EuroVis16: Eurographics Conference on Visualization

Permanent URI for this collection

https://diglib7.eg.org/handle/10.2312/15437

EuroVis 2016: Frontmatter

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Kwan-Liu Ma

;

Giuseppe Santucci

;

Jarke van Wijk

High-Dimensional Data

The Grassmannian Atlas: A General Framework for Exploring Linear Projections of High-Dimensional Data

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Liu, Shusen

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Bremer, Peer-Timo

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Jayaraman, Jayaraman Thiagarajan

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Wang, Bei

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Summa, Brian

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Pascucci, Valerio

High-Dimensional Data

Enhancing Scatterplots with Multi-Dimensional Focal Blur

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Staib, Joachim

;

Grottel, Sebastian

;

Gumhold, Stefan

High-Dimensional Data

Hierarchical Stochastic Neighbor Embedding

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Pezzotti, Nicola

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Höllt, Thomas

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Lelieveldt, Boudewijn P. F.

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Eisemann, Elmar

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Vilanova, Anna

High-Dimensional Data

Exploring Items and Features with IF,FI-Tables

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Corput, Paul van der

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Wijk, Jarke J. van

Networks and Graphs 1

Comparing Node-Link and Node-Link-Group Visualizations From An Enjoyment Perspective

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Saket, Bahador

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Scheidegger, Carlos

;

Kobourov, Stephen

Networks and Graphs 1

Interactive 3D Force-Directed Edge Bundling

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Zielasko, Daniel

;

Weyers, Benjamin

;

Hentschel, Bernd

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Kuhlen, Torsten W.

Networks and Graphs 1

TimeArcs: Visualizing Fluctuations in Dynamic Networks

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Dang, Tuan Nhon

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Pendar, Nick

;

Forbes, Angus G.

Networks and Graphs 1

Pathfinder: Visual Analysis of Paths in Graphs

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Partl, Christian

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Gratzl, Samuel

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Streit, Marc

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Wassermann, Anne-Mai

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Pfister, Hanspeter

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Schmalstieg, Dieter

;

Lex, Alexander

Structures, Clusters, and Patterns

Exploring and Comparing Clusterings of Multivariate Data Sets Using Persistent Homology

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Rieck, Bastian

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Leitte, Heike

Charts and Glyphs

Comparing Bar Chart Authoring with Microsoft Excel and Tangible Tiles

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Wun, Tiffany

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Payne, Jennifer

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Huron, Samuel

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Carpendale, Sheelagh

Structures, Clusters, and Patterns

Visualizing the Impact of Geographical Variations on Multivariate Clustering

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Zhang, Yifan

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Luo, Wei

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Mack, Elizabeth A.

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Maciejewski, Ross

Structures, Clusters, and Patterns

Space-Time Bifurcation Lines for Extraction of 2D Lagrangian Coherent Structures

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Machado, Gustavo Mello

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Boblest, Sebastian

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Ertl, Thomas

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Sadlo, Filip

Charts and Glyphs

Arcs, Angles, or Areas: Individual Data Encodings in Pie and Donut Charts

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Skau, Drew

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Kosara, Robert

Biological Data Visualization

Visualizing Co-occurrence of Events in Populations of Viral Genome Sequences

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Sarikaya, Alper

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Correll, Michael

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Dinis, Jorge M.

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O'Connor, David H.

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Gleicher, Michael

Charts and Glyphs

Glyphs for Asymmetric Second-Order 2D Tensors

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Seltzer, Nicholas

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Kindlmann, Gordon

Charts and Glyphs

How Ordered Is It? On the Perceptual Orderability of Visual Channels

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Chung, David H. S.

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Archambault, Daniel

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Borgo, Rita

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Edwards, Darren J.

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Laramee, Robert S.

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Chen, Min

Biological Data Visualization

Visibility Equalizer Cutaway Visualization of Mesoscopic Biological Models

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Muzic, Mathieu Le

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Mindek, Peter

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Sorger, Johannes

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Autin, Ludovic

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Goodsell, David S.

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Viola, Ivan

Biological Data Visualization

Evaluating Viewpoint Entropy for Ribbon Representation of Protein Structure

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Heinrich, Julian

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Vuong, Jenny

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Hammang, Christopher J.

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Wu, Andrew

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Rittenbruch, Markus

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Hogan, Jim

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Brereton, Margot

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O'Donoghue, Sean I.

Biological Data Visualization

Cytosplore: Interactive Immune Cell Phenotyping for Large Single-Cell Datasets

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Höllt, Thomas

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Pezzotti, Nicola

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Unen, Vincent van

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Koning, Frits

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Eisemann, Elmar

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Lelieveldt, Boudewijn P. F.

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Vilanova, Anna

Volume Data Applications

GEMSe: Visualization-Guided Exploration of Multi-channel Segmentation Algorithms

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Fröhler, Bernhard

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Möller, Torsten

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Heinzl, Christoph

Volume Data Applications

Visual Analysis of Defects in Glass Fiber Reinforced Polymers for 4DCT Interrupted In situ Tests

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Amirkhanov, Alexander

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Amirkhanov, Artem

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Salaberger, Dietmar

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Kastner, Johann

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Gröller, Eduard

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Heinzl, Christoph

Volume Data Applications

Parallel Marching Blocks: A Practical Isosurfacing Algorithm for Large Data on Many-Core Architectures

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Liu, Baoquan

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Clapworthy, Gordon J.

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Dong, Feng

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Wu, Enhua

Prediction and Forecasting

Visual Analysis of Spatial Variability and Global Correlations in Ensembles of Iso-Contours

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Ferstl, Florian

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Kanzler, Mathias

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Rautenhaus, Marc

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Westermann, Rüdiger

Prediction and Forecasting

Visual Analysis of Tumor Control Models for Prediction of Radiotherapy Response

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Raidou, Renata Georgia

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Casares-Magaz, Oscar

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Muren, Ludvig Paul

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Heide, Uulke A. van der

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Rørvik, Jarle

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Breeuwer, Marcel

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Vilanova, Anna

Coordinated Views and Interaction Design

Faceted Views of Varying Emphasis (FaVVEs): a Framework for Visualising Multi-perspective Small Multiples

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Beecham, Roger

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Rooney, Chris

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Meier, Sebastian

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Dykes, Jason

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Slingsby, Aidan

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Turkay, Cagatay

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Wood, Jo

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Wong, B. L. William

Coordinated Views and Interaction Design

Towards Quantitative Visual Analytics with Structured Brushing and Linked Statistics

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Radoš, Sanjin

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Splechtna, Rainer

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Matkovic, Kresimir

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Duras, Mario

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Gröller, Eduard

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Hauser, Helwig

Coordinated Views and Interaction Design

Designing Multiple Coordinated Visualizations for Tablets

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Sadana, Ramik

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Stasko, John

Coordinated Views and Interaction Design

Visual Debugging Techniques for Reactive Data Visualization

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Hoffswell, Jane

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Satyanarayan, Arvind

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Heer, Jeffrey

Networks and Graphs 2

Using Visualization to Explore Original and Anonymized LBSN Data

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Tarameshloo, Ebrahim

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Loorak, Mona Hosseinkhani

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Fong, Philip W. L.

