}},
author = {
Lawonn, Kai
and
Meuschke, Monique
and
Wickenhöfer, Ralph
and
Preim, Bernhard
and
Hildebrandt, Klaus
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13699}
}
                
@article{
10.1111:cgf.13700,
journal = {Computer Graphics Forum}, title = {{
Interactive Volumetric Visual Analysis of Glycogen-derived Energy Absorption in Nanometric Brain Structures}},
author = {
Agus, Marco
and
Calì, Corrado
and
Al-Awami, Ali K.
and
Gobbetti, Enrico
and
Magistretti, Pierre J.
and
Hadwiger, Markus
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13700}
}
                
@article{
10.1111:cgf.13701,
journal = {Computer Graphics Forum}, title = {{
Analysis of Long Molecular Dynamics Simulations Using Interactive Focus+Context Visualization}},
author = {
Byška, Jan
and
Trautner, Thomas
and
Marques, Sérgio M.
and
Damborský, Jiří
and
Kozlíková, Barbora
and
Waldner, Manuela
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13701}
}
                
@article{
10.1111:cgf.13702,
journal = {Computer Graphics Forum}, title = {{
Scalable Ray Tracing Using the Distributed FrameBuffer}},
author = {
Usher, Will
and
Wald, Ingo
and
Amstutz, Jefferson
and
Günther, Johannes
and
Brownlee, Carson
and
Pascucci, Valerio
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13702}
}
                
@article{
10.1111:cgf.13703,
journal = {Computer Graphics Forum}, title = {{
Ray Tracing Generalized Tube Primitives: Method and Applications}},
author = {
Han, Mengjiao
and
Wald, Ingo
and
Usher, Will
and
Wu, Qi
and
Wang, Feng
and
Pascucci, Valerio
and
Hansen, Charles D.
and
Johnson, Chris R.
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13703}
}
                
@article{
10.1111:cgf.13704,
journal = {Computer Graphics Forum}, title = {{
Visual Analysis of Charge Flow Networks for Complex Morphologies}},
author = {
Kottravel, Sathish
and
Falk, Martin
and
Bin Masood, Talha
and
linares, mathieu
and
Hotz, Ingrid
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13704}
}
                
@article{
10.1111:cgf.13705,
journal = {Computer Graphics Forum}, title = {{
IGM-Vis: Analyzing Intergalactic and Circumgalactic Medium Absorption Using Quasar Sightlines in a Cosmic Web Context}},
author = {
Burchett, Joseph N.
and
Abramov, David
and
Otto, Jasmine Tan
and
Artanegara, Cassia
and
Prochaska, Jason Xavier
and
Forbes, Angus G.
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13705}
}
                
@article{
10.1111:cgf.13706,
journal = {Computer Graphics Forum}, title = {{
Analysis of Decadal Climate Predictions with User-guided Hierarchical Ensemble Clustering}},
author = {
Kappe, Christopher
and
Böttinger, Michael
and
Leitte, Heike
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13706}
}
                
@article{
10.1111:cgf.13707,
journal = {Computer Graphics Forum}, title = {{
Evaluating Image Quality Measures to Assess the Impact of Lossy Data Compression Applied to Climate Simulation Data}},
author = {
Baker, Allison
and
Hammerling, Dorit
and
Turton, Terece
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13707}
}
                
@article{
10.1111:cgf.13708,
journal = {Computer Graphics Forum}, title = {{
Kyrix: Interactive Pan/Zoom Visualizations at Scale}},
author = {
Tao, Wenbo
and
Liu, Xiaoyu
and
Wang, Yedi
and
Battle, Leilani
and
Demiralp, Çagatay
and
Chang, Remco
and
Stonebraker, Michael
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13708}
}
                
@article{
10.1111:cgf.13709,
journal = {Computer Graphics Forum}, title = {{
Designing Animated Transitions to Convey Aggregate Operations}},
author = {
Kim, Younghoon
and
Correll, Michael
and
Heer, Jeffrey
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13709}
}
                
@article{
10.1111:cgf.13710,
journal = {Computer Graphics Forum}, title = {{
Hybrid Touch/Tangible Spatial 3D Data Selection}},
author = {
Besançon, Lonni
and
Sereno, Mickael
and
Yu, Lingyun
and
Ammi, Mehdi
and
Isenberg, Tobias
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13710}
}
                
@article{
10.1111:cgf.13711,
journal = {Computer Graphics Forum}, title = {{
Focus+Context Exploration of Hierarchical Embeddings}},
author = {
Höllt, Thomas
and
Vilanova, Anna
and
Pezzotti, Nicola
and
Lelieveldt, Boudewijn P. F.
and
Hauser, Helwig
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13711}
}
                
@article{
10.1111:cgf.13712,
journal = {Computer Graphics Forum}, title = {{
Route-Aware Edge Bundling for Visualizing Origin-Destination Trails in Urban Traffic}},
author = {
Zeng, Wei
and
Shen, Qiaomu
and
Jiang, Yuzhe
and
Telea, Alexandru
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13712}
}
                
@article{
10.1111:cgf.13713,
journal = {Computer Graphics Forum}, title = {{
Bird's-Eye - Large-Scale Visual Analytics of City Dynamics using Social Location Data}},
author = {
Krueger, Robert
and
Han, Qi
and
Ivanov, Nikolay
and
Mahtal, Sanae
and
Thom, Dennis
and
Pfister, Hanspeter
and
Ertl, Thomas
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13713}
}
                
@article{
10.1111:cgf.13714,
journal = {Computer Graphics Forum}, title = {{
Topic Tomographies (TopTom): a Visual Approach to Distill Information From Media Streams}},
author = {
Gobbo, Beatrice
and
Balsamo, Duilio
and
Mauri, Michele
and
Bajardi, Paolo
and
Panisson, André
and
CIUCCARELLI, PAOLO
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13714}
}
                
@article{
10.1111:cgf.13715,
journal = {Computer Graphics Forum}, title = {{
Segmentifier: Interactive Refinement of Clickstream Data}},
author = {
Dextras-Romagnino, Kimberly
and
Munzner, Tamara
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13715}
}
                
@article{
10.1111:cgf.13716,
journal = {Computer Graphics Forum}, title = {{
Augmenting Tactile 3D Data Navigation With Pressure Sensing}},
author = {
Wang, Xiyao
and
Besançon, Lonni
and
Ammi, Mehdi
and
Isenberg, Tobias
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13716}
}
                
@article{
10.1111:cgf.13717,
journal = {Computer Graphics Forum}, title = {{
InsideInsights: Integrating Data-Driven Reporting in Collaborative Visual Analytics}},
author = {
Mathisen, Andreas
and
Horak, Tom
and
Klokmose, Clemens Nylandsted
and
Grønbæk, Kaj
and
Elmqvist, Niklas
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13717}
}
                
@article{
10.1111:cgf.13718,
journal = {Computer Graphics Forum}, title = {{
Investigating the Manual View Specification and Visualization by Demonstration Paradigms for Visualization Construction}},
author = {
Saket, Bahador
and
Endert, Alex
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13718}
}
                
@article{
10.1111:cgf.13719,
journal = {Computer Graphics Forum}, title = {{
Linking and Layout: Exploring the Integration of Text and Visualization in Storytelling}},
author = {
Zhi, Qiyu
and
Ottley, Alvitta
and
Metoyer, Ronald
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13719}
}
                
@article{
10.1111:cgf.13720,
journal = {Computer Graphics Forum}, title = {{
Capture & Analysis of Active Reading Behaviors for Interactive Articles on the Web}},
author = {
Conlen, Matthew
and
Kale, Alex
and
Heer, Jeffrey
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13720}
}
                
@article{
10.1111:cgf.13721,
journal = {Computer Graphics Forum}, title = {{
netflower: Dynamic Network Visualization for Data Journalists}},
author = {
Stoiber, Christina
and
Rind, Alexander
and
Grassinger, Florian
and
Gutounig, Robert
and
Goldgruber, Eva
and
Sedlmair, Michael
and
Emrich, Štefan
and
Aigner, Wolfgang
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13721}
}
                
@article{
10.1111:cgf.13722,
journal = {Computer Graphics Forum}, title = {{
Efficient Optimal Overlap Removal: Algorithms and Experiments}},
author = {
Meulemans, Wouter
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13722}
}
                
@article{
10.1111:cgf.13723,
journal = {Computer Graphics Forum}, title = {{
A Stable Graph Layout Algorithm for Processes}},
author = {
Mennens, Robin
and
Scheepens, Roeland
and
Westenberg, Michel
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13723}
}
                
@article{
10.1111:cgf.13724,
journal = {Computer Graphics Forum}, title = {{
A Random Sampling O(n) Force-calculation Algorithm for Graph Layouts}},
author = {
Gove, Robert
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13724}
}

