DevLan/vispubs huggingface dataset:
Visual Comparison of Eye Movement Patterns
Authors: Tanja Blascheck, Markus Schweizer, Fabian Beck 0001, Thomas Ertl
Published at EuroVis, 2017

In eye tracking research, finding eye movement patterns and similar strategies between participants’ eye movements is important to understand task solving strategies and obstacles. In this application paper, we present a graph comparison method using radial graphs that show Areas of Interest (AOIs) and their transitions. An analyst investigates a single graph based on dwell times, directed transitions, and temporal AOI sequences. Two graphs can be compared directly and temporal changes may be analyzed. A list and matrix approach facilitate the analyst to contrast more than two graphs guided by visually encoded graph similarities. We evaluated our approach in case studies with three eye tracking and visualization experts. They identified temporal transition patterns of eye movements across participants, groups of participants, and outliers.
Visual Exploration of Global Trade Networks with Time-Dependent and Weighted Hierarchical Edge Bundles on GPU
Authors: J. Hofmann, M. Größler, Manuel Rubio-Sánchez, Peter-Paul Pichler, Dirk J. Lehmann
Published at EuroVis, 2017

The UN Comtrade database is the world's largest repository of bilateral trade data. Their complexity poses a challenge to visualization systems, leading to issues such as scalability and visual clutter. Thus, we propose a radial layout‐based visual exploration system to enable the user to smoothly explore the change over time and to explore different commodity classes at once by using a novel edge bundling concept. We evaluated our system with the aid of a group of domain experts.
Visual Narrative Flow: Exploring Factors Shaping Data Visualization Story Reading Experiences
Authors: Sean McKenna, Nathalie Henry Riche, Bongshin Lee, Jeremy Boy, Miriah Meyer
Published at EuroVis, 2017

Many factors can shape the flow of visual data‐driven stories, and thereby the way readers experience those stories. Through the analysis of 80 existing stories found on popular websites, we systematically investigate and identify seven characteristics of these stories, which we name “flow‐factors,” and we illustrate how they feed into the broader concept of “visual narrative flow.” These flow‐factors are navigation input, level of control, navigation progress, story layout, role of visualization, story progression, and navigation feedback. We also describe a series of studies we conducted, which shed initial light on how different visual narrative flows impact the reading experience. We report on two exploratory studies, in which we gathered reactions and preferences of readers for stepper‐ vs. scroller‐driven flows. We then report on a crowdsourced study with 240 participants, in which we explore the effect of the combination of different flow‐factors on readers’ engagement. Our results indicate that visuals and navigation feedback (e.g., static vs. animated transitions) have an impact on readers’ engagement, while level of control (e.g., discrete vs. continuous) may not.
Visual Verification of Cancer Staging for Therapy Decision Support
Authors: Mario A. Cypko, Jan Wojdziak, Matthaeus Stoehr, Bettina Kirchner, Bernhard Preim, Andreas Dietz, Heinz U. Lemke, Steffen Oeltze-Jafra
Published at EuroVis, 2017

It is generally accepted practice that each cancer patient case should be discussed in a clinical expert meeting, the so‐called tumor board. A central role in finding the best therapy options for patients with solid tumors plays the Tumor, lymph Node, and Metastasis staging (TNM staging). Correctness of TNM staging has a significant impact on the therapy choice and hence on the patient's post‐therapeutic quality of life or even survival. If inconsistencies in the TNM staging occur, possible explanations and solutions must be found based on the complex patient records, which takes the costly time of (multiple) physicians. We propose a more efficient visual analysis component, which supports a physician in verifying the given TNM staging before forwarding it to the tumor board. Our component comprises a Bayesian network model of the TNM staging process. Using information from the patient records and Bayesian inference, the models computes a patient‐specific TNM staging, which is then explored and compared to the given staging by means of a graph‐based visualization. Our component is implemented in a research prototype that supports an understanding of the model computations, allows for a fast identification of important influencing factors, and facilitates a quick detection of differences between two TNM stagings. We evaluated our component with five physicians, each studying 20 cases of laryngeal cancer.
Visualization of Delay Uncertainty and its Impact on Train Trip Planning: A Design Study
Authors: Marcel Wunderlich, Kathrin Ballweg, Georg Fuchs, Tatiana von Landesberger
Published at EuroVis, 2017