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Carpendale, Sheelagh

Networks and Graphs 2

BubbleNet: A Cyber Security Dashboard for Visualizing Patterns

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McKenna, Sean

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Staheli, Diane

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Fulcher, Cody

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Meyer, Miriah

Networks and Graphs 2

Visual Analysis of Governing Topological Structures in Excitable Network Dynamics

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Ngo, Quynh Quang

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Hütt, Marc-Thorsten

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Linsen, Lars

Time Series Data and Sequences

Dynamic Change Arcs to Explore Model Forecasts

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St. Jean, Carmen

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Ware, Colin

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Gamble, Robert

Time Series Data and Sequences

Time-Series Plots Integrated in Parallel-Coordinates Displays

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Gruendl, Henning

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Riehmann, Patrick

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Pausch, Yves

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Froehlich, Bernd

Time Series Data and Sequences

There is More to Streamgraphs than Movies: Better Aesthetics via Ordering and Lassoing

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Bartolomeo, Marco Di

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Hu, Yifan

Time Series Data and Sequences

PhysioEx: Visual Analysis of Physiological Event Streams

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Kamaleswaran, Rishikesan

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Collins, Christopher

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James, Andrew

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McGregor, Carolyn

Flow Visualization

Semi-automatic Vortex Flow Classification in 4D PC-MRI Data of the Aorta

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Meuschke, Monique

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Köhler, Benjamin

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Preim, Uta

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Preim, Bernhard

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Lawonn, Kai

Flow Visualization

Critical Points of Gaussian-Distributed Scalar Fields on Simplicial Grids

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Liebmann, Tom

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Scheuermann, Gerik

Flow Visualization

Source Inversion by Forward Integration in Inertial Flows

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Günther, Tobias

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Theisel, Holger

Flow Visualization

MCFTLE: Monte Carlo Rendering of Finite-Time Lyapunov Exponent Fields

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Günther, Tobias

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Kuhn, Alexander

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Theisel, Holger

Volume Data Visualization

Similarity Voting based Viewpoint Selection for Volumes

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Tao, Yubo

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Wang, Qirui

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Chen, Wei

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Wu, Yingcai

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Lin, Hai

Volume Data Visualization

Decoupled Shading for Real-time Heterogeneous Volume Illumination

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Zhang, Yubo

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Ma, Kwan-Liu

Volume Data Visualization

Retailoring Box Splines to Lattices for Highly Isotropic Volume Representations

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Csébfalvi, Balázs

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Rácz, Gergely

Text and Document Data

TextDNA: Visualizing Word Usage with Configurable Colorfields

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Szafir, Danielle Albers

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Stuffer, Deidre

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Sohail, Yusef

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Gleicher, Michael

Text and Document Data

ConToVi: Multi-Party Conversation Exploration using Topic-Space Views

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El-Assady, Mennatallah

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Gold, Valentin

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Acevedo, Carmela

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Collins, Christopher

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Keim, Daniel

Geospatial Data Visualization

Location-dependent Generalization of Road Networks Based on Equivalent Destinations

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Dijk, Thomas C. van

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Haunert, Jan-Henrik

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Oehrlein, Johannes

Text and Document Data

A Visual Analytics Framework for Microblog Data Analysis at Multiple Scales of Aggregation

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Zhang, Jiawei

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Ahlbrand, Benjamin

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Malik, Abish

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Chae, Junghoon

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Min, Zhiyu

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Ko, Sungahn

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Ebert, David S.

Geospatial Data Visualization

Composite Flow Maps

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Cornel, Daniel

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Konev, Artem

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Sadransky, Bernhard

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Horváth, Zsolt

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Brambilla, Andrea

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Viola, Ivan

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Waser, Jürgen

Geospatial Data Visualization

Exploratory Visual Analysis for Animal Movement Ecology

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Slingsby, Aidan

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Loon, Emiel van

Story, History, and Evolution

AVOCADO: Visualization of Workflow-Derived Data Provenance for Reproducible Biomedical Research