Browse

Recent Submissions

Now showing 1 - 59 of 59
  • Item
    EuroVis 2019 CGF 38-3: Frontmatter
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Gleicher, Michael; Viola, Ivan; Leitte, Heike; Gleicher, Michael and Viola, Ivan and Leitte, Heike
  • Item
    V-Awake: A Visual Analytics Approach for Correcting Sleep Predictions from Deep Learning Models
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Garcia Caballero, Humberto; Westenberg, Michel; Gebre, Binyam; Wijk, Jarke J. van; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    The usage of deep learning models for tagging input data has increased over the past years because of their accuracy and highperformance. A successful application is to score sleep stages. In this scenario, models are trained to predict the sleep stages of individuals. Although their predictive accuracy is high, there are still misclassifications that prevent doctors from properly diagnosing sleep-related disorders. This paper presents a system that allows users to explore the output of deep learning models in a real-life scenario to spot and analyze faulty predictions. These can be corrected by users to generate a sequence of sleep stages to be examined by doctors. Our approach addresses a real-life scenario with absence of ground truth. It differs from others in that our goal is not to improve the model itself, but to correct the predictions it provides. We demonstrate that our approach is effective in identifying faulty predictions and helping users to fix them in the proposed use case.
  • Item
    Optimizing Stepwise Animation in Dynamic Set Diagrams
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Mizuno, Kazuyo; WU, Hsiang-Yun; Takahashi, Shigeo; Igarashi, Takeo; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    A set diagram represents the membership relation among data elements. It is often visualized as secondary information on top of primary information, such as the spatial positions of elements on maps and charts. Visualizing the temporal evolution of such set diagrams as well as their primary features is quite important; however, conventional approaches have only focused on the temporal behavior of the primary features and do not provide an effective means to highlight notable transitions within the set relationships. This paper presents an approach for generating a stepwise animation between set diagrams by decomposing the entire transition into atomic changes associated with individual data elements. The key idea behind our approach is to optimize the ordering of the atomic changes such that the synthesized animation minimizes unwanted set occlusions by considering their depth ordering and reduces the gaze shift between two consecutive stepwise changes. Experimental results and a user study demonstrate that the proposed approach effectively facilitates the visual identification of the detailed transitions inherent in dynamic set diagrams.
  • Item
    Interactive Visualization of Flood and Heavy Rain Simulations
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Cornel, Daniel; Buttinger-Kreuzhuber, Andreas; Konev, Artem; Horváth, Zsolt; Wimmer, Michael; Heidrich, Raimund; Waser, Jürgen; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    In this paper, we present a real-time technique to visualize large-scale adaptive height fields with C1-continuous surface reconstruction. Grid-based shallow water simulation is an indispensable tool for interactive flood management applications. Height fields defined on adaptive grids are often the only viable option to store and process the massive simulation data. Their visualization requires the reconstruction of a continuous surface from the spatially discrete simulation data. For regular grids, fast linear and cubic interpolation are commonly used for surface reconstruction. For adaptive grids, however, there exists no higher-order interpolation technique fast enough for interactive applications. Our proposed technique bridges the gap between fast linear and expensive higher-order interpolation for adaptive surface reconstruction. During reconstruction, no matter if regular or adaptive, discretization and interpolation artifacts can occur, which domain experts consider misleading and unaesthetic. We take into account boundary conditions to eliminate these artifacts, which include water climbing uphill, diving towards walls, and leaking through thin objects. We apply realistic water shading with visual cues for depth perception and add waves and foam synthesized from the simulation data to emphasize flow directions. The versatility and performance of our technique are demonstrated in various real-world scenarios. A survey conducted with domain experts of different backgrounds and concerned citizens proves the usefulness and effectiveness of our technique.
  • Item
    Follow The Clicks: Learning and Anticipating Mouse Interactions During Exploratory Data Analysis
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Ottley, Alvitta; Garnett, Roman; Wan, Ran; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    The goal of visual analytics is to create a symbiosis between human and computer by leveraging their unique strengths. While this model has demonstrated immense success, we are yet to realize the full potential of such a human-computer partnership. In a perfect collaborative mixed-initiative system, the computer must possess skills for learning and anticipating the users' needs. Addressing this gap, we propose a framework for inferring attention from passive observations of the user's click, thereby allowing accurate predictions of future events. We demonstrate this technique with a crime map and found that users' clicks can appear in our prediction set 92% - 97% of the time. Further analysis shows that we can achieve high prediction accuracy typically after three clicks. Altogether, we show that passive observations of interaction data can reveal valuable information that will allow the system to learn and anticipate future events.
  • Item
    A Framework for GPU-accelerated Exploration of Massive Time-varying Rectilinear Scalar Volumes
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Marton, Fabio; Agus, Marco; Gobbetti, Enrico; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    We introduce a novel flexible approach to spatiotemporal exploration of rectilinear scalar volumes. Our out-of-core representation, based on per-frame levels of hierarchically tiled non-redundant 3D grids, efficiently supports spatiotemporal random access and streaming to the GPU in compressed formats. A novel low-bitrate codec able to store into fixed-size pages a variable-rate approximation based on sparse coding with learned dictionaries is exploited to meet stringent bandwidth constraint during time-critical operations, while a near-lossless representation is employed to support high-quality static frame rendering. A flexible high-speed GPU decoder and raycasting framework mixes and matches GPU kernels performing parallel object-space and image-space operations for seamless support, on fat and thin clients, of different exploration use cases, including animation and temporal browsing, dynamic exploration of single frames, and high-quality snapshots generated from near-lossless data. The quality and performance of our approach are demonstrated on large data sets with thousands of multi-billion-voxel frames.
  • Item
    Latent Space Cartography: Visual Analysis of Vector Space Embeddings
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Liu, Yang; Jun, Eunice; Li, Qisheng; Heer, Jeffrey; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    Latent spaces-reduced-dimensionality vector space embeddings of data, fit via machine learning-have been shown to capture interesting semantic properties and support data analysis and synthesis within a domain. Interpretation of latent spaces is challenging because prior knowledge, sometimes subtle and implicit, is essential to the process. We contribute methods for ''latent space cartography'', the process of mapping and comparing meaningful semantic dimensions within latent spaces. We first perform a literature survey of relevant machine learning, natural language processing, and scientific research to distill common tasks and propose a workflow process. Next, we present an integrated visual analysis system for supporting this workflow, enabling users to discover, define, and verify meaningful relationships among data points, encoded within latent space dimensions. Three case studies demonstrate how users of our system can compare latent space variants in image generation, challenge existing findings on cancer transcriptomes, and assess a word embedding benchmark.
  • Item
    Multiple Views: Different Meanings and Collocated Words
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Roberts, Jonathan; Al-Maneea, Hayder; Butcher, Peter; Lew, Robert; Rees, Geraint Paul; Sharma, Nirwan; Frankenberg-Garcia, Ana; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    We report on an in-depth corpus linguistic study on 'multiple views' terminology and word collocation. We take a broad interpretation of these terms, and explore the meaning and diversity of their use in visualisation literature. First we explore senses of the term 'multiple views' (e.g.,'multiple views' can mean juxtaposition, many viewport projections or several alternative opinions). Second, we investigate term popularity and frequency of occurrences, investigating usage of 'multiple' and 'view' (e.g., multiple views, multiple visualisations, multiple sets). Third, we investigate word collocations and terms that have a similar sense (e.g., multiple views, side-by-side, small multiples). We built and used several corpora, including a 6-million-word corpus of all IEEE Visualisation conference articles published in IEEE Transactions on Visualisation and Computer Graphics 2012 to 2017. We draw on our substantial experience from early work in coordinated and multiple views, and with collocation analysis develop several lists of terms. This research provides insight into term use, a reference for novice and expert authors in visualisation, and contributes a taxonomy of 'multiple view' terms.
  • Item
    DIVA: Exploration and Validation of Hypothesized Drug-Drug Interactions
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Kakar, Tabassum; Qin, Xiao; Rundensteiner, Elke A.; Harrison, Lane; Sahoo, Sanjay K.; De, Suranjan; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    Adverse reactions caused by drug-drug interactions are a major public health concern. Currently, adverse reaction signals are detected through a tedious manual process in which drug safety analysts review a large number of reports collected through post-marketing drug surveillance. While computational techniques in support of this signal analysis are necessary, alone they are not sufficient. In particular, when machine learning techniques are applied to extract candidate signals from reports, the resulting set is (1) too large in size, i.e., exponential to the number of unique drugs and reactions in reports, (2) disconnected from the underlying reports that serve as evidence and context, and (3) ultimately requires human intervention to be validated in the domain context as a true signal warranting action. In this work, we address these challenges though a visual analytics system, DIVA, designed to align with the drug safety analysis workflow by supporting the detection, screening, and verification of candidate drug interaction signals. DIVA's abstractions and encodings are informed by formative interviews with drug safety analysts. DIVA's coordinated visualizations realize a proposed novel augmented interaction data model (AIM) which links signals generated by machine learning techniques with domain-specific metadata critical for signal analysis. DIVA's alignment with the drug review process allows an analyst to interactively screen for important signals, triage signals for in-depth investigation, and validate signals by reviewing the underlying reports that serve as evidence. The evaluation of DIVA encompasses case-studies and interviews by drug analysts at the US Food and Drug Administration - both of which confirm that DIVA indeed is effective in supporting analysts in the critical task of exploring and verifying dangerous drug-drug interactions.
