EuroVis17: Eurographics Conference on Visualization

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https://diglib.eg.org/handle/10.2312/3607288

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Adaptable Radial Axes Plots for Improved Multivariate Data Visualization
(The Eurographics Association and John Wiley & Sons Ltd., 2017) Rubio-Sánchez, Manuel; Sanchez, Alberto; Lehmann, Dirk J.; Heer, Jeffrey and Ropinski, Timo and van Wijk, Jarke
Radial axes plots are multivariate visualization techniques that extend scatterplots in order to represent high-dimensional data as points on an observable display. Well-known methods include star coordinates or principal component biplots, which represent data attributes as vectors that de ne axes, and produce linear dimensionality reduction mappings. In this paper we propose a hybrid approach that bridges the gap between star coordinates and principal component biplots, which we denominate adaptable radial axes plots . It is based on solving convex optimization problems where users can: (a) update the axis vectors interactively, as in star coordinates, while producing mappings that enable to estimate attribute values optimally through labeled axes, similarly to principal component biplots; (b) use different norms in order to explore additional nonlinear mappings of the data; and (c) include weights and constraints in the optimization problems for sorting the data along one axis. The result is a exible technique that complements, extends, and enhances current radial methods for data analysis.
Comparing Personal Image Collections with PICTuReVis
(The Eurographics Association and John Wiley & Sons Ltd., 2017) Corput, Paul van der; Wijk, Jarke J. van; Heer, Jeffrey and Ropinski, Timo and van Wijk, Jarke
Digital image collections contain a wealth of information, which for instance can be used to trace illegal activities and investigate criminal networks. We present a method that enables analysts to reveal relations among people, based on the patterns in their collections. Similar temporal and spatial patterns can be found using a parameterized algorithm, visualization is used to choose the right parameters and to inspect the patterns found. The visualization shows relations between image properties: the person it belongs to, the concepts in the image, its time stamp and location. We demonstrate the method with image collections of 10;000 people containing 460;000 images in total.
NEREx: Named-Entity Relationship Exploration in Multi-Party Conversations
(The Eurographics Association and John Wiley & Sons Ltd., 2017) El-Assady, Mennatallah; Sevastjanova, Rita; Gipp, Bela; Keim, Daniel A.; Collins, Christopher; Heer, Jeffrey and Ropinski, Timo and van Wijk, Jarke
Abstract We present NEREx, an interactive visual analytics approach for the exploratory analysis of verbatim conversational transcripts. By revealing different perspectives on multi-party conversations, NEREx gives an entry point for the analysis through high-level overviews and provides mechanisms to form and verify hypotheses through linked detail-views. Using a tailored named-entity extraction, we abstract important entities into ten categories and extract their relations with a distance-restricted entity-relationship model. This model complies with the often ungrammatical structure of verbatim transcripts, relating two entities if they are present in the same sentence within a small distance window. Our tool enables the exploratory analysis of multi-party conversations using several linked views that reveal thematic and temporal structures in the text. In addition to distant-reading, we integrated close-reading views for a text-level investigation process. Beyond the exploratory and temporal analysis of conversations, NEREx helps users generate and validate hypotheses and perform comparative analyses of multiple conversations. We demonstrate the applicability of our approach on real-world data from the 2016 U.S. Presidential Debates through a qualitative study with three domain experts from political science.
Overview + Detail Visualization for Ensembles of Diffusion Tensors
(The Eurographics Association and John Wiley & Sons Ltd., 2017) Zhang, Changgong; Caan, Matthan W. A.; Höllt, Thomas; Eisemann, Elmar; Vilanova, Anna; Heer, Jeffrey and Ropinski, Timo and van Wijk, Jarke
A Diffusion Tensor Imaging (DTI) group study consists of a collection of volumetric diffusion tensor datasets (i.e., an ensemble) acquired from a group of subjects. The multivariate nature of the diffusion tensor imposes challenges on the analysis and the visualization. These challenges are commonly tackled by reducing the diffusion tensors to scalar-valued quantities that can be analyzed with common statistical tools. However, reducing tensors to scalars poses the risk of losing intrinsic information about the tensor. Visualization of tensor ensemble data without loss of information is still a largely unsolved problem. In this work, we propose an overview + detail visualization to facilitate the tensor ensemble exploration. We define an ensemble representative tensor and variations in terms of the three intrinsic tensor properties (i.e., scale, shape, and orientation) separately. The ensemble summary information is visually encoded into the newly designed aggregate tensor glyph which, in a spatial layout, functions as the overview. The aggregate tensor glyph guides the analyst to interesting areas that would need further detailed inspection. The detail views reveal the original information that is lost during aggregation. It helps the analyst to further understand the sources of variation and formulate hypotheses. To illustrate the applicability of our prototype, we compare with most relevant previous work through a user study and we present a case study on the analysis of a brain diffusion tensor dataset ensemble from healthy volunteers.