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Stitz, Holger

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Luger, Stefan

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Streit, Marc

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Gehlenborg, Nils

Story, History, and Evolution

From Visual Exploration to Storytelling and Back Again

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Gratzl, Samuel

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Lex, Alexander

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Gehlenborg, Nils

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Cosgrove, Nicola

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Streit, Marc

Browse

Now showing 1 - 51 of 51

EuroVis 2016: Frontmatter
(Eurographics Association, 2016) Kwan-Liu Ma; Giuseppe Santucci; Jarke van Wijk;
The Grassmannian Atlas: A General Framework for Exploring Linear Projections of High-Dimensional Data
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Liu, Shusen; Bremer, Peer-Timo; Jayaraman, Jayaraman Thiagarajan; Wang, Bei; Summa, Brian; Pascucci, Valerio; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
Linear projections are one of the most common approaches to visualize high-dimensional data. Since the space of possible projections is large, existing systems usually select a small set of interesting projections by ranking a large set of candidate projections based on a chosen quality measure. However, while highly ranked projections can be informative, some lower ranked ones could offer important complementary information. Therefore, selection based on ranking may miss projections that are important to provide a global picture of the data. The proposed work fills this gap by presenting the Grassmannian Atlas, a framework that captures the global structures of quality measures in the space of all projections, which enables a systematic exploration of many complementary projections and provides new insights into the properties of existing quality measures.
Enhancing Scatterplots with Multi-Dimensional Focal Blur
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Staib, Joachim; Grottel, Sebastian; Gumhold, Stefan; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
Scatterplots directly depict two dimensions of multi-dimensional data points, discarding all other information. To visualize all data, these plots are extended to scatterplot matrices, which distribute the information of each data point over many plots. Problems arising from the resulting visual complexity are nowadays alleviated by concepts like filtering and focus and context. We present a method based on depth of field that contains both aspects and injects information from all dimensions into each scatterplot. Our approach is a natural generalization of the commonly known focus effects from optics. It is based on a multidimensional focus selection body. Points outside of this body are defocused depending on their distance. Our method allows for a continuous transition from data points in focus, over regions of blurry points providing contextual information, to visually filtered data. Our algorithm supports different focus selection bodies, blur kernels, and point shapes. We present an optimized GPU-based implementation for interactive exploration and show the usefulness of our approach on several data sets.
Hierarchical Stochastic Neighbor Embedding
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Pezzotti, Nicola; Höllt, Thomas; Lelieveldt, Boudewijn P. F.; Eisemann, Elmar; Vilanova, Anna; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
In recent years, dimensionality-reduction techniques have been developed and are widely used for hypothesis generation in Exploratory Data Analysis. However, these techniques are confronted with overcoming the trade-off between computation time and the quality of the provided dimensionality reduction. In this work, we address this limitation, by introducing Hierarchical Stochastic Neighbor Embedding (Hierarchical-SNE). Using a hierarchical representation of the data, we incorporate the wellknown mantra of Overview-First, Details-On-Demand in non-linear dimensionality reduction. First, the analysis shows an embedding, that reveals only the dominant structures in the data (Overview). Then, by selecting structures that are visible in the overview, the user can filter the data and drill down in the hierarchy. While the user descends into the hierarchy, detailed visualizations of the high-dimensional structures will lead to new insights. In this paper, we explain how Hierarchical-SNE scales to the analysis of big datasets. In addition, we show its application potential in the visualization of Deep-Learning architectures and the analysis of hyperspectral images.
Exploring Items and Features with IF,FI-Tables
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Corput, Paul van der; Wijk, Jarke J. van; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
The exploration of high-dimensional data is challenging because humans have difficulty to understand more than three dimensions. We present a new visualization concept that enables users to explore such data and, specifically, to learn about important items and features that are unknown or overlooked, based on the items and features that are already known. The visualization consists of two juxtaposed tables: an IF-Table, showing all items with a selection of features; and an FI-Table, showing all features with a selection of items. This enables the user to limit the number of visible items and features to those needed for the exploration. The interaction is kept simple: each selection of items and features results in a complete overview of similar and relevant items and features.
Comparing Node-Link and Node-Link-Group Visualizations From An Enjoyment Perspective
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Saket, Bahador; Scheidegger, Carlos; Kobourov, Stephen; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
While evaluation studies in visualization often involve traditional performance measurements, there has been a concerted effort to move beyond time and accuracy. Of these alternative aspects, memorability and recall of visualizations have been recently considered, but other aspects such as enjoyment and engagement are not as well explored. We study the enjoyment of two different visualization methods through a user study. In particular, we describe the results of a three-phase experiment comparing the enjoyment of two different visualizations of the same relational data: node-link and node-link-group visualizations. The results indicate that the participants in this study found node-link-group visualizations more enjoyable than node-link visualizations.
Interactive 3D Force-Directed Edge Bundling
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Zielasko, Daniel; Weyers, Benjamin; Hentschel, Bernd; Kuhlen, Torsten W.; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
Interactive analysis of 3D relational data is challenging. A common way of representing such data are node-link diagrams as they support analysts in achieving a mental model of the data. However, naïve 3D depictions of complex graphs tend to be visually cluttered, even more than in a 2D layout. This makes graph exploration and data analysis less efficient. This problem can be addressed by edge bundling. We introduce a 3D cluster-based edge bundling algorithm that is inspired by the force-directed edge bundling (FDEB) algorithm [HvW09b] and fulfills the requirements to be embedded in an interactive framework for spatial data analysis. It is parallelized and scales with the size of the graph regarding the runtime. Furthermore, it maintains the edge's model and thus supports rendering the graph in different structural styles. We demonstrate this with a graph originating from a simulation of the function of a macaque brain.
TimeArcs: Visualizing Fluctuations in Dynamic Networks
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Dang, Tuan Nhon; Pendar, Nick; Forbes, Angus G.; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
In this paper we introduce TimeArcs, a novel visualization technique for representing dynamic relationships between entities in a network. Force-directed layouts provide a way to highlight related entities by positioning them near to each other. Entities are brought closer to each other (forming clusters) by forces applied on nodes and connections between nodes. In many application domains, relationships between entities are not temporally stable, which means that cluster structures and cluster memberships also may vary across time. Our approach merges multiple force-directed layouts at different time points into a single comprehensive visualization that provides a big picture overview of the most significant clusters within a user-defined period of time. TimeArcs also supports a range of interactive features, such as allowing users to drill-down in order to see details about a particular cluster. To highlight the benefits of this technique, we demonstrate its application to various datasets, including the IMDB co-star network, a dataset showing conflicting evidences within biomedical literature of protein interactions, and collocated popular phrases obtained from political blogs.
Pathfinder: Visual Analysis of Paths in Graphs
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Partl, Christian; Gratzl, Samuel; Streit, Marc; Wassermann, Anne-Mai; Pfister, Hanspeter; Schmalstieg, Dieter; Lex, Alexander; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
The analysis of paths in graphs is highly relevant in many domains. Typically, path-related tasks are performed in node-link layouts. Unfortunately, graph layouts often do not scale to the size of many real world networks. Also, many networks are multivariate, i.e., contain rich attribute sets associated with the nodes and edges. These attributes are often critical in judging paths, but directly visualizing attributes in a graph layout exacerbates the scalability problem. In this paper, we present visual analysis solutions dedicated to path-related tasks in large and highly multivariate graphs. We show that by focusing on paths, we can address the scalability problem of multivariate graph visualization, equipping analysts with a powerful tool to explore large graphs. We introduce Pathfinder, a technique that provides visual methods to query paths, while considering various constraints. The resulting set of paths is visualized in both a ranked list and as a node-link diagram. For the paths in the list, we display rich attribute data associated with nodes and edges, and the node-link diagram provides topological context. The paths can be ranked based on topological properties, such as path length or average node degree, and scores derived from attribute data. Pathfinder is designed to scale to graphs with tens of thousands of nodes and edges by employing strategies such as incremental query results. We demonstrate Pathfinder's fitness for use in scenarios with data from a coauthor network and biological pathways.
Exploring and Comparing Clusterings of Multivariate Data Sets Using Persistent Homology
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Rieck, Bastian; Leitte, Heike; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
Clustering algorithms support exploratory data analysis by grouping inputs that share similar features. Especially the clustering of unlabelled data is said to be a fiendishly difficult problem, because users not only have to choose a suitable clustering algorithm but also a suitable number of clusters. The known issues of existing clustering validity measures comprise instabilities in the presence of noise and restrictive assumptions about cluster shapes. In addition, they cannot evaluate individual clusters locally. We present a new measure for assessing and comparing different clusterings both on a global and on a local level. Our measure is based on the topological method of persistent homology, which is stable and unbiased towards cluster shapes. Based on our measure, we also describe a new visualization that displays similarities between different clusterings (using a global graph view) and supports their comparison on the individual cluster level (using a local glyph view). We demonstrate how our visualization helps detect different—-but equally valid-clusterings of data sets from multiple application domains.