  • Item
    CV3: Visual Exploration, Assessment, and Comparison of CVs
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Filipov, Velitchko; Arleo, Alessio; Federico, Paolo; Miksch, Silvia; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    The Curriculum Vitae (CV, also referred to as ''résumé'') is an established representation of a person's academic and professional history. A typical CV is comprised of multiple sections associated with spatio-temporal, nominal, hierarchical, and ordinal data. The main task of a recruiter is, given a job application with specific requirements, to compare and assess CVs in order to build a short list of promising candidates to interview. Commonly, this is done by viewing CVs in a side-by-side fashion. This becomes challenging when comparing more than two CVs, because the reader is required to switch attention between them. Furthermore, there is no guarantee that the CVs are structured similarly, thus making the overview cluttered and significantly slowing down the comparison process. In order to address these challenges, in this paper we propose ''CV3'', an interactive exploration environment offering users a new way to explore, assess, and compare multiple CVs, to suggest suitable candidates for specific job requirements. We validate our system by means of domain expert feedback whose results highlight both the efficacy of our approach and its limitations. We learned that CV3 eases the overall burden of recruiters thereby assisting them in the selection process.
  • Item
    VIAN: A Visual Annotation Tool for Film Analysis
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Halter, Gaudenz; Ballester-Ripoll, Rafael; Flueckiger, Barbara; Pajarola, Renato; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    While color plays a fundamental role in film design and production, existing solutions for film analysis in the digital humanities address perceptual and spatial color information only tangentially. We introduce VIAN, a visual film annotation system centered on the semantic aspects of film color analysis. The tool enables expert-assessed labeling, curation, visualization and classification of color features based on their perceived context and aesthetic quality. It is the first of its kind that incorporates foreground-background information made possible by modern deep learning segmentation methods. The proposed tool seamlessly integrates a multimedia data management system, so that films can undergo a full color-oriented analysis pipeline.
  • Item
    Characterizing Exploratory Visual Analysis: A Literature Review and Evaluation of Analytic Provenance in Tableau
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Battle, Leilani; Heer, Jeffrey; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    Supporting exploratory visual analysis (EVA) is a central goal of visualization research, and yet our understanding of the process is arguably vague and piecemeal. We contribute a consistent definition of EVA through review of the relevant literature, and an empirical evaluation of existing assumptions regarding how analysts perform EVA using Tableau, a popular visual analysis tool. We present the results of a study where 27 Tableau users answered various analysis questions across 3 datasets. We measure task performance, identify recurring patterns across participants' analyses, and assess variance from task specificity and dataset. We find striking differences between existing assumptions and the collected data. Participants successfully completed a variety of tasks, with over 80% accuracy across focused tasks with measurably correct answers. The observed cadence of analyses is surprisingly slow compared to popular assumptions from the database community. We find significant overlap in analyses across participants, showing that EVA behaviors can be predictable. Furthermore, we find few structural differences between behavior graphs for open-ended and more focused exploration tasks.
  • Item
    An Ontological Framework for Supporting the Design and Evaluation of Visual Analytics Systems
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Chen, Min; Ebert, David; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    Designing, evaluating, and improving visual analytics (VA) systems is a primary area of activities in our discipline. In this paper, we present an ontological framework for recording and categorizing technical shortcomings to be addressed in a VA workflow, reasoning about the causes of such problems, identifying technical solutions, and anticipating secondary effects of the solutions. The methodology is built on the theoretical premise that designing a VA workflow is an optimization of the costbenefit ratio of the processes in the workflow. It makes uses three fundamental measures to group and connect ''symptoms'', ''causes'', ''remedies'', and ''side-effects'', and guide the search for potential solutions to the problems. In terms of requirement analysis and system design, the proposed methodology can enable system designers to explore the decision space in a structured manner. In terms of evaluation, the proposed methodology is time-efficient and complementary to various forms of empirical studies, such as user surveys, controlled experiments, observational studies, focus group discussions, and so on. In general, it reduces the amount of trial-and-error in the lifecycle of VA system development.
  • Item
    Investigating Effects of Visual Anchors on Decision-Making about Misinformation
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Wesslen, Ryan; Santhanam, Sashank; Karduni, Alireza; Cho, Isaac; Shaikh, Samira; Dou, Wenwen; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    Cognitive biases are systematic errors in judgment due to an over-reliance on rule-of-thumb heuristics. Recent research suggests that cognitive biases, like numerical anchoring, transfers to visual analytics in the form of visual anchoring. However, it is unclear how visualization users can be visually anchored and how the anchors affect decision-making. To investigate, we performed a between-subjects laboratory experiment with 94 participants to analyze the effects of visual anchors and strategy cues using a visual analytics system. The decision-making task was to identify misinformation from Twitter news accounts. Participants were randomly assigned to conditions that modified the scenario video (visual anchor) and/or strategy cues provided. Our findings suggest that such interventions affect user activity, speed, confidence, and, under certain circumstances, accuracy. We discuss implications of our results on the forking paths problem and raise concerns on how visualization researchers train users to avoid unintentionally anchoring users and affecting the end result.
  • Item
    A User-based Visual Analytics Workflow for Exploratory Model Analysis
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Cashman, Dylan; Humayoun, Shah Rukh; Heimerl, Florian; Park, Kendall; Das, Subhajit; Thompson, John; Saket, Bahador; Mosca, Abigail; Stasko, John; Endert, Alex; Gleicher, Michael; Chang, Remco; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    Many visual analytics systems allow users to interact with machine learning models towards the goals of data exploration and insight generation on a given dataset. However, in some situations, insights may be less important than the production of an accurate predictive model for future use. In that case, users are more interested in generating of diverse and robust predictive models, verifying their performance on holdout data, and selecting the most suitable model for their usage scenario. In this paper, we consider the concept of Exploratory Model Analysis (EMA), which is defined as the process of discovering and selecting relevant models that can be used to make predictions on a data source. We delineate the differences between EMA and the well-known term exploratory data analysis in terms of the desired outcome of the analytic process: insights into the data or a set of deployable models. The contributions of this work are a visual analytics system workflow for EMA, a user study, and two use cases validating the effectiveness of the workflow. We found that our system workflow enabled users to generate complex models, to assess them for various qualities, and to select the most relevant model for their task.
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    An Exploratory User Study of Visual Causality Analysis
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Yen, Chi-Hsien Eric; Parameswaran, Aditya; Fu, Wai-Tat; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    Interactive visualization tools are being used by an increasing number of members of the general public; however, little is known about how, and how well, people use visualizations to infer causality. Adapted from the mediation causal model, we designed an analytic framework to systematically evaluate human performance, strategies, and pitfalls in a visual causal reasoning task. We recruited 24 participants and asked them to identify the mediators in a fictitious dataset using bar charts and scatter plots within our visualization interface. The results showed that the accuracy of their responses as to whether a variable is a mediator significantly decreased when a confounding variable directly influenced the variable being analyzed. Further analysis demonstrated how individual visualization exploration strategies and interfaces might influence reasoning performance. We also identified common strategies and pitfalls in their causal reasoning processes. Design implications for how future visual analytics tools can be designed to better support causal inference are discussed.
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    Toward Understanding Representation Methods in Visualization Recommendations through Scatterplot Construction Tasks
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) L'Yi, Sehi; Chang, Youli; Shin, DongHwa; Seo, Jinwook; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    Most visualization recommendation systems predominantly rely on graphical previews to describe alternative visual encodings. However, since InfoVis novices are not familiar with visual representations (e.g., interpretation barriers [GTS10]), novices might have difficulty understanding and choosing recommended visual encodings. As an initial step toward understanding effective representation methods for visualization recommendations, we investigate the effectiveness of three representation methods (i.e., previews, animated transitions, and textual descriptions) under scatterplot construction tasks. Our results show how different representations individually and cooperatively help users understand and choose recommended visualizations, for example, by supporting their expect-and-confirm process. Based on our study results, we discuss design implications for visualization recommendation interfaces.
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    ClustMe: A Visual Quality Measure for Ranking Monochrome Scatterplots based on Cluster Patterns
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Abbas, Mostafa M.; Aupetit, Michaël; Sedlmair, Michael; Bensmail, Halima; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    We propose ClustMe, a new visual quality measure to rank monochrome scatterplots based on cluster patterns. ClustMe is based on data collected from a human-subjects study, in which 34 participants judged synthetically generated cluster patterns in 1000 scatterplots. We generated these patterns by carefully varying the free parameters of a simple Gaussian Mixture Model with two components. and asked the participants to count the number of clusters they could see (1 or more than 1). Based on the results, we form ClustMe by selecting the model that best predicts these human judgments among 7 different state-of-the-art merging techniques (DEMP). To quantitatively evaluate ClustMe, we conducted a second study, in which 31 human subjects ranked 435 pairs of scatterplots of real and synthetic data in terms of cluster patterns complexity. We use this data to compare ClustMe's performance to 4 other state-of-the-art clustering measures, including the well-known Clumpiness scagnostics. We found that of all measures, ClustMe is in strongest agreement with the human rankings.