Uncertainty about possible train delays has an impact on train trips, as the exact arrival time is unknown during trip planning. Delays can lead to missing a connecting train at the transfer station, or to coming too late to an appointment at the destination. Facing this uncertainty, the traveler may wish to use an earlier train or a different connection arriving well before the appointment. Currently, train trip planning is based on scheduled times of connections between two stations. Information about approximate delays is only available shortly before train departure. Although several visualization approaches can show temporal uncertainty, we are not aware of any visual design specifically supporting trip planning, which can show delay uncertainty and its impact on the connections. We propose and evaluate a visual design which extends train trip planning with delay uncertainty. It shows the scheduled train connections together with their expected train delays as well as their impacts on both the arrival time, and the potential of missing a transfer. The visualization also includes information about alternative connections in case of these critical transfers. In this way the user is able to judge which train connection is suitable for a trip. We conducted a user study with 76 participants to evaluate our design. We compared it to two alternative presentations that are prominent in Germany. The study showed that our design performs comparably well for tasks concerning train schedules. The additional uncertainty display as well as the visualization of alternative connections was appreciated and well understood. The participants were able to estimate when they would likely arrive at their destination despite possible train delays while they were unable to estimate this with existing presentations. The users would prefer to use the new design for their trip planning.
Visualizing a Sequence of a Thousand Graphs (or Even More)
Authors: Michael Burch, Marcel Hlawatsch, Daniel Weiskopf
Published at EuroVis, 2017

The visualization of dynamic graphs demands visually encoding at least three major data dimensions: vertices, edges, and time steps. Many of the state‐of‐the‐art techniques can show an overview of vertices and edges but lack a data‐scalable visual representation of the time aspect. In this paper, we address the problem of displaying dynamic graphs with a thousand or more time steps. Our proposed interleaved parallel edge splatting technique uses a time‐to‐space mapping and shows the complete dynamic graph in a static visualization. It provides an overview of all data dimensions, allowing for visually detecting time‐varying data patterns; hence, it serves as a starting point for further data exploration. By applying clustering and ordering techniques on the vertices, edge splatting on the links, and a dense time‐to‐space mapping, our approach becomes visually scalable in all three dynamic graph data dimensions. We illustrate the usefulness of our technique by applying it to call graphs and US domestic flight data with several hundred vertices, several thousand edges, and more than a thousand time steps.
Visualizing Probabilistic Multi-Phase Fluid Simulation Data using a Sampling Approach
Authors: Mathias Hummel, Lisa Jöckel, J. Schäfer, Mark W. Hlawitschka, Christoph Garth
Published at EuroVis, 2017

Eulerian Method of Moment (MoM) solvers are gaining popularity for multi‐phase CFD simulation involving bubbles or droplets in process engineering. Because the actual positions of bubbles are uncertain, the spatial distribution of bubbles is described by scalar fields of moments, which can be interpreted as probability density functions. Visualizing these simulation results and comparing them to physical experiments is challenging, because neither the shape nor the distribution of bubbles described by the moments lend themselves to visual interpretation.In this work, we describe a visualization approach that provides explicit instances of the bubble distribution and produces bubble geometry based on local flow properties. To facilitate animation, the instancing of the bubble distribution provides coherence over time by advancing bubbles between time steps and updating the distribution. Our approach provides an intuitive visualization and enables direct visual comparison of simulation results to physical experiments.
Visualizing the Uncertainty of Graph-based 2D Segmentation with Min-path Stability
Authors: Brian Summa, Julien Tierny, Valerio Pascucci
Published at EuroVis, 2017

This paper presents a novel approach to visualize the uncertainty in graph‐based segmentations of scalar data. Segmentation of 2D scalar data has wide application in a variety of scientific and medical domains. Typically, a segmentation is presented as a single unambiguous boundary although the solution is often uncertain due to noise or blur in the underlying data as well as imprecision in user input. Our approach provides insight into this uncertainty by computing the “min‐path stability”, a scalar measure analyzing the stability of the segmentation given a set of input constraints. Our approach is efficient, easy to compute, and can be generally applied to either graph cuts or live‐wire (even partial) segmentations. In addition to its general applicability, our new approach to graph cuts uncertainty visualization improves on the time complexity of the current state‐of‐the‐art with an additional fast approximate solution. We also introduce a novel query enabled by our approach which provides users with alternate segmentations by efficiently extracting local minima of the segmentation optimization. Finally, we evaluate our approach and demonstrate its utility on data from scientific and medical applications.
Affective Color in Visualization
Authors: Lyn Bartram, Abhisekh Patra, Maureen C. Stone
Published at CHI, 2017

Communicating the right affect, a feeling, experience or emotion, is critical in creating engaging visual communication. We carried out three studies examining how different color properties (lightness, chroma and hue) and different palette properties (combinations and distribution of colors) contribute to different affective interpretations in information visualization where the numbers of colors is typically smaller than the rich palettes used in design. Our results show how color and palette properties can be manipulated to achieve affective expressiveness even in the small sets of colors used for data encoding in information visualization.
AVUI: Designing a Toolkit for Audiovisual Interfaces
Authors: Nuno N. Correia, Atau Tanaka
Published at CHI, 2017