Comparing Bar Chart Authoring with Microsoft Excel and Tangible Tiles
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Wun, Tiffany; Payne, Jennifer; Huron, Samuel; Carpendale, Sheelagh; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
Providing tools that make visualization authoring accessible to visualization non-experts is a major research challenge. Currently the most common approach to generating a visualization is to use software that quickly and automatically produces visualizations based on templates. However, it has recently been suggested that constructing a visualization with tangible tiles may be a more accessible method, especially for people without visualization expertise. There is still much to be learned about the differences between these two visualization authoring practices. To better understand how people author visualizations in these two conditions, we ran a qualitative study comparing the use of software to the use of tangible tiles, for the creation of bar charts. Close observation of authoring activities showed how each of the following varied according to the tool used: 1) sequences of action; 2) distribution of time spent on different aspects of the InfoVis pipeline; 3) pipeline task separation; and 4) freedom to manipulate visual variables. From these observations, we discuss the implications of the variations in activity sequences, noting tool design considerations and pointing to future research questions.
Visualizing the Impact of Geographical Variations on Multivariate Clustering
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Zhang, Yifan; Luo, Wei; Mack, Elizabeth A.; Maciejewski, Ross; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
Traditional multivariate clustering approaches are common in many geovisualization applications. These algorithms are used to define geodemographic profiles, ecosystems and various other land use patterns that are based on multivariate measures. Cluster labels are then projected onto a choropleth map to enable analysts to explore spatial dependencies and heterogeneity within the multivariate attributes. However, local variations in the data and choices of clustering parameters can greatly impact the resultant visualization. In this work, we develop a visual analytics framework for exploring and comparing the impact of geographical variations for multivariate clustering. Our framework employs a variety of graphical configurations and summary statistics to explore the spatial extents of clustering. It also allows users to discover patterns that can be concealed by traditional global clustering via several interactive visualization techniques including a novel drag & drop clustering difference view. We demonstrate the applicability of our framework over a demographics dataset containing quick facts about counties in the continental United States and demonstrate the need for analytical tools that can enable users to explore and compare clustering results over varying geographical features and scales.
Space-Time Bifurcation Lines for Extraction of 2D Lagrangian Coherent Structures
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Machado, Gustavo Mello; Boblest, Sebastian; Ertl, Thomas; Sadlo, Filip; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
We present a novel and efficient technique to extract Lagrangian coherent structures in two-dimensional time-dependent vector fields. We show that this can be achieved by employing bifurcation line extraction in the space-time representation of the vector field, and generating space-time bifurcation manifolds therefrom. To show the utility and applicability of our approach, we provide an evaluation of existing extraction techniques for Lagrangian coherent structures, and compare them to our approach.
Arcs, Angles, or Areas: Individual Data Encodings in Pie and Donut Charts
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Skau, Drew; Kosara, Robert; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
Pie and donut charts have been a hotly debated topic in the visualization community for some time now. Even though pie charts have been around for over 200 years, our understanding of the perceptual factors used to read data in them is still limited. Data is encoded in pie and donut charts in three ways: arc length, center angle, and segment area. For our first study, we designed variations of pie charts to test the importance of individual encodings for reading accuracy. In our second study, we varied the inner radius of a donut chart from a filled pie to a thin outline to test the impact of removing the central angle. Both studies point to angle being the least important visual cue for both charts, and the donut chart being as accurate as the traditional pie chart.
Visualizing Co-occurrence of Events in Populations of Viral Genome Sequences
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Sarikaya, Alper; Correll, Michael; Dinis, Jorge M.; O'Connor, David H.; Gleicher, Michael; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
Virologists are not only interested in point mutations in a genome, but also in relationships between mutations. In this work, we present a design study to support the discovery of correlated mutation events (called co-occurrences) in populations of viral genomes. The key challenge is to identify potentially interesting pairs of events within the vast space of event combinations. In our work, we identify analyst requirements and develop a prototype through a participatory process. The key ideas of our approach are to use interest metrics to create dynamic filtering that guides the viewer to interesting and relevant correlations of genome mutations, and to provide visual encodings designed to fit scientists' mental map of the data, along with dynamic filtering techniques. We demonstrate the strength of our approach in virology-situated case studies, and offer suggestions for extending our strategy to other sequence-based domains.
Glyphs for Asymmetric Second-Order 2D Tensors
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Seltzer, Nicholas; Kindlmann, Gordon; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
Tensors model a wide range of physical phenomena. While symmetric tensors are sufficient for some applications (such as diffusion), asymmetric tensors are required, for example, to describe differential properties of fluid flow. Glyphs permit inspecting individual tensor values, but existing tensor glyphs are fully defined only for symmetric tensors. We propose a glyph to visualize asymmetric second-order two-dimensional tensors. The glyph includes visual encoding for physically significant attributes of the tensor, including rotation, anisotropic stretching, and isotropic dilation. Our glyph design conserves the symmetry and continuity properties of the underlying tensor, in that transformations of a tensor (such as rotation or negation) correspond to analogous transformations of the glyph. We show results with synthetic data from computational fluid dynamics.
How Ordered Is It? On the Perceptual Orderability of Visual Channels
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Chung, David H. S.; Archambault, Daniel; Borgo, Rita; Edwards, Darren J.; Laramee, Robert S.; Chen, Min; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
The design of effective glyphs for visualisation involves a number of different visual encodings. Since spatial position is usually already specified in advance, we must rely on other visual channels to convey additional relationships for multivariate analysis. One such relationship is the apparent order present in the data. This paper presents two crowdsourcing empirical studies that focus on the perceptual evaluation of orderability for visual channels, namely Bertin's retinal variables. The first study investigates the perception of order in a sequence of elements encoded with different visual channels. We found evidence that certain visual channels are perceived as more ordered (for example, value) while others are perceived as less ordered (for example, hue) than the measured order present in the data. As a result, certain visual channels are more/less sensitive to disorder. The second study evaluates how visual orderability affects min and max judgements of elements in the sequence. We found that visual channels that tend to be perceived as ordered, improve the accuracy of identifying these values.
Visibility Equalizer Cutaway Visualization of Mesoscopic Biological Models
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Muzic, Mathieu Le; Mindek, Peter; Sorger, Johannes; Autin, Ludovic; Goodsell, David S.; Viola, Ivan; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
In scientific illustrations and visualization, cutaway views are often employed as an effective technique for occlusion management in densely packed scenes.We propose a novel method for authoring cutaway illustrations of mesoscopic biological models. In contrast to the existing cutaway algorithms, we take advantage of the specific nature of the biological models. These models consist of thousands of instances with a comparably smaller number of different types. Our method constitutes a two stage process. In the first step, clipping objects are placed in the scene, creating a cutaway visualization of the model. During this process, a hierarchical list of stacked bars inform the user about the instance visibility distribution of each individual molecular type in the scene. In the second step, the visibility of each molecular type is fine-tuned through these bars, which at this point act as interactive visibility equalizers. An evaluation of our technique with domain experts confirmed that our equalizer-based approach for visibility specification is valuable and effective for both, scientific and educational purposes.
Evaluating Viewpoint Entropy for Ribbon Representation of Protein Structure
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Heinrich, Julian; Vuong, Jenny; Hammang, Christopher J.; Wu, Andrew; Rittenbruch, Markus; Hogan, Jim; Brereton, Margot; O'Donoghue, Sean I.; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
While many measures of viewpoint goodness have been proposed in computer graphics, none have been evaluated for ribbon representations of protein secondary structure. To fill this gap, we conducted a user study on Amazon's Mechanical Turk platform, collecting human viewpoint preferences from 65 participants for 4 representative superfamilies of protein domains. In particular, we evaluated viewpoint entropy, which was previously shown to be a good predictor for human viewpoint preference of other, mostly non-abstract objects. In a second study, we asked 7 experts in molecular biology to find the best viewpoint of the same protein domains and compared their choices with viewpoint entropy. Our results indicate that viewpoint entropy overall is a significant predictor of human viewpoint preference for ribbon representations of protein secondary structure. However, the accuracy depends on the type and composition of the structure: while most participants agree on good viewpoints for structures with mainly beta sheets, viewpoint preference varies considerably for complex arrangements of alpha helices. Finally, experts tend to choose viewpoints of both low and high viewpoint entropy to emphasize different aspects of the respective structure.