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    Oui! Outlier Interpretation on Multi-dimensional Data via Visual Analytics
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Zhao, Xun; Cui, Weiwei; Wu, Yanhong; Zhang, Haidong; Qu, Huamin; Zhang, Dongmei; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    Outliers, the data instances that do not conform with normal patterns in a dataset, are widely studied in various domains, such as cybersecurity, social analysis, and public health. By detecting and analyzing outliers, users can either gain insights into abnormal patterns or purge the data of errors. However, different domains usually have different considerations with respect to outliers. Understanding the defining characteristics of outliers is essential for users to select and filter appropriate outliers based on their domain requirements. Unfortunately, most existing work focuses on the efficiency and accuracy of outlier detection, neglecting the importance of outlier interpretation. To address these issues, we propose Oui, a visual analytic system that helps users understand, interpret, and select the outliers detected by various algorithms. We also present a usage scenario on a real dataset and a qualitative user study to demonstrate the effectiveness and usefulness of our system.
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    SurgeryCuts: Embedding Additional Information in Maps without Occluding Features
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Angelini, Marco; Buchmüller, Juri; Keim, Daniel A.; Meschenmoser, Philipp; Santucci, Giuseppe; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    Visualizing contextual information to a map often comes at the expense of overplotting issues. Especially for use cases with relevant map features in the immediate vicinity of an information to add, occlusion of the relevant map context should be avoided. We present SurgeryCuts, a map manipulation technique for the creation of additional canvas area for contextual visualizations on maps. SurgeryCuts is occlusion-free and does not shift, zoom or alter the map viewport. Instead, relevant parts of the map can be cut apart. The affected area is controlledly distorted using a parameterizable warping function fading out the map distortion depending on the distance to the cut. We define extended metrics for our approach and compare to related approaches. As well, we demonstrate the applicability of our approach at the example of tangible use cases and a comparative user study.
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    Visualizing for the Non-Visual: Enabling the Visually Impaired to Use Visualization
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Choi, Jinho; Jung, Sanghun; Park, Deok Gun; Choo, Jaegul; Elmqvist, Niklas; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    The majority of visualizations on the web are still stored as raster images, making them inaccessible to visually impaired users. We propose a deep-neural-network-based approach that automatically recognizes key elements in a visualization, including a visualization type, graphical elements, labels, legends, and most importantly, the original data conveyed in the visualization. We leverage such extracted information to provide visually impaired people with the reading of the extracted information. Based on interviews with visually impaired users, we built a Google Chrome extension designed to work with screen reader software to automatically decode charts on a webpage using our pipeline. We compared the performance of the back-end algorithm with existing methods and evaluated the utility using qualitative feedback from visually impaired users.
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    The Dependent Vectors Operator
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Hofmann, Lutz; Sadlo, Filip; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    In this paper, we generalize the parallel vectors operator due to Peikert and Roth to arbitrary dimension, i.e., to four-dimensional fields and beyond. Whereas the original operator tested for parallelism of two (derived) 2D or 3D vector fields, we reformulate the concept in terms of linear dependency of sets of vector fields, and propose a generic technique to extract and filter the solution manifolds.We exemplify our approach for vortex cores, bifurcations, and ridges as well as valleys in higher dimensions.
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    A Visual Tool for the Analysis of Algorithms for Tomographic Fiber Reconstruction in Materials Science
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Fröhler, Bernhard; Elberfeld, Tim; Möller, Torsten; Hege, Hans-Christian; Weissenböck, Johannes; De Beenhouwer, Jan; Sijbers, Jan; Kastner, Johann; Heinzl, Christoph; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    We present visual analysis methods for the evaluation of tomographic fiber reconstruction algorithms by means of analysis, visual debugging and comparison of reconstructed fibers in materials science. The methods are integrated in a tool (FIAKER) that supports the entire workflow. It enables the analysis of various fiber reconstruction algorithms, of differently parameterized fiber reconstruction algorithms and of individual steps in iterative fiber reconstruction algorithms. Insight into the performance of fiber reconstruction algorithms is obtained by a list-based ranking interface. A 3D view offers interactive visualization techniques to gain deeper insight, e.g., into the aggregated quality of the examined fiber reconstruction algorithms and parameterizations. The tool was designed in close collaboration with researchers who work with fiber-reinforced polymers on a daily basis and develop algorithms for tomographic reconstruction and characterization of such materials. We evaluate the tool using synthetic datasets as well as tomograms of real materials. Five case studies certify the usefulness of the tool, showing that it significantly accelerates the analysis and provides valuable insights that make it possible to improve the fiber reconstruction algorithms. The main contribution of the paper is the well-considered combination of methods and their seamless integration into a visual tool that supports the entire workflow. Further findings result from the analysis of (dis-)similarity measures for fibers as well as from the discussion of design decisions. It is also shown that the generality of the analytical methods allows a wider range of applications, such as the application in pore space analysis.
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    Robust Reference Frame Extraction from Unsteady 2D Vector Fields with Convolutional Neural Networks
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Kim, Byungsoo; Günther, Tobias; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    Robust feature extraction is an integral part of scientific visualization. In unsteady vector field analysis, researchers recently directed their attention towards the computation of near-steady reference frames for vortex extraction, which is a numerically challenging endeavor. In this paper, we utilize a convolutional neural network to combine two steps of the visualization pipeline in an end-to-end manner: the filtering and the feature extraction. We use neural networks for the extraction of a steady reference frame for a given unsteady 2D vector field. By conditioning the neural network to noisy inputs and resampling artifacts, we obtain numerically stabler results than existing optimization-based approaches. Supervised deep learning typically requires a large amount of training data. Thus, our second contribution is the creation of a vector field benchmark data set, which is generally useful for any local deep learning-based feature extraction. Based on Vatistas velocity profile, we formulate a parametric vector field mixture model that we parameterize based on numerically-computed example vector fields in near-steady reference frames. Given the parametric model, we can efficiently synthesize thousands of vector fields that serve as input to our deep learning architecture. The proposed network is evaluated on an unseen numerical fluid flow simulation.
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    An Interactive Visualization System for Large Sets of Phase Space Trajectories
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Neuroth, Tyson; Sauer, Franz; Ma, Kwan-Liu; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    We introduce a visual analysis system with GPU acceleration techniques for large sets of trajectories from complex dynamical systems. The approach is based on an interactive Boolean combination of subsets into a Focus+Context phase-space visualization. We achieve high performance through efficient bitwise algorithms utilizing runtime generated GPU shaders and kernels. This enables a higher level of interactivity for visualizing the large multivariate trajectory data. We explain how our design meets a set of carefully considered analysis requirements, provide performance results, and demonstrate utility through case studies with many-particle simulation data from two application areas.
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    Visualization of Equivalence in 2D Bivariate Fields
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Zheng, Boyan; Rieck, Bastian; Leitte, Heike; Sadlo, Filip; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    In this paper, we show how the equivalence property leads to the novel concept of equivalent regions in mappings from Rn to Rn. We present a technique for obtaining these regions both in the domain and the codomain of such a mapping, and determine their correspondence. This enables effective investigation of variation equivalence within mappings, and between mappings in terms of comparative visualization. We implement our approach for n = 2, and demonstrate its utility using different examples.
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    Towards Glyphs for Uncertain Symmetric Second-Order Tensors
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Gerrits, Tim; Rössl, Christian; Theisel, Holger; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    Measured data often incorporates some amount of uncertainty, which is generally modeled as a distribution of possible samples. In this paper, we consider second-order symmetric tensors with uncertainty. In the 3D case, this means the tensor data consists of 6 coefficients - uncertainty, however, is encoded by 21 coefficients assuming a multivariate Gaussian distribution as model. The high dimension makes the direct visualization of tensor data with uncertainty a difficult problem, which was until now unsolved. The contribution of this paper consists in the design of glyphs for uncertain second-order symmetric tensors in 2D and 3D. The construction consists of a standard glyph for the mean tensor that is augmented by a scalar field that represents uncertainty. We show that this scalar field and therefore the displayed glyph encode the uncertainty comprehensively, i.e., there exists a bijective map between the glyph and the parameters of the distribution. Our approach can extend several classes of existing glyphs for symmetric tensors to additionally encode uncertainty and therefore provides a possible foundation for further uncertain tensor glyph design. For demonstration, we choose the well-known superquadric glyphs, and we show that the uncertainty visualization satisfies all their design constraints.