The combined use of sound and image has a rich history, from audiovisual artworks to research exploring the potential of data visualization and sonification. However, we lack standard tools or guidelines for audiovisual (AV) interaction design, particularly for live performance. We propose the AVUI (AudioVisual User Interface), where sound and image are used together in a cohesive way in the interface; and an enabling technology, the ofxAVUI toolkit. AVUI guidelines and ofxAVUI were developed in a three-stage process, together with AV producers: 1) participatory design activities; 2) prototype development; 3) encapsulation of prototype as a plug-in, evaluation, and roll out. Best practices identified include: reconfigurable interfaces and mappings; object-oriented packaging of AV and UI; diverse sound visualization; flexible media manipulation and management. The toolkit and a mobile app developed using it have been released as open-source. Guidelines and toolkit demonstrate the potential of AVUI and offer designers a convenient framework for AV interaction design.
Bottom-up vs. Top-down: Trade-offs in Efficiency, Understanding, Freedom and Creativity with InfoVis Tools
Authors: Gonzalo Gabriel Méndez, Uta Hinrichs, Miguel A. Nacenta
Published at CHI, 2017

The emergence of tools that support fast-and-easy visualization creation by non-experts has made the benefits of InfoVis widely accessible. Key features of these tools include attribute-level operations, automated mappings, and visualization templates. However, these features shield people from lower-level visualization design steps, such as the specific mapping of data points to visuals. In contrast, recent research promotes constructive visualization where individual data units and visuals are directly manipulated. We present a qualitative study comparing people's visualization processes using two visualization tools: one promoting a top-down approach to visualization construction (Tableau Desktop) and one implementing a bottom-up constructive visualization approach (iVoLVER). Our results show how the two approaches influence: 1) the visualization process, 2) decisions on the visualization design, 3) the feeling of control and authorship, and 4) the willingness to explore alternative designs. We discuss the complex trade-offs between the two approaches and outline considerations for designing better visualization tools.
Building with Data: Architectural Models as Inspiration for Data Physicalization
Authors: Carmen Hull, Wesley Willett
Published at CHI, 2017

In this paper we analyze the role of physical scale models in the architectural design process and apply insights from architecture for the creation and use of data physicalizations. Based on a survey of the architecture literature on model making and ten interviews with practicing architects, we describe the role of physical models as a tool for exploration and communication. From these observations, we identify trends in the use of physical models in architecture, which have the potential to inform the design of data physicalizations. We identify four functions of architectural modeling that can be directly adapted for use in the process of building rich data models. Finally, we discuss how the visualization community can apply observations from architecture to the design of new data physicalizations.
Designing Game-Based Myoelectric Prosthesis Training
Authors: Aaron Tabor, Scott Bateman, Erik J. Scheme, David R. Flatla, Kathrin Gerling
Published at CHI, 2017

A myoelectric prosthesis (myo) is a dexterous artificial limb controlled by muscle contractions. Learning to use a myo can be challenging, so extensive training is often required to use a myo prosthesis effectively. Signal visualizations and simple muscle-controlled games are currently used to help patients train their muscles, but are boring and frustrating. Furthermore, current training systems require expensive medical equipment and clinician oversight, restricting training to infrequent clinical visits. To address these limitations, we developed a new game that promotes fun and success, and shows the viability of a low-cost myoelectric input device. We adapted a user-centered design (UCD) process to receive feedback from patients, clinicians, and family members as we iteratively addressed challenges to improve our game. Through this work, we introduce a free and open myo training game, provide new information about the design of myo training games, and reflect on an adapted UCD process for the practical iterative development of therapeutic games.
Effects of Sharing Physiological States of Players in a Collaborative Virtual Reality Gameplay
Authors: Arindam Dey 0001, Thammathip Piumsomboon, Youngho Lee, Mark Billinghurst
Published at CHI, 2017

Interfaces for collaborative tasks, such as multiplayer games can enable more effective and enjoyable collaboration. However, in these systems, the emotional states of the users are often not communicated properly due to their remoteness from one another. In this paper, we investigate the effects of showing emotional states of one collaborator to the other during an immersive Virtual Reality (VR) gameplay experience. We created two collaborative immersive VR games that display the real-time heart-rate of one player to the other. The two different games elicited different emotions, one joyous and the other scary. We tested the effects of visualizing heart-rate feedback in comparison with conditions where such a feedback was absent. The games had significant main effects on the overall emotional experience.
Empirical Analysis of the Subjective Impressions and Objective Measures of Domain Scientists' Visual Analytic Judgments
Authors: Aritra Dasgupta, Susannah Burrows, Kyungsik Han, Philip J. Rasch
Published at CHI, 2017