EuroVis 2017: Frontmatter
(Eurographics Association, 2017) Heer, Jeffrey; Ropinski, Timo; van Wijk, Jarke;
Nested Tracking Graphs
(The Eurographics Association and John Wiley & Sons Ltd., 2017) Lukasczyk, Jonas; Weber, Gunther; Maciejewski, Ross; Garth, Christoph; Leitte, Heike; Heer, Jeffrey and Ropinski, Timo and van Wijk, Jarke
Tracking graphs are a well established tool in topological analysis to visualize the evolution of components and their properties over time, i.e., when components appear, disappear, merge, and split. However, tracking graphs are limited to a single level threshold and the graphs may vary substantially even under small changes to the threshold. To examine the evolution of features for varying levels, users have to compare multiple tracking graphs without a direct visual link between them. We propose a novel, interactive, nested graph visualization based on the fact that the tracked superlevel set components for different levels are related to each other through their nesting hierarchy. This approach allows us to set multiple tracking graphs in context to each other and enables users to effectively follow the evolution of components for different levels simultaneously. We demonstrate the effectiveness of our approach on datasets from finite pointset methods, computational fluid dynamics, and cosmology simulations.
Linear Discriminative Star Coordinates for Exploring Class and Cluster Separation of High Dimensional Data
(The Eurographics Association and John Wiley & Sons Ltd., 2017) Wang, Yunhai; Li, Jingting; Nie, Feiping; Theisel, Holger; Gong, Minglun; Lehmann, Dirk J.; Heer, Jeffrey and Ropinski, Timo and van Wijk, Jarke
One main task for domain experts in analysing their nD data is to detect and interpret class/cluster separations and outliers. In fact, an important question is, which features/dimensions separate classes best or allow a cluster-based data classification. Common approaches rely on projections from nD to 2D, which comes with some challenges, such as: The space of projection contains an infinite number of items. How to find the right one? The projection approaches suffers from distortions and misleading effects. How to rely to the projected class/cluster separation? The projections involve the complete set of dimensions/ features. How to identify irrelevant dimensions? Thus, to address these challenges, we introduce a visual analytics concept for the feature selection based on linear discriminative star coordinates (DSC), which generate optimal cluster separating views in a linear sense for both labeled and unlabeled data. This way the user is able to explore how each dimension contributes to clustering. To support to explore relations between clusters and data dimensions, we provide a set of cluster-aware interactions allowing to smartly iterate through subspaces of both records and features in a guided manner. We demonstrate our features selection approach for optimal cluster/class separation analysis with a couple of experiments on real-life benchmark high-dimensional data sets.
Visualizing the Uncertainty of Graph-based 2D Segmentation with Min-path Stability
(The Eurographics Association and John Wiley & Sons Ltd., 2017) Summa, Brian; Tierny, Julien; Pascucci, Valerio; Heer, Jeffrey and Ropinski, Timo and van Wijk, Jarke
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-ofthe- 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.
Steering the Craft: UI Elements and Visualizations for Supporting Progressive Visual Analytics
(The Eurographics Association and John Wiley & Sons Ltd., 2017) Badam, Sriram Karthik; Elmqvist, Niklas; Fekete, Jean-Daniel; Heer, Jeffrey and Ropinski, Timo and van Wijk, Jarke
Progressive visual analytics (PVA) has emerged in recent years to manage the latency of data analysis systems. When analysis is performed progressively, rough estimates of the results are generated quickly and are then improved over time. Analysts can therefore monitor the progression of the results, steer the analysis algorithms, and make early decisions if the estimates provide a convincing picture. In this article, we describe interface design guidelines for helping users understand progressively updating results and make early decisions based on progressive estimates. To illustrate our ideas, we present a prototype PVA tool called INSIGHTSFEED for exploring Twitter data at scale. As validation, we investigate the tradeoffs of our tool when exploring a Twitter dataset in a user study. We report the usage patterns in making early decisions using the user interface, guiding computational methods, and exploring different subsets of the dataset, compared to sequential analysis without progression.
Generating Tile Maps
(The Eurographics Association and John Wiley & Sons Ltd., 2017) McNeill, Graham; Hale, Scott A.; Heer, Jeffrey and Ropinski, Timo and van Wijk, Jarke
Tile maps are an important tool in thematic cartography with distinct qualities (and limitations) that distinguish them from better-known techniques such as choropleths, cartograms and symbol maps. Specifically, tile maps display geographic regions as a grid of identical tiles so large regions do not dominate the viewer's attention and small regions are easily seen. Furthermore, complex data such as time series can be shown on each tile in a consistent format, and the grid layout facilitates comparisons across tiles. Whilst a small number of handcrafted tile maps have become popular, the time-consuming process of creating new tile maps limits their wider use. To address this issue, we present an algorithm that generates a tile map of the specified type (e.g. square, hexagon, triangle) from raw shape data. Since the 'best' tile map depends on the specific geography visualized and the task to be performed, the algorithm generates and ranks multiple tile maps and allows the user to choose the most appropriate. The approach is demonstrated on a range of examples using a prototype browser-based application.
Stardust: Accessible and Transparent GPU Support for Information Visualization Rendering
(The Eurographics Association and John Wiley & Sons Ltd., 2017) Ren, Donghao; Lee, Bongshin; Höllerer, Tobias; Heer, Jeffrey and Ropinski, Timo and van Wijk, Jarke
Web-based visualization libraries are in wide use, but performance bottlenecks occur when rendering, and especially animating