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    Robust Extraction and Simplification of 2D Symmetric Tensor Field Topology
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Jankowai, Jochen; Wang, Bei; Hotz, Ingrid; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    In this work, we propose a controlled simplification strategy for degenerated points in symmetric 2D tensor fields that is based on the topological notion of robustness. Robustness measures the structural stability of the degenerate points with respect to variation in the underlying field. We consider an entire pipeline for generating a hierarchical set of degenerate points based on their robustness values. Such a pipeline includes the following steps: the stable extraction and classification of degenerate points using an edge labeling algorithm, the computation and assignment of robustness values to the degenerate points, and the construction of a simplification hierarchy. We also discuss the challenges that arise from the discretization and interpolation of real world data.
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    Visualization Support for Developing a Matrix Calculus Algorithm: A Case Study
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Giesen, Joachim; Klaus, Julien; Laue, Sören; Schreck, Ferdinand; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    The development of custom interactive visualization tools for specific domains and applications has been made much simpler recently by a surge of visualization tools, libraries and frameworks. Most of these tools are developed for classical data science applications, where a user is supported in analyzing measured or simulated data. But recently, there has also been an increasing interest in visual support for understanding machine learning algorithms and frameworks, especially for deep learning. Many, if not most, of the visualization support for (deep) learning addresses the developer of the learning system and not the end user (data scientist). Here we show on a specific example, namely the development of a matrix calculus algorithm, that supporting visualizations can also greatly benefit the development of algorithms in classical domains like in our case computer algebra. The idea is similar to visually supporting the understanding of learning algorithms, namely provide the developer with an interactive, visual tool that provides insights into the workings and, importantly, also into the failures of the algorithm under development. Developing visualization support for matrix calculus development went similar as the development of more traditional visual support systems for data analysts. First, we had to acquaint ourselves with the problem, its language and challenges by talking to the core developer of the matrix calculus algorithm. Once we understood the challenge, it was fairly easy to develop visual support that streamlined the development of the matrix calculus algorithm significantly.
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    Examining Implicit Discretization in Spectral Schemes
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Quinan, P. Samuel; Padilla, Lace M. K.; Creem-Regehr, Sarah H.; Meyer, Miriah; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    Two of the primary reasons rainbow color maps are considered ineffective trace back to the idea that they implicitly discretize encoded data into hue-based bands, yet no research addresses what this discretization looks like or how consistent it is across individuals. This paper presents an exploratory study designed to empirically investigate the implicit discretization of common spectral schemes and explore whether the phenomenon can be modeled by variations in lightness, chroma, and hue. Our results suggest that three commonly used rainbow color maps are implicitly discretized with consistency across individuals. The results also indicate, however, that this implicit discretization varies across different datasets, in a way that suggests the visualization community's understanding of both rainbow color maps, and more generally effective color usage, remains incomplete.
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    Bridging the Data Analysis Communication Gap Utilizing a Three-Component Summarized Line Graph
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Yau, Calvin; Karimzadeh, Morteza; Surakitbanharn, Chittayong; Elmqvist, Niklas; Ebert, David; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    Communication-minded visualizations are designed to provide their audience-managers, decision-makers, and the public-with new knowledge. Authoring such visualizations effectively is challenging because the audience often lacks the expertise, context, and time that professional analysts have at their disposal to explore and understand datasets. We present a novel summarized line graph visualization technique designed specifically for data analysts to communicate data to decision-makers more effectively and efficiently. Our summarized line graph reduces a large and detailed dataset of multiple quantitative time-series into (1) representative data that provides a quick takeaway of the full dataset; (2) analytical highlights that distinguish specific insights of interest; and (3) a data envelope that summarizes the remaining aggregated data. Our summarized line graph achieved the best overall results when evaluated against line graphs, band graphs, stream graphs, and horizon graphs on four representative tasks.
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    ChronoCorrelator: Enriching Events with Time Series
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) van Dortmont, Martijn; Elzen, Stef van den; Wijk, Jarke J. van; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    Event sequences and time series are widely recorded in many application domains; examples are stock market prices, electronic health records, server operation and performance logs. Common goals for recording are monitoring, root cause analysis and predictive analytics. Current analysis methods generally focus on the exploration of either event sequences or time series. However, deeper insights are gained by combining both. We present a visual analytics approach where users can explore both time series and event data simultaneously, combining visualization, automated methods and human interaction. We enable users to iteratively refine the visualization. Correlations between event sequences and time series can be found by means of an interactive algorithm, which also computes the presence of monotonic effects. We illustrate the effectiveness of our method by applying it to real world and synthetic data sets.
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    Visual-Interactive Preprocessing of Multivariate Time Series Data
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Bernard, Jürgen; Hutter, Marco; Reinemuth, Heiko; Pfeifer, Hendrik; Bors, Christian; Kohlhammer, Jörn; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    Pre-processing is a prerequisite to conduct effective and efficient downstream data analysis. Pre-processing pipelines often require multiple routines to address data quality challenges and to bring the data into a usable form. For both the construction and the refinement of pre-processing pipelines, human-in-the-loop approaches are highly beneficial. This particularly applies to multivariate time series, a complex data type with multiple values developing over time. Due to the high specificity of this domain, it has not been subject to in-depth research in visual analytics. We present a visual-interactive approach for preprocessing multivariate time series data with the following aspects. Our approach supports analysts to carry out six core analysis tasks related to pre-processing of multivariate time series. To support these tasks, we identify requirements to baseline toolkits that may help practitioners in their choice. We characterize the space of visualization designs for uncertainty-aware pre-processing and justify our decisions. Two usage scenarios demonstrate applicability of our approach, design choices, and uncertainty visualizations for the six analysis tasks. This work is one step towards strengthening the visual analytics support for data pre-processing in general and for uncertainty-aware pre-processing of multivariate time series in particular.
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    A Geometric Optimization Approach for the Detection and Segmentation of Multiple Aneurysms
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Lawonn, Kai; Meuschke, Monique; Wickenhöfer, Ralph; Preim, Bernhard; Hildebrandt, Klaus; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    We present a method for detecting and segmenting aneurysms in blood vessels that facilitates the assessment of risks associated with the aneurysms. The detection and analysis of aneurysms is important for medical diagnosis as aneurysms bear the risk of rupture with fatal consequences for the patient. For risk assessment and treatment planning, morphological descriptors, such as the height and width of the aneurysm, are used. Our system enables the fast detection, segmentation and analysis of single and multiple aneurysms. The method proceeds in two stages plus an optional third stage in which the user interacts with the system. First, a set of aneurysm candidate regions is created by segmenting regions of the vessels. Second, the aneurysms are detected by a classification of the candidates. The third stage allows users to adjust and correct the result of the previous stages using a brushing interface. When the segmentation of the aneurysm is complete, the corresponding ostium curves and morphological descriptors are computed and a report including the results of the analysis and renderings of the aneurysms is generated. The novelty of our approach lies in combining an analytic characterization of aneurysms and vessels to generate a list of candidate regions with a classifier trained on data to identify the aneurysms in the candidate list. The candidate generation is modeled as a global combinatorial optimization problem that is based on a local geometric characterization of aneurysms and vessels and can be efficiently solved using a graph cut algorithm. For the aneurysm classification scheme, we identified four suitable features and modeled appropriate training data. An important aspect of our approach is that the resulting system is fast enough to allow for user interaction with the global optimization by specifying additional constraints via a brushing interface.
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    Interactive Volumetric Visual Analysis of Glycogen-derived Energy Absorption in Nanometric Brain Structures
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Agus, Marco; Calì, Corrado; Al-Awami, Ali K.; Gobbetti, Enrico; Magistretti, Pierre J.; Hadwiger, Markus; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    Digital acquisition and processing techniques are changing the way neuroscience investigation is carried out. Emerging applications range from statistical analysis on image stacks to complex connectomics visual analysis tools targeted to develop and test hypotheses of brain development and activity. In this work, we focus on neuroenergetics, a field where neuroscientists analyze nanoscale brain morphology and relate energy consumption to glucose storage in form of glycogen granules. In order to facilitate the understanding of neuroenergetic mechanisms, we propose a novel customized pipeline for the visual analysis of nanometric-level reconstructions based on electron microscopy image data. Our framework supports analysis tasks by combining i) a scalable volume visualization architecture able to selectively render image stacks and corresponding labelled data, ii) a method for highlighting distance-based energy absorption probabilities in form of glow maps, and iii) a hybrid connectivitybased and absorption-based interactive layout representation able to support queries for selective analysis of areas of interest and potential activity within the segmented datasets. This working pipeline is currently used in a variety of studies in the neuroenergetics domain. Here, we discuss a test case in which the framework was successfully used by domain scientists for the analysis of aging effects on glycogen metabolism, extracting knowledge from a series of nanoscale brain stacks of rodents somatosensory cortex.