Cytosplore: Interactive Immune Cell Phenotyping for Large Single-Cell Datasets
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Höllt, Thomas; Pezzotti, Nicola; Unen, Vincent van; Koning, Frits; Eisemann, Elmar; Lelieveldt, Boudewijn P. F.; Vilanova, Anna; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
To understand how the immune system works, one needs to have a clear picture of its cellular compositon and the cells' corresponding properties and functionality. Mass cytometry is a novel technique to determine the properties of single-cells with unprecedented detail. This amount of detail allows for much finer differentiation but also comes at the cost of more complex analysis. In this work, we present Cytosplore, implementing an interactive workflow to analyze mass cytometry data in an integrated system, providing multiple linked views, showing different levels of detail and enabling the rapid definition of known and unknown cell types. Cytosplore handles millions of cells, each represented as a high-dimensional data point, facilitates hypothesis generation and confirmation, and provides a significant speed up of the current workflow. We show the effectiveness of Cytosplore in a case study evaluation.
GEMSe: Visualization-Guided Exploration of Multi-channel Segmentation Algorithms
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Fröhler, Bernhard; Möller, Torsten; Heinzl, Christoph; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
We present GEMSe, an interactive tool for exploring and analyzing the parameter space of multi-channel segmentation algorithms. Our targeted user group are domain experts who are not necessarily segmentation specialists. GEMSe allows the exploration of the space of possible parameter combinations for a segmentation framework and its ensemble of results. Users start with sampling the parameter space and computing the corresponding segmentations. A hierarchically clustered image tree provides an overview of variations in the resulting space of label images. Details are provided through exemplary images from the selected cluster and histograms visualizing the parameters and the derived output in the selected cluster. The correlation between parameters and derived output as well as the effect of parameter changes can be explored through interactive filtering and scatter plots. We evaluate the usefulness of GEMSe through expert reviews and case studies based on three different kinds of datasets: A synthetic dataset emulating the combination of 3D X-ray computed tomography with data from K-Edge spectroscopy, a three-channel scan of a rock crystal acquired by a Talbot-Lau grating interferometer X-ray computed tomography device, as well as a hyperspectral image.
Visual Analysis of Defects in Glass Fiber Reinforced Polymers for 4DCT Interrupted In situ Tests
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Amirkhanov, Alexander; Amirkhanov, Artem; Salaberger, Dietmar; Kastner, Johann; Gröller, Eduard; Heinzl, Christoph; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
Material engineers use interrupted in situ tensile testing to investigate the damage mechanisms in composite materials. For each subsequent scan, the load is incrementally increased until the specimen is completely fractured. During the interrupted in situ testing of glass fiber reinforced polymers (GFRPs) defects of four types are expected to appear: matrix fracture, fiber/matrix debonding, fiber pull-out, and fiber fracture. There is a growing demand for the detection and analysis of these defects among the material engineers. In this paper, we present a novel workflow for the detection, classification, and visual analysis of defects in GFRPs using interrupted in situ tensile tests in combination with X-ray Computed Tomography. The workflow is based on the automatic extraction of defects and fibers. We introduce the automatic Defect Classifier assigning the most suitable type to each defect based on its geometrical features. We present a visual analysis system that integrates four visualization methods: 1) the Defect Viewer highlights defects with visually encoded type in the context of the original CT image, 2) the Defect Density Maps provide an overview of the defect distributions according to type in 2D and 3D, 3) the Final Fracture Surface estimates the material fracture's location and displays it as a 3D surface, 4) the 3D Magic Lens enables interactive exploration by combining detailed visualizations in the region of interest with overview visualizations as context. In collaboration with material engineers, we evaluate our solution and demonstrate its practical applicability.
Parallel Marching Blocks: A Practical Isosurfacing Algorithm for Large Data on Many-Core Architectures
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Liu, Baoquan; Clapworthy, Gordon J.; Dong, Feng; Wu, Enhua; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
Interactive isosurface visualisation has been made possible by mapping algorithms to GPU architectures. However, current state-of-the-art isosurfacing algorithms usually consume large amounts of GPU memory owing to the additional acceleration structures they require. As a result, the continued limitations on available GPU memory mean that they are unable to deal with the larger datasets that are now increasingly becoming prevalent. This paper proposes a new parallel isosurface-extraction algorithm that exploits the blocked organisation of the parallel threads found in modern many-core platforms to achieve fast isosurface extraction and reduce the associated memory requirements. This is achieved by optimising thread co-operation within thread-blocks and reducing redundant computation; ultimately, an indexed triangular mesh can be produced. Experiments have shown that the proposed algorithm is much faster (up to 10x ) than state-of-the-art GPU algorithms and has a much smaller memory footprint, enabling it to handle much larger datasets (up to 64x) on the same GPU.
Visual Analysis of Spatial Variability and Global Correlations in Ensembles of Iso-Contours
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Ferstl, Florian; Kanzler, Mathias; Rautenhaus, Marc; Westermann, Rüdiger; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
For an ensemble of iso-contours in multi-dimensional scalar fields, we present new methods to a) visualize their dominant spatial patterns of variability, and b) to compute the conditional probability of the occurrence of a contour at one location given the occurrence at some other location. We first show how to derive a statistical model describing the contour variability, by representing the contours implicitly via signed distance functions and clustering similar functions in a reduced order space. We show that the spatial patterns of the ensemble can then be derived by analytically transforming the boundaries of a confidence interval computed from each cluster into the spatial domain. Furthermore, we introduce a mathematical basis for computing correlations between the occurrences of iso-contours at different locations. We show that the computation of these correlations can be posed in the reduced order space as an integration problem over a region bounded by four hyper-planes. To visualize the derived statistical properties we employ a variant of variability plots for streamlines, now including the color coding of probabilities of joint contour occurrences. We demonstrate the use of the proposed techniques for ensemble exploration in a number of 2D and 3D examples, using artificial and meteorological data sets.
Visual Analysis of Tumor Control Models for Prediction of Radiotherapy Response
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Raidou, Renata Georgia; Casares-Magaz, Oscar; Muren, Ludvig Paul; Heide, Uulke A. van der; Rørvik, Jarle; Breeuwer, Marcel; Vilanova, Anna; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
In radiotherapy, tumors are irradiated with a high dose, while surrounding healthy tissues are spared. To quantify the probability that a tumor is effectively treated with a given dose, statistical models were built and employed in clinical research. These are called tumor control probability (TCP) models. Recently, TCP models started incorporating additional information from imaging modalities. In this way, patient-specific properties of tumor tissues are included, improving the radiobiological accuracy of models. Yet, the employed imaging modalities are subject to uncertainties with significant impact on the modeling outcome, while the models are sensitive to a number of parameter assumptions. Currently, uncertainty and parameter sensitivity are not incorporated in the analysis, due to time and resource constraints. To this end, we propose a visual tool that enables clinical researchers working on TCP modeling, to explore the information provided by their models, to discover new knowledge and to confirm or generate hypotheses within their data. Our approach incorporates the following four main components: (1) It supports the exploration of uncertainty and its effect on TCP models; (2) It facilitates parameter sensitivity analysis to common assumptions; (3) It enables the identification of inter-patient response variability; (4) It allows starting the analysis from the desired treatment outcome, to identify treatment strategies that achieve it. We conducted an evaluation with nine clinical researchers. All participants agreed that the proposed visual tool provides better understanding and new opportunities for the exploration and analysis of TCP modeling.
Faceted Views of Varying Emphasis (FaVVEs): a Framework for Visualising Multi-perspective Small Multiples
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Beecham, Roger; Rooney, Chris; Meier, Sebastian; Dykes, Jason; Slingsby, Aidan; Turkay, Cagatay; Wood, Jo; Wong, B. L. William; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
Many datasets have multiple perspectives - for example space, time and description - and often analysts are required to study these multiple perspectives concurrently. This concurrent analysis becomes difficult when data are grouped and split into small multiples for comparison. A design challenge is thus to provide representations that enable multiple perspectives, split into small multiples, to be viewed simultaneously in ways that neither clutter nor overload. We present a design framework that allows us to do this. We claim that multi-perspective comparison across small multiples may be possible by superimposing perspectives on one another rather than juxtaposing those perspectives side-by-side. This approach defies conventional wisdom and likely results in visual and informational clutter. For this reason we propose designs at three levels of abstraction for each perspective. By flexibly varying the abstraction level, certain perspectives can be brought into, or out of, focus. We evaluate our framework through laboratory-style user tests. We find that superimposing, rather than juxtaposing, perspective views has little effect on performance of a low-level comparison task. We reflect on the user study and its design to further identify analysis situations for which our framework may be desirable. Although the user study findings were insufficiently discriminating, we believe our framework opens up a new design space for multi-perspective visual analysis.
Towards Quantitative Visual Analytics with Structured Brushing and Linked Statistics
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Radoš, Sanjin; Splechtna, Rainer; Matkovic, Kresimir; Duras, Mario; Gröller, Eduard; Hauser, Helwig; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