Scientists often use specific data analysis and presentation methods familiar within their domain. But does high familiarity drive better analytical judgment? This question is especially relevant when familiar methods themselves can have shortcomings: many visualizations used conventionally for scientific data analysis and presentation do not follow established best practices. This necessitates new methods that might be unfamiliar yet prove to be more effective. But there is little empirical understanding of the relationships between scientists' subjective impressions about familiar and unfamiliar visualizations and objective measures of their visual analytic judgments. To address this gap and to study these factors, we focus on visualizations used for comparison of climate model performance. We report on a comprehensive survey-based user study with 47 climate scientists and present an analysis of: i) relationships among scientists' familiarity, their perceived levels of comfort, confidence, accuracy, and objective measures of accuracy, and ii) relationships among domain experience, visualization familiarity, and post-study preference.
Evaluating Perceptually Complementary Views for Network Exploration Tasks
Authors: Chunlei Chang, Benjamin Bach, Tim Dwyer, Kim Marriott
Published at CHI, 2017

We explore the relative merits of matrix, node-link and combined side-by-side views for the visualisation of weighted networks with three controlled studies: (1) finding the most effective visual encoding for weighted edges in matrix representations; (2) comparing matrix, node-link and combined views for static weighted networks; and (3) comparing MatrixWave, Sankey and combined views of both for event-sequence data. Our studies underline that node-link and matrix views are suited to different analysis tasks. For the combined view, our studies show that there is a perceptually complementary effect in terms of improved accuracy for some tasks, but that there is a cost in terms of longer completion time than the faster of the two techniques alone. Eye-movement data shows that for many tasks participants strongly favour one of the two views, after trying both in the training phase.
Explaining the Gap: Visualizing One's Predictions Improves Recall and Comprehension of Data
Authors: Yea-Seul Kim, Katharina Reinecke, Jessica Hullman
Published at CHI, 2017

Information visualizations use interactivity to enable user-driven querying of visualized data. However, users' interactions with their internal representations, including their expectations about data, are also critical for a visualization to support learning. We present multiple graphically-based techniques for eliciting and incorporating a user's prior knowledge about data into visualization interaction. We use controlled experiments to evaluate how graphically eliciting forms of prior knowledge and presenting feedback on the gap between prior knowledge and the observed data impacts a user's ability to recall and understand the data. We find that participants who are prompted to reflect on their prior knowledge by predicting and self-explaining data outperform a control group in recall and comprehension. These effects persist when participants have moderate or little prior knowledge on the datasets. We discuss how the effects differ based on text versus visual presentations of data. We characterize the design space of graphical prediction and feedback techniques and describe design recommendations.
GIAnT: Visualizing Group Interaction at Large Wall Displays
Authors: Ulrich von Zadow, Raimund Dachselt
Published at CHI, 2017

Large interactive displays are increasingly important and a relevant research topic, and several studies have focused on wall interaction. However, in many cases, thorough user studies currently require time-consuming video analysis and coding. We present the Group Interaction Analysis Toolkit GIAnT, which provides a rich set of visualizations supporting investigation of multi-user interaction at large display walls. GIAnT focuses on visualizing time periods, making it possible to gain overview-level insights quickly. The toolkit is designed to be extensible and features several carefully crafted visualizations: A novel timeline visualization shows movement in front of the wall over time, a wall visualization shows interactions on the wall and gaze data, and a floor visualization displays user positions. In addition, GIAnT shows the captured video stream along with basic statistics. We validate our tool by analyzing how it supports investigating major research topics and by practical use in evaluating a cooperative game.
GraphScape: A Model for Automated Reasoning about Visualization Similarity and Sequencing
Authors: Younghoon Kim, Kanit Wongsuphasawat, Jessica Hullman, Jeffrey Heer
Published at CHI, 2017

We present GraphScape, a directed graph model of the vi- sualization design space that supports automated reasoning about visualization similarity and sequencing. Graph nodes represent grammar-based chart specifications and edges rep- resent edits that transform one chart to another. We weight edges with an estimated cost of the difficulty of interpreting a target visualization given a source visualization. We con- tribute (1) a method for deriving transition costs via a partial ordering of edit operations and the solution of a resulting lin- ear program, and (2) a global weighting term that rewards consistency across transition subsequences. In a controlled experiment, subjects rated visualization sequences covering a taxonomy of common transition types. In all but one case, GraphScape's highest-ranked suggestion aligns with subjects' top-rated sequences. Finally, we demonstrate applications of GraphScape to automatically sequence visualization presen- tations, elaborate transition paths between visualizations, and recommend design alternatives (e.g., to improve scalability while minimizing design changes).