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    Analysis of Long Molecular Dynamics Simulations Using Interactive Focus+Context Visualization
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Byška, Jan; Trautner, Thomas; Marques, Sérgio M.; Damborský, Jiří; Kozlíková, Barbora; Waldner, Manuela; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    Analyzing molecular dynamics (MD) simulations is a key aspect to understand protein dynamics and function. With increasing computational power, it is now possible to generate very long and complex simulations, which are cumbersome to explore using traditional 3D animations of protein movements. Guided by requirements derived from multiple focus groups with protein engineering experts, we designed and developed a novel interactive visual analysis approach for long and crowded MD simulations. In this approach, we link a dynamic 3D focus+context visualization with a 2D chart of time series data to guide the detection and navigation towards important spatio-temporal events. The 3D visualization renders elements of interest in more detail and increases the temporal resolution dependent on the time series data or the spatial region of interest. In case studies with different MD simulation data sets and research questions, we found that the proposed visual analysis approach facilitates exploratory analysis to generate, confirm, or reject hypotheses about causalities. Finally, we derived design guidelines for interactive visual analysis of complex MD simulation data.
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    Scalable Ray Tracing Using the Distributed FrameBuffer
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Usher, Will; Wald, Ingo; Amstutz, Jefferson; Günther, Johannes; Brownlee, Carson; Pascucci, Valerio; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    Image- and data-parallel rendering across multiple nodes on high-performance computing systems is widely used in visualization to provide higher frame rates, support large data sets, and render data in situ. Specifically for in situ visualization, reducing bottlenecks incurred by the visualization and compositing is of key concern to reduce the overall simulation runtime. Moreover, prior algorithms have been designed to support either image- or data-parallel rendering and impose restrictions on the data distribution, requiring different implementations for each configuration. In this paper, we introduce the Distributed FrameBuffer, an asynchronous image-processing framework for multi-node rendering. We demonstrate that our approach achieves performance superior to the state of the art for common use cases, while providing the flexibility to support a wide range of parallel rendering algorithms and data distributions. By building on this framework, we extend the open-source ray tracing library OSPRay with a data-distributed API, enabling its use in data-distributed and in situ visualization applications.
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    Ray Tracing Generalized Tube Primitives: Method and Applications
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Han, Mengjiao; Wald, Ingo; Usher, Will; Wu, Qi; Wang, Feng; Pascucci, Valerio; Hansen, Charles D.; Johnson, Chris R.; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    We present a general high-performance technique for ray tracing generalized tube primitives. Our technique efficiently supports tube primitives with fixed and varying radii, general acyclic graph structures with bifurcations, and correct transparency with interior surface removal. Such tube primitives are widely used in scientific visualization to represent diffusion tensor imaging tractographies, neuron morphologies, and scalar or vector fields of 3D flow. We implement our approach within the OSPRay ray tracing framework, and evaluate it on a range of interactive visualization use cases of fixed- and varying-radius streamlines, pathlines, complex neuron morphologies, and brain tractographies. Our proposed approach provides interactive, high-quality rendering, with low memory overhead.
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    Visual Analysis of Charge Flow Networks for Complex Morphologies
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Kottravel, Sathish; Falk, Martin; Bin Masood, Talha; linares, mathieu; Hotz, Ingrid; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    In the field of organic electronics, understanding complex material morphologies and their role in efficient charge transport in solar cells is extremely important. Related processes are studied using the Ising model and Kinetic Monte Carlo simulations resulting in large ensembles of stochastic trajectories. Naive visualization of these trajectories, individually or as a whole, does not lead to new knowledge discovery through exploration. In this paper, we present novel visualization and exploration methods to analyze this complex dynamic data, which provide succinct and meaningful abstractions leading to scientific insights. We propose a morphology abstraction yielding a network composed of material pockets and the interfaces, which serves as backbone for the visualization of the charge diffusion. The trajectory network is created using a novel way of implicitly attracting the trajectories to the skeleton of the morphology relying on a relaxation process. Each individual trajectory is then represented as a connected sequence of nodes in the skeleton. The final network summarizes all of these sequences in a single aggregated network. We apply our method to three different morphologies and demonstrate its suitability for exploring this kind of data.
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    IGM-Vis: Analyzing Intergalactic and Circumgalactic Medium Absorption Using Quasar Sightlines in a Cosmic Web Context
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Burchett, Joseph N.; Abramov, David; Otto, Jasmine Tan; Artanegara, Cassia; Prochaska, Jason Xavier; Forbes, Angus G.; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    We introduce IGM-Vis, a novel astrophysics visualization and data analysis application for investigating galaxies and the gas that surrounds them in context with their larger scale environment, the Cosmic Web. Environment is an important factor in the evolution of galaxies from actively forming stars to quiescent states with little, if any, discernible star formation activity. The gaseous halos of galaxies (the circumgalactic medium, or CGM) play a critical role in their evolution, because the gas necessary to fuel star formation and any gas expelled from widely observed galactic winds must encounter this interface region between galaxies and the intergalactic medium (IGM). We present a taxonomy of tasks typically employed in IGM/CGM studies informed by a survey of astrophysicists at various career levels, and demonstrate how these tasks are facilitated via the use of our visualization software. Finally, we evaluate the effectiveness of IGM-Vis through two in-depth use cases that depict real-world analysis sessions that use IGM/CGM data.
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    Analysis of Decadal Climate Predictions with User-guided Hierarchical Ensemble Clustering
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Kappe, Christopher; Böttinger, Michael; Leitte, Heike; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    In order to gain probabilistic results, ensemble simulation techniques are increasingly applied in the weather and climate sciences (as well as in various other scientific disciplines). In many cases, however, only mean results or other abstracted quantities such as percentiles are used for further analyses and dissemination of the data. In this work, we aim at a more detailed visualization of the temporal development of the whole ensemble that takes the variability of all single members into account. We propose a visual analytics tool that allows an effective analysis process based on a hierarchical clustering of the time-dependent scalar fields. The system includes a flow chart that shows the ensemble members' cluster affiliation over time, reflecting the whole cluster hierarchy. The latter one can be dynamically explored using a visualization derived from a dendrogram. As an aid in linking the different views, we have developed an adaptive coloring scheme that takes into account cluster similarity and the containment relationships. Finally, standard visualizations of the involved field data (cluster means, ground truth data, etc.) are also incorporated. We include results of our work on real-world datasets to showcase the utility of our approach.
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    Evaluating Image Quality Measures to Assess the Impact of Lossy Data Compression Applied to Climate Simulation Data
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Baker, Allison; Hammerling, Dorit; Turton, Terece; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    Applying lossy data compression to climate model output is an attractive means of reducing the enormous volumes of data generated by climate models. However, because lossy data compression does not exactly preserve the original data, its application to scientific data must be done judiciously. To this end, a collection of measures is being developed to evaluate various aspects of lossy compression quality on climate model output. Given the importance of data visualization to climate scientists interacting with model output, any suite of measures must include a means of assessing whether images generated from the compressed model data are noticeably different from images based on the original model data. Therefore, in this work we conduct a forcedchoice visual evaluation study with climate model data that surveyed more than one hundred participants with domain relevant expertise. In addition to the images created from unaltered climate model data, study images are generated from model data that is subjected to two different types of lossy compression approaches and multiple levels (amounts) of compression. Study participants indicate whether a visual difference can be seen, with respect to the reference image, due to lossy compression effects. We assess the relationship between the perceptual scores from the user study to a number of common (full reference) image quality assessment (IQA) measures, and use statistical models to suggest appropriate measures and thresholds for evaluating lossily compressed climate data. We find the structural similarity index (SSIM) to perform the best, and our findings indicate that the threshold required for climate model data is much higher than previous findings in the literature.
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    Kyrix: Interactive Pan/Zoom Visualizations at Scale
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Tao, Wenbo; Liu, Xiaoyu; Wang, Yedi; Battle, Leilani; Demiralp, Çagatay; Chang, Remco; Stonebraker, Michael; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    Pan and zoom are basic yet powerful interaction techniques for exploring large datasets. However, existing zoomable UI toolkits such as Pad++ and ZVTM do not provide the backend database support and data-driven primitives that are necessary for creating large-scale visualizations. This limitation in existing general-purpose toolkits has led to many purpose-built solutions (e.g. Google Maps and ForeCache) that address the issue of scalability but cannot be easily extended to support visualizations beyond their intended data types and usage scenarios. In this paper, we introduce Kyrix to ease the process of creating general and large-scale web-based pan/zoom visualizations. Kyrix is an integrated system that provides the developer with a concise and expressive declarative language along with a backend support for performance optimization of large-scale data. To evaluate the scalability of Kyrix, we conducted a set of benchmarked experiments and show that Kyrix can support high interactivity (with an average latency of 100 ms or below) on pan/zoom visualizations of 100 million data points. We further demonstrate the accessibility of Kyrix through an observational study with 8 developers. Results indicate that developers can quickly learn Kyrix's underlying declarative model to create scalable pan/zoom visualizations. Finally, we provide a gallery of visualizations and show that Kyrix is expressive and flexible in that it can support the developer in creating a wide range of customized visualizations across different application domains and data types.