Until now a lot of visual analytics predominantly delivers qualitative results-based, for example, on a continuous color map or a detailed spatial encoding. Important target applications, however, such as medical diagnosis and decision making, clearly benefit from quantitative analysis results. In this paper we propose several specific extensions to the well-established concept of linking&brushing in order to make the analysis results more quantitative. We structure the brushing space in order to improve the reproducibility of the brushing operation, e.g., by introducing the percentile grid. We also enhance the linked visualization with overlaid descriptive statistics to enable a more quantitative reading of the resulting focus+context visualization. Additionally, we introduce two novel brushing techniques: the percentile brush and the Mahalanobis brush. Both use the underlying data to support statistically meaningful interactions with the data. We illustrate the use of the new techniques in the context of two case studies, one based on meteorological data and the other one focused on data from the automotive industry where we evaluate a shaft design in the context of mechanical power transmission in cars.
Designing Multiple Coordinated Visualizations for Tablets
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Sadana, Ramik; Stasko, John; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
The use of multiple coordinated views (MCV) in data visualization provides analytic power because it allows a person to explore data under a variety of different perspectives. Since this design pattern utilizes multiple visualizations and requires coordinated interactions across the views, a clever use of screen space is vital and many synchronized interface operations must be provided. Bringing this design pattern to tablet computers is challenging due to their small display size and the absence of keyboard and mouse input. In this article, we explain important design considerations for MCV visualization on tablets and describe a prototype MCV visualization system we have built for the iPad. The design is based on the principles of maximizing screen space for data presentation, promoting consistent interactions across visualizations, and minimizing occlusion from a person's hands.
Visual Debugging Techniques for Reactive Data Visualization
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Hoffswell, Jane; Satyanarayan, Arvind; Heer, Jeffrey; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
Interaction is critical to effective visualization, but can be difficult to author and debug due to dependencies among input events, program state, and visual output. Recent advances leverage reactive semantics to support declarative design and avoid the ''spaghetti code'' of imperative event handlers. While reactive programming improves many aspects of development, textual specifications still fail to convey the complex runtime dynamics. In response, we contribute a set of visual debugging techniques to reveal the runtime behavior of reactive visualizations. A timeline view records input events and dynamic variable updates, allowing designers to replay and inspect the propagation of values step-by-step. On-demand annotations overlay the output visualization to expose relevant state and scale mappings in-situ. Dynamic tables visualize how backing datasets change over time. To evaluate the effectiveness of these techniques, we study how first-time Vega users debug interactions in faulty, unfamiliar specifications; with no prior knowledge, participants were able to accurately trace errors through the specification.
Using Visualization to Explore Original and Anonymized LBSN Data
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Tarameshloo, Ebrahim; Loorak, Mona Hosseinkhani; Fong, Philip W. L.; Carpendale, Sheelagh; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
We present GSUVis, a visualization tool designed to provide better understanding of location-based social network (LBSN) data. LBSN data is one of the most important sources of information for transportation, marketing, health, and public safety. LBSN data consumers are interested in accessing and analysing data that is as complete and as accurate as possible. However, LBSN data contains sensitive information about individuals. Consequently, data anonymization is of critical importance if this data is to be made available to consumers. However, anonymization commonly reduces the utility of information available. Working with privacy experts, we designed GSUVis a visual analytic tool to help experts better understand the effects of anonymization techniques on LBSN data utility. One of GSUVis's primary goals is to make it possible for people to use LBSN data, without requiring them to gain deep knowledge about data anonymization. To inform the design of GSUVis, we interviewed privacy experts, and collected their tasks and system requirements. Based on this understanding, we designed and implemented GSUVis. It applies two anonymization algorithms for social and location trajectory data to a real-world LBSN dataset and visualizes the data both before and after anonymization. Through feedback from domain experts, we reflect on the effectiveness of GSUVis and the impact of anonymization using visualization.
BubbleNet: A Cyber Security Dashboard for Visualizing Patterns
(The Eurographics Association and John Wiley & Sons Ltd., 2016) McKenna, Sean; Staheli, Diane; Fulcher, Cody; Meyer, Miriah; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
The field of cyber security is faced with ever-expanding amounts of data and a constant barrage of cyber attacks. Within this space, we have designed BubbleNet as a cyber security dashboard to help network analysts identify and summarize patterns within the data. This design study faced a range of interesting constraints from limited time with various expert users and working with users beyond the network analyst, such as network managers. To overcome these constraints, the design study employed a user-centered design process and a variety of methods to incorporate user feedback throughout the design of BubbleNet. This approach resulted in a successfully evaluated dashboard with users and further deployments of these ideas in both research and operational environments. By explaining these methods and the process, it can benefit future visualization designers to help overcome similar challenges in cyber security or alternative domains.
Visual Analysis of Governing Topological Structures in Excitable Network Dynamics
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Ngo, Quynh Quang; Hütt, Marc-Thorsten; Linsen, Lars; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
To understand how topology shapes the dynamics in excitable networks is one of the fundamental problems in network science when applied to computational systems biology and neuroscience. Recent advances in the field discovered the influential role of two macroscopic topological structures, namely hubs and modules. We propose a visual analytics approach that allows for a systematic exploration of the role of those macroscopic topological structures on the dynamics in excitable networks. Dynamical patterns are discovered using the dynamical features of excitation ratio and co-activation. Our approach is based on the interactive analysis of the correlation of topological and dynamical features using coordinated views. We designed suitable visual encodings for both the topological and the dynamical features. A degree map and an adjacency matrix visualization allow for the interaction with hubs and modules, respectively. A barycentric-coordinates layout and a multi-dimensional scaling approach allow for the analysis of excitation ratio and co-activation, respectively. We demonstrate how the interplay of the visual encodings allows us to quickly reconstruct recent findings in the field within an interactive analysis and even discovered new patterns. We apply our approach to network models of commonly investigated topologies as well as to the structural networks representing the connectomes of different species. We evaluate our approach with domain experts in terms of its intuitiveness, expressiveness, and usefulness.
Dynamic Change Arcs to Explore Model Forecasts
(The Eurographics Association and John Wiley & Sons Ltd., 2016) St. Jean, Carmen; Ware, Colin; Gamble, Robert; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
In many planning applications, a computational model is used to make predictions about the effects of management or engineering decisions. To understand the implications of alternative scenarios, a user typically adjusts one or more of the input parameters, runs the model, and examines the outcomes using simple charts. For example, a time series showing changes in productivity or revenue might be generated. While this approach can be effective in showing the projected effects of changes to the model's input parameters, it fails to show the mechanisms that cause those changes. In order to promote understanding of model mechanics using a simple graphical device, we propose dynamic change arcs. Dynamic change arcs graphically reveal the internal model structure as cause and effect linkages. They are signed to show both positive and negative effects. We implemented this concept using a species interaction model developed for fisheries management based on a system of Lotka-Volterra equations. The model has 10 economically important fish species and incorporates both predation and competition between species. The model predicts that changing the catch of one species can sometimes result in changes in biomass of another species through multi-step causal chains. The dynamic change arcs make it possible to interpret the resulting complex causal chains and interaction effects. We carried out an experiment to evaluate three alternative forms of arcs for portraying causal connections in the model. The results show that all linkage representations enabled participants to reason better about complex chains of causality than not showing linkages. However, none of them were significantly better than the others.
Time-Series Plots Integrated in Parallel-Coordinates Displays
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Gruendl, Henning; Riehmann, Patrick; Pausch, Yves; Froehlich, Bernd; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
We present a natural extension of two-dimensional parallel-coordinates plots for revealing relationships in time-dependent multi-attribute data by building on the idea that time can be considered as the third dimension. A time slice through the visualization represents a certain point in time and can be viewed as a regular parallel-coordinates display. A vertical slice through one of the axes of the parallel-coordinates display would show a time-series plot. For a focus-and-context integration of both views, we embed time-series plots between two adjacent axes of the parallel-coordinates plot. Both time-series plots are drawn using a pseudo three-dimensional perspective with a single vanishing point. An independent parallel-coordinates panel that connects the two perspectively displayed time-series plots can move forward and backward in time to reveal changes in the relationship between the time-dependent attributes. The visualization of time-series plots in the context of the parallelcoordinates plot facilitates the exploration of time-related aspects of the data without the need to switch to a separate display. We provide a consistent set of tools for selecting and contrasting subsets of the data, which are important for various application domains.
There is More to Streamgraphs than Movies: Better Aesthetics via Ordering and Lassoing
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Bartolomeo, Marco Di; Hu, Yifan; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