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    Designing Animated Transitions to Convey Aggregate Operations
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Kim, Younghoon; Correll, Michael; Heer, Jeffrey; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    Data can be aggregated in many ways before being visualized in charts, profoundly affecting what a chart conveys. Despite this importance, the type of aggregation is often communicated only via axis titles. In this paper, we investigate the use of animation to disambiguate different types of aggregation and communicate the meaning of aggregate operations. We present design rationales for animated transitions depicting aggregate operations and present the results of an experiment assessing the impact of these different transitions on identification tasks. We find that judiciously staged animated transitions can improve subjects' accuracy at identifying the aggregation performed, though sometimes with longer response times than with static transitions. Through an analysis of participants' rankings and qualitative responses, we find a consistent preference for animation over static transitions and highlight visual features subjects report relying on to make their judgments. We conclude by extending our animation designs to more complex charts of aggregated data such as box plots and bootstrapped confidence intervals.
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    Hybrid Touch/Tangible Spatial 3D Data Selection
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Besançon, Lonni; Sereno, Mickael; Yu, Lingyun; Ammi, Mehdi; Isenberg, Tobias; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    We discuss spatial selection techniques for three-dimensional datasets. Such 3D spatial selection is fundamental to exploratory data analysis. While 2D selection is efficient for datasets with explicit shapes and structures, it is less efficient for data without such properties. We first propose a new taxonomy of 3D selection techniques, focusing on the amount of control the user has to define the selection volume. We then describe the 3D spatial selection technique Tangible Brush, which gives manual control over the final selection volume. It combines 2D touch with 6-DOF 3D tangible input to allow users to perform 3D selections in volumetric data. We use touch input to draw a 2D lasso, extruding it to a 3D selection volume based on the motion of a tangible, spatially-aware tablet. We describe our approach and present its quantitative and qualitative comparison to state-of-the-art structure-dependent selection. Our results show that, in addition to being dataset-independent, Tangible Brush is more accurate than existing dataset-dependent techniques, thus providing a trade-off between precision and effort.
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    Focus+Context Exploration of Hierarchical Embeddings
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Höllt, Thomas; Vilanova, Anna; Pezzotti, Nicola; Lelieveldt, Boudewijn P. F.; Hauser, Helwig; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    Hierarchical embeddings, such as HSNE, address critical visual and computational scalability issues of traditional techniques for dimensionality reduction. The improved scalability comes at the cost of the need for increased user interaction for exploration. In this paper, we provide a solution for the interactive visual Focus+Context exploration of such embeddings. We explain how to integrate embedding parts from different levels of detail, corresponding to focus and context groups, in a joint visualization. We devise an according interaction model that relates typical semantic operations on a Focus+Context visualization with the according changes in the level-of-detail-hierarchy of the embedding, including also a mode for comparative Focus+Context exploration and extend HSNE to incorporate the presented interaction model. In order to demonstrate the effectiveness of our approach, we present a use case based on the visual exploration of multi-dimensional images.
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    Route-Aware Edge Bundling for Visualizing Origin-Destination Trails in Urban Traffic
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Zeng, Wei; Shen, Qiaomu; Jiang, Yuzhe; Telea, Alexandru; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    Origin-destination (OD) trails describe movements across space. Typical visualizations thereof use either straight lines or plot the actual trajectories. To reduce clutter inherent to visualizing large OD datasets, bundling methods can be used. Yet, bundling OD trails in urban traffic data remains challenging. Two specific reasons hereof are the constraints implied by the underlying road network and the difficulty of finding good bundling settings. To cope with these issues, we propose a new approach called Route Aware Edge Bundling (RAEB). To handle road constraints, we first generate a hierarchical model of the road-and-trajectory data. Next, we derive optimal bundling parameters, including kernel size and number of iterations, for a user-selected level of detail of this model, thereby allowing users to explicitly trade off simplification vs accuracy. We demonstrate the added value of RAEB compared to state-of-the-art trail bundling methods on both synthetic and real-world traffic data for tasks that include the preservation of road network topology and the support of multiscale exploration.
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    Bird's-Eye - Large-Scale Visual Analytics of City Dynamics using Social Location Data
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Krueger, Robert; Han, Qi; Ivanov, Nikolay; Mahtal, Sanae; Thom, Dennis; Pfister, Hanspeter; Ertl, Thomas; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    The analysis of behavioral city dynamics, such as temporal patterns of visited places and citizens' mobility routines, is an essential task for urban and transportation planning. Social media applications such as Foursquare and Twitter provide access to large-scale and up-to-date dynamic movement data that not only help to understand the social life and pulse of a city but also to maintain and improve urban infrastructure. However, the fast growth rate of this data poses challenges for conventional methods to provide up-to-date, flexible analysis. Therefore, planning authorities barely consider it. We present a system and design study to leverage social media data that assist urban and transportation planners to achieve better monitoring and analysis of city dynamics such as visited places and mobility patterns in large metropolitan areas. We conducted a goal-and-task analysis with urban planning experts. To address these goals, we designed a system with a scalable data monitoring back-end and an interactive visual analytics interface. The monitoring component uses intelligent pre-aggregation to allow dynamic queries in near real-time. The visual analytics interface leverages unsupervised learning to reveal clusters, routines, and unusual behavior in massive data, allowing to understand patterns in time and space. We evaluated our approach based on a qualitative user study with urban planning experts which demonstrates that intuitive integration of advanced analytical tools with visual interfaces is pivotal in making behavioral city dynamics accessible to practitioners. Our interviews also revealed areas for future research.
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    Topic Tomographies (TopTom): a Visual Approach to Distill Information From Media Streams
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Gobbo, Beatrice; Balsamo, Duilio; Mauri, Michele; Bajardi, Paolo; Panisson, André; CIUCCARELLI, PAOLO; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    In this paper we present TopTom, a digital platform whose goal is to provide analytical and visual solutions for the exploration of a dynamic corpus of user-generated messages and media articles, with the aim of i) distilling the information from thousands of documents in a low-dimensional space of explainable topics, ii) cluster them in a hierarchical fashion while allowing to drill down to details and stories as constituents of the topics, iii) spotting trends and anomalies. TopTom implements a batch processing pipeline able to run both in near-real time with time stamped data from streaming sources and on historical data with a temporal dimension in a cold start mode. The resulting output unfolds along three main axes: time, volume and semantic similarity (i.e. topic hierarchical aggregation). To allow the browsing of data in a multiscale fashion and the identification of anomalous behaviors, three visual metaphors were adopted from biological and medical fields to design visualizations, i.e. the flowing of particles in a coherent stream, tomographic cross sectioning and contrast-like analysis of biological tissues. The platform interface is composed by three main visualizations with coherent and smooth navigation interactions: calendar view, flow view, and temporal cut view. The integration of these three visual models with the multiscale analytic pipeline proposes a novel system for the identification and exploration of topics from unstructured texts. We evaluated the system using a collection of documents about the emerging opioid epidemics in the United States.
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    Segmentifier: Interactive Refinement of Clickstream Data
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Dextras-Romagnino, Kimberly; Munzner, Tamara; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    Clickstream data has the potential to provide insights into e-commerce consumer behavior, but previous techniques fall short of handling the scale and complexity of real-world datasets because they require relatively clean and small input. We present Segmentifier, a novel visual analytics interface that supports an iterative process of refining collections of action sequences into meaningful segments. We present task and data abstractions for clickstream data analysis, leading to a high-level model built around an iterative view-refine-record loop with outcomes of conclude with an answer, export segment for further analysis in downstream tools, or abandon the question for a more fruitful analysis path. Segmentifier supports fast and fluid refinement of segments through tightly coupled visual encoding and interaction with a rich set of views that show evocative derived attributes for segments, sequences, and actions in addition to underlying raw sequences. These views support fast and fluid refinement of segments through filtering and partitioning attribute ranges. Interactive visual queries on custom action sequences are aggregated according to a three-level hierarchy. Segmentifier features a detailed glyph-based visual history of the automatically recorded analysis process showing the provenance of each segment as an analysis path of attribute constraints. We demonstrate the effectiveness of our approach through a usage scenario with real-world data and a case study documenting the insights gained by a corporate e-commerce analyst.
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    Augmenting Tactile 3D Data Navigation With Pressure Sensing