Streamgraphs were popularized in 2008 when The New York Times used them to visualize box office revenues for 7500 movies over 21 years. The aesthetics of a streamgraph is affected by three components: the ordering of the layers, the shape of the lowest curve of the drawing, known as the baseline, and the labels for the layers. As of today, the ordering and baseline computation algorithms proposed in the paper of Byron and Wattenberg are still considered the state of the art. However, their ordering algorithm exploits statistical properties of the movie revenue data that may not hold in other data. In addition, the baseline optimization is based on a definition of visual energy that in some cases results in considerable amount of visual distortion. We offer an ordering algorithm that works well regardless of the properties of the input data, and propose a 1-norm based definition of visual energy and the associated solution method that overcomes the limitation of the original baseline optimization procedure. Furthermore, we propose an efficient layer labeling algorithm that scales linearly to the data size in place of the brute-force algorithm adopted by Byron and Wattenberg. We demonstrate the advantage of our algorithms over existing techniques on a number of real world data sets.
PhysioEx: Visual Analysis of Physiological Event Streams
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Kamaleswaran, Rishikesan; Collins, Christopher; James, Andrew; McGregor, Carolyn; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
In this work, we introduce a novel visualization technique, the Temporal Intensity Map, which visually integrates data values over time to reveal the frequency, duration, and timing of significant features in streaming data. We combine the Temporal Intensity Map with several coordinated visualizations of detected events in data streams to create PhysioEx, a visual dashboard for multiple heterogeneous data streams. We have applied PhysioEx in a design study in the field of neonatal medicine, to support clinical researchers exploring physiologic data streams. We evaluated our method through consultations with domain experts. Results show that our tool provides deep insight capabilities, supports hypothesis generation, and can be well integrated into the workflow of clinical researchers.
Semi-automatic Vortex Flow Classification in 4D PC-MRI Data of the Aorta
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Meuschke, Monique; Köhler, Benjamin; Preim, Uta; Preim, Bernhard; Lawonn, Kai; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
We present an Aortic Vortex Classification (AVOCLA) that allows to classify vortices in the human aorta semi-automatically. Current medical studies assume a strong relation between cardiovascular diseases and blood flow patterns such as vortices. Such vortices are extracted and manually classified according to specific, unstandardized properties. We employ an agglomerative hierarchical clustering to group vortex-representing path lines as basis for the subsequent classification. Classes are based on the vortex' size, orientation and shape, its temporal occurrence relative to the cardiac cycle as well as its spatial position relative to the vessel course. The classification results are presented by a 2D and 3D visualization technique. To confirm the usefulness of both approaches, we report on the results of a user study. Moreover, AVOCLA was applied to 15 datasets of healthy volunteers and patients with different cardiovascular diseases. The results of the semi-automatic classification were qualitatively compared to a manually generated ground truth of two domain experts considering the vortex number and five specific properties.
Critical Points of Gaussian-Distributed Scalar Fields on Simplicial Grids
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Liebmann, Tom; Scheuermann, Gerik; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
Simulations and measurements often result in scalar fields with uncertainty due to errors or output sensitivity estimates. Methods for analyzing topological features of such fields usually are not capable of handling all aspects of the data. They either are not deterministic due to using Monte Carlo approaches, approximate the data with confidence intervals, or miss out on incorporating important properties, such as correlation. In this paper, we focus on the analysis of critical points of Gaussiandistributed scalar fields. We introduce methods to deterministically extract critical points, approximate their probability with high precision, and even capture relations between them resulting in an abstract graph representation. Unlike many other methods, we incorporate all information contained in the data including global correlation. Our work therefore is a first step towards a reliable and complete description of topological features of Gaussian-distributed scalar fields.
Source Inversion by Forward Integration in Inertial Flows
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Günther, Tobias; Theisel, Holger; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
Inertial particles are finite-sized objects traveling with a certain velocity that differs from the underlying carrying flow, i.e., they are mass-dependent and subject to inertia. Their backward integration is in practice infeasible, since a slight change in the initial velocity causes extreme changes in the recovered position. Thus, if an inertial particle is observed, it is difficult to recover where it came from. This is known as the source inversion problem, which has many practical applications in recovering the source of airborne or waterborne pollutions. Inertial trajectories live in a higher dimensional spatio-velocity space. In this paper, we show that this space is only sparsely populated. Assuming that inertial particles are released with a given initial velocity (e.g., from rest), particles may reach a certain location only with a limited set of possible velocities. In fact, with increasing integration duration and dependent on the particle response time, inertial particles converge to a terminal velocity. We show that the set of initial positions that lead to the same location form a curve. We extract these curves by devising a derived vector field in which they appear as tangent curves. Most importantly, the derived vector field only involves forward integrated flow map gradients, which are much more stable to compute than backward trajectories. After extraction, we interactively visualize the curves in the domain and display the reached velocities using glyphs. In addition, we encode the rate of change of the terminal velocity along the curves, which gives a notion for the convergence to the terminal velocity. With this, we present the first solution to the source inversion problem that considers actual inertial trajectories. We apply the method to steady and unsteady flows in both 2D and 3D domains.
MCFTLE: Monte Carlo Rendering of Finite-Time Lyapunov Exponent Fields
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Günther, Tobias; Kuhn, Alexander; Theisel, Holger; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
Traditionally, Lagrangian fields such as finite-time Lyapunov exponents (FTLE) are precomputed on a discrete grid and are ray casted afterwards. This, however, introduces both grid discretization errors and sampling errors during ray marching. In this work, we apply a progressive, view-dependent Monte Carlo-based approach for the visualization of such Lagrangian fields in time-dependent flows. Our approach avoids grid discretization and ray marching errors completely, is consistent, and has a low memory consumption. The system provides noisy previews that converge over time to an accurate high-quality visualization. Compared to traditional approaches, the proposed system avoids explicitly predefined fieldline seeding structures, and uses a Monte Carlo sampling strategy named Woodcock tracking to distribute samples along the view ray. An acceleration of this sampling strategy requires local upper bounds for the FTLE values, which we progressively acquire during the rendering. Our approach is tailored for high-quality visualizations of complex FTLE fields and is guaranteed to faithfully represent detailed ridge surface structures as indicators for Lagrangian coherent structures (LCS). We demonstrate the effectiveness of our approach by using a set of analytic test cases and real-world numerical simulations.
Similarity Voting based Viewpoint Selection for Volumes
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Tao, Yubo; Wang, Qirui; Chen, Wei; Wu, Yingcai; Lin, Hai; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
Previous viewpoint selection methods in volume visualization are generally based on some deterministic measures of viewpoint quality. However, they may not express the familiarity and aesthetic sense of users for features of interest. In this paper, we propose an image-based viewpoint selection model to learn how visualization experts choose representative viewpoints for volumes with similar features. For a given volume, we first collect images with similar features, and these images reflect the viewpoint preferences of the experts when visualizing these features. Each collected image tallies votes to the viewpoints with the best matching based on an image similarity measure, which evaluates the spatial shape and appearance similarity between the collected image and the rendered image from the viewpoint. The optimal viewpoint is the one with the most votes from the collected images, that is, the viewpoint chosen by most visualization experts for similar features. We performed experiments on various volumes available in volume visualization, and made comparisons with traditional viewpoint selection methods. The results demonstrate that our model can select more canonical viewpoints, which are consistent with human perception.
Decoupled Shading for Real-time Heterogeneous Volume Illumination
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Zhang, Yubo; Ma, Kwan-Liu; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
Existing real-time volume rendering techniques which support global illumination are limited in modeling distinct realistic appearances for classified volume data, which is a desired capability in many fields of study for illustration and education. Directly extending the emission-absorption volume integral with heterogeneous material shading becomes unaffordable for real-time applications because the high-frequency view-dependent global lighting needs to be evaluated per sample along the volume integral. In this paper, we present a decoupled shading algorithm for multi-material volume rendering that separates global incident lighting evaluation from per-sample material shading under multiple light sources. We show how the incident lighting calculation can be optimized through a sparse volume integration method. The quality, performance and usefulness of our new multi-material volume rendering method is demonstrated through several examples.
Retailoring Box Splines to Lattices for Highly Isotropic Volume Representations
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Csébfalvi, Balázs; Rácz, Gergely; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
3D box splines are defined by convolving a 1D box function with itself along different directions. In volume visualization, box splines are mainly used as reconstruction kernels that are easy to adapt to various sampling lattices, such as the Cartesian Cubic (CC), Body-Centered Cubic (BCC), and Face-Centered Cubic (FCC) lattices. The usual way of tailoring a box spline to a specific lattice is to span the box spline by exactly those principal directions that span the lattice itself. However, in this case, the preferred directions of the box spline and the lattice are the same, amplifying the anisotropic effects of each other. This leads to an anisotropic volume representation with strongly preferred directions. Therefore, in this paper, we retailor box splines to lattices such that the sets of vectors that span the box spline and the lattice are disjoint sets. As the preferred directions of the box spline and the lattice compensate each other, a more isotropic volume representation can be achieved. We demonstrate this by comparing different combinations of box splines and lattices concerning their anisotropic behavior in tomographic reconstruction and volume visualization.