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Wang, Xiyao; Besançon, Lonni; Ammi, Mehdi; Isenberg, Tobias; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    We present a pressure-augmented tactile 3D data navigation technique, specifically designed for small devices, motivated by the need to support the interactive visualization beyond traditional workstations. While touch input has been studied extensively on large screens, current techniques do not scale to small and portable devices. We use phone-based pressure sensing with a binary mapping to separate interaction degrees of freedom (DOF) and thus allow users to easily select different manipulation schemes (e. g., users first perform only rotation and then with a simple pressure input to switch to translation). We compare our technique to traditional 3D-RST (rotation, scaling, translation) using a docking task in a controlled experiment. The results show that our technique increases the accuracy of interaction, with limited impact on speed. We discuss the implications for 3D interaction design and verify that our results extend to older devices with pseudo pressure and are valid in realistic phone usage scenarios.
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    InsideInsights: Integrating Data-Driven Reporting in Collaborative Visual Analytics
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Mathisen, Andreas; Horak, Tom; Klokmose, Clemens Nylandsted; Grønbæk, Kaj; Elmqvist, Niklas; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    Analyzing complex data is a non-linear process that alternates between identifying discrete facts and developing overall assessments and conclusions. In addition, data analysis rarely occurs in solitude; multiple collaborators can be engaged in the same analysis, or intermediate results can be reported to stakeholders. However, current data-driven communication tools are detached from the analysis process and promote linear stories that forego the hierarchical and branching nature of data analysis, which leads to either too much or too little detail in the final report. We propose a conceptual design for integrated data-driven reporting that allows for iterative structuring of insights into hierarchies linked to analytic provenance and chosen analysis views. The hierarchies become dynamic and interactive reports where collaborators can review and modify the analysis at a desired level of detail. Our web-based INSIDEINSIGHTS system provides interaction techniques to annotate states of analytic components, structure annotations, and link them to appropriate presentation views. We demonstrate the generality and usefulness of our system with two use cases and a qualitative expert review.
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    Investigating the Manual View Specification and Visualization by Demonstration Paradigms for Visualization Construction
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Saket, Bahador; Endert, Alex; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    Abstract Interactivity plays an important role in data visualization. Therefore, understanding how people create visualizations given different interaction paradigms provides empirical evidence to inform interaction design. We present a two-phase study comparing people's visualization construction processes using two visualization tools: one implementing the manual view specification paradigm (Polestar) and another implementing visualization by demonstration (VisExemplar). Findings of our study indicate that the choice of interaction paradigm influences the visualization construction in terms of: 1) the overall effectiveness, 2) how participants phrase their goals, and 3) their perceived control and engagement. Based on our findings, we discuss trade-offs and open challenges with these interaction paradigms.
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    Linking and Layout: Exploring the Integration of Text and Visualization in Storytelling
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Zhi, Qiyu; Ottley, Alvitta; Metoyer, Ronald; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    Modern web technologies are enabling authors to create various forms of text visualization integration for storytelling. This integration may shape the stories' flow and thereby affect the reading experience. In this paper, we seek to understand two text visualization integration forms: (i) different text and visualization spatial arrangements (layout), namely, vertical and slideshow; and (ii) interactive linking of text and visualization (linking). Here, linking refers to a bidirectional interaction mode that explicitly highlights the explanatory visualization element when selecting narrative text and vice versa. Through a crowdsourced study with 180 participants, we measured the effect of layout and linking on the degree to which users engage with the story (user engagement), their understanding of the story content (comprehension), and their ability to recall the story information (recall). We found that participants performed significantly better in comprehension tasks with the slideshow layout. Participant recall was better with the slideshow layout under conditions with linking versus no linking. We also found that linking significantly increased user engagement. Additionally, linking and the slideshow layout were preferred by the participants. We also explored user reading behaviors with different conditions.
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    Capture & Analysis of Active Reading Behaviors for Interactive Articles on the Web
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Conlen, Matthew; Kale, Alex; Heer, Jeffrey; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    Journalists, educators, and technical writers are increasingly publishing interactive content on the web. However, popular analytics tools provide only coarse information about how readers interact with individual pages, and laboratory studies often fail to capture the variability of a real-world audience. We contribute extensions to the Idyll markup language to automate the detailed instrumentation of interactive articles and corresponding visual analysis tools for inspecting reader behavior at both micro- and macro-levels. We present three case studies of interactive articles that were instrumented, posted online, and promoted via social media to reach broad audiences, and share data from over 50,000 reader sessions. We demonstrate the use of our tools to characterize article-specific interaction patterns, compare behavior across desktop and mobile devices, and reveal reading patterns common across articles. Our contributed findings, tools, and corpus of behavioral data can help advance and inform more comprehensive studies of narrative visualization.
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    netflower: Dynamic Network Visualization for Data Journalists
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Stoiber, Christina; Rind, Alexander; Grassinger, Florian; Gutounig, Robert; Goldgruber, Eva; Sedlmair, Michael; Emrich, Štefan; Aigner, Wolfgang; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    Journalists need visual interfaces that cater to the exploratory nature of their investigative activities. In this paper, we report on a four-year design study with data journalists. The main result is netflower, a visual exploration tool that supports journalists in investigating quantitative flows in dynamic network data for story-finding. The visual metaphor is based on Sankey diagrams and has been extended to make it capable of processing large amounts of input data as well as network change over time. We followed a structured, iterative design process including requirement analysis and multiple design and prototyping iterations in close cooperation with journalists. To validate our concept and prototype, a workshop series and two diary studies were conducted with journalists. Our findings indicate that the prototype can be picked up quickly by journalists and valuable insights can be achieved in a few hours. The prototype can be accessed at: http://netflower.fhstp.ac.at/
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    Efficient Optimal Overlap Removal: Algorithms and Experiments
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Meulemans, Wouter; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    Motivated by visualizing spatial data using proportional symbols, we study the following problem: given a set of overlapping squares of varying sizes, minimally displace the squares as to remove the overlap while maintaining the orthogonal order on their centers. Though this problem is NP-hard, we show that rotating the squares by 45 degrees into diamonds allows for a linear or convex quadratic program. It is thus efficiently solvable even for relatively large instances. This positive result and the flexibility offered by constraint programming allow us to study various trade-offs for overlap removal. Specifically, we model and evaluate through computational experiments the relations between displacement, scale and order constraints for static data, and between displacement and temporal coherence for time-varying data. Finally, we also explore the generalization of our methodology to other shapes.
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    A Stable Graph Layout Algorithm for Processes
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Mennens, Robin; Scheepens, Roeland; Westenberg, Michel; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    Process mining enables organizations to analyze data about their (business) processes. Visualization is key to gaining insight into these processes and the associated data. Process visualization requires a high-quality graph layout that intuitively represents the semantics of the process. Process analysis additionally requires interactive filtering to explore the process data and process graph. The ideal process visualization therefore provides a high-quality, intuitive layout and preserves the mental map of the user during the visual exploration. The current industry standard used for process visualization does not satisfy either of these requirements. In this paper, we propose a novel layout algorithm for processes based on the Sugiyama framework. Our approach consists of novel ranking and order constraint algorithms and a novel crossing minimization algorithm. These algorithms make use of the process data to compute stable, high-quality layouts. In addition, we use phased animation to further improve mental map preservation. Quantitative and qualitative evaluations show that our approach computes layouts of higher quality and preserves the mental map better than the industry standard. Additionally, our approach is substantially faster, especially for graphs with more than 250 edges.
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    A Random Sampling O(n) Force-calculation Algorithm for Graph Layouts
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Gove, Robert; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    This paper proposes a linear-time repulsive-force-calculation algorithm with sub-linear auxiliary space requirements, achieving an asymptotic improvement over the Barnes-Hut and Fast Multipole Method force-calculation algorithms. The algorithm, named random vertex sampling (RVS), achieves its speed by updating a random sample of vertices at each iteration, each with a random sample of repulsive forces. This paper also proposes a combination algorithm that uses RVS to derive an initial layout and then applies Barnes-Hut to refine the layout. An evaluation of RVS and the combination algorithm compares their speed and quality on 109 graphs against a Barnes-Hut layout algorithm. The RVS algorithm performs up to 6.1 times faster on the tested graphs while maintaining comparable layout quality. The combination algorithm also performs faster than Barnes-Hut, but produces layouts that are more symmetric than using RVS alone. Data and code: https://osf.io/nb7m8/