TextDNA: Visualizing Word Usage with Configurable Colorfields
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Szafir, Danielle Albers; Stuffer, Deidre; Sohail, Yusef; Gleicher, Michael; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
Patterns of words used in different text collections can characterize interesting properties of a corpus. However, these patterns are challenging to explore as they often involve complex relationships across many words and collections in a large space of words. In this paper, we propose a configurable colorfield design to aid this exploration. Our approach uses a dense colorfield overview to present large amounts of data in ways that make patterns perceptible. It allows flexible configuration of both data mappings and aggregations to expose different kinds of patterns, and provides interactions to help connect detailed patterns to the corpus overview. TextDNA, our prototype implementation, leverages the GPU to provide interactivity in the web browser even on large corpora. We present five case studies showing how the tool supports inquiry in corpora ranging in size from single document to millions of books. Our work shows how to make a configurable colorfield approach practical for a range of analytic tasks.
ConToVi: Multi-Party Conversation Exploration using Topic-Space Views
(The Eurographics Association and John Wiley & Sons Ltd., 2016) El-Assady, Mennatallah; Gold, Valentin; Acevedo, Carmela; Collins, Christopher; Keim, Daniel; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
We introduce a novel visual analytics approach to analyze speaker behavior patterns in multi-party conversations. We propose Topic-Space Views to track the movement of speakers across the thematic landscape of a conversation. Our tool is designed to assist political science scholars in exploring the dynamics of a conversation over time to generate and prove hypotheses about speaker interactions and behavior patterns. Moreover, we introduce a glyph-based representation for each speaker turn based on linguistic and statistical cues to abstract relevant text features. We present animated views for exploring the general behavior and interactions of speakers over time and interactive steady visualizations for the detailed analysis of a selection of speakers. Using a visual sedimentation metaphor we enable the analysts to track subtle changes in the flow of a conversation over time while keeping an overview of all past speaker turns. We evaluate our approach on real-world datasets and the results have been insightful to our domain experts.
Location-dependent Generalization of Road Networks Based on Equivalent Destinations
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Dijk, Thomas C. van; Haunert, Jan-Henrik; Oehrlein, Johannes; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
Suppose a user located at a certain vertex in a road network wants to plan a route using a wayfinding map. The user's exact destination may be irrelevant for planning most of the route, because many destinations will be equivalent in the sense that they allow the user to choose almost the same paths. We propose a method to find such groups of destinations automatically and to contract the resulting clusters in a detailed map to achieve a simplified visualization. We model the problem as a clustering problem in rooted, edge-weighted trees. Two vertices are allowed to be in the same cluster if and only if they share at least a given fraction of their path to the root. We analyze some properties of these clusterings and give a linear-time algorithm to compute the minimum-cardinality clustering. This algorithm may have various other applications in network visualization and graph drawing, but in this paper we apply it specifically to focus-and-context map generalization. When contracting shortestpath trees in a geographic network, the computed clustering additionally provides a constant-factor bound on the detour that results from routing using the generalized network instead of the full network. This is a desirable property for wayfinding maps.
A Visual Analytics Framework for Microblog Data Analysis at Multiple Scales of Aggregation
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Zhang, Jiawei; Ahlbrand, Benjamin; Malik, Abish; Chae, Junghoon; Min, Zhiyu; Ko, Sungahn; Ebert, David S.; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
Real-time microblogs can be utilized to provide situational awareness during emergency and disaster events. However, the utilization of these datasets requires the decision makers to perform their exploration and analysis across a range of data scales from local to global, while maintaining a cohesive thematic context of the transition between the different granularity levels. The exploration of different information dimensions at the varied data and human scales remains to be a non-trivial task. To this end, we present a visual analytics situational awareness environment that supports the real-time exploration of microblog data across multiple scales of analysis. We classify microblogs based on a fine-grained, crisis-related categorization approach, and visualize the spatiotemporal evolution of multiple categories by coupling a spatial lens with a glyph-based visual design. We propose a transparency-based spatial context preserving technique that maintains a smooth transition between different spatial scales. To evaluate our system, we conduct user studies and provide domain expert feedback.
Composite Flow Maps
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Cornel, Daniel; Konev, Artem; Sadransky, Bernhard; Horváth, Zsolt; Brambilla, Andrea; Viola, Ivan; Waser, Jürgen; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
Flow maps are widely used to provide an overview of geospatial transportation data. Existing solutions lack the support for the interactive exploration of multiple flow components at once. Flow components are given by different materials being transported, different flow directions, or by the need for comparing alternative scenarios. In this paper, we combine flows as individual ribbons in one composite flow map. The presented approach can handle an arbitrary number of sources and sinks. To avoid visual clutter, we simplify our flow maps based on a force-driven algorithm, accounting for restrictions with respect to application semantics. The goal is to preserve important characteristics of the geospatial context. This feature also enables us to highlight relevant spatial information on top of the flow map such as traffic conditions or accessibility. The flow map is computed on the basis of flows between zones. We describe a method for auto-deriving zones from geospatial data according to application requirements. We demonstrate the method in real-world applications, including transportation logistics, evacuation procedures, and water simulation. Our results are evaluated with experts from corresponding fields.
Exploratory Visual Analysis for Animal Movement Ecology
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Slingsby, Aidan; Loon, Emiel van; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
Movement ecologists study animals' movement to help understand their behaviours and interactions with each other and the environment. Data from GPS loggers are increasingly important for this. These data need to be processed, segmented and summarised for further visual and statistical analysis, often using predefined parameters. Usually, this process is separate from the subsequent visual and statistical analysis, making it difficult for these results to inform the data processing and to help set appropriate scale and thresholds parameters. This paper explores the use of highly interactive visual analytics techniques to close the gap between processing raw data and exploratory visual analysis. Working closely with animal movement ecologists, we produced requirements to enable data characteristics to be determined, initial research questions to be investigated, and the suitability of data for further analysis to be assessed. We design visual encodings and interactions to meet these requirements and provide software that implements them. We demonstrate these techniques with indicative research questions for a number of bird species, provide software, and discuss wider implications for animal movement ecology.
AVOCADO: Visualization of Workflow-Derived Data Provenance for Reproducible Biomedical Research
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Stitz, Holger; Luger, Stefan; Streit, Marc; Gehlenborg, Nils; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
A major challenge in data-driven biomedical research lies in the collection and representation of data provenance information to ensure that findings are reproducibile. In order to communicate and reproduce multi-step analysis workflows executed on datasets that contain data for dozens or hundreds of samples, it is crucial to be able to visualize the provenance graph at different levels of aggregation. Most existing approaches are based on node-link diagrams, which do not scale to the complexity of typical data provenance graphs. In our proposed approach, we reduce the complexity of the graph using hierarchical and motif-based aggregation. Based on user action and graph attributes, a modular degree-of-interest (DoI) function is applied to expand parts of the graph that are relevant to the user. This interest-driven adaptive approach to provenance visualization allows users to review and communicate complex multi-step analyses, which can be based on hundreds of files that are processed by numerous workflows. We have integrated our approach into an analysis platform that captures extensive data provenance information, and demonstrate its effectiveness by means of a biomedical usage scenario.
From Visual Exploration to Storytelling and Back Again
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Gratzl, Samuel; Lex, Alexander; Gehlenborg, Nils; Cosgrove, Nicola; Streit, Marc; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
The primary goal of visual data exploration tools is to enable the discovery of new insights. To justify and reproduce insights, the discovery process needs to be documented and communicated. A common approach to documenting and presenting findings is to capture visualizations as images or videos. Images, however, are insufficient for telling the story of a visual discovery, as they lack full provenance information and context. Videos are difficult to produce and edit, particularly due to the non-linear nature of the exploratory process. Most importantly, however, neither approach provides the opportunity to return to any point in the exploration in order to review the state of the visualization in detail or to conduct additional analyses. In this paper we present CLUE (Capture, Label, Understand, Explain), a model that tightly integrates data exploration and presentation of discoveries. Based on provenance data captured during the exploration process, users can extract key steps, add annotations, and author ''Vistories'', visual stories based on the history of the exploration. These Vistories can be shared for others to view, but also to retrace and extend the original analysis. We discuss how the CLUE approach can be integrated into visualization tools and provide a prototype implementation. Finally, we demonstrate the general applicability of the model in two usage scenarios: a Gapminder-inspired visualization to explore public health data and an example from molecular biology that illustrates how Vistories could be used in scientific journals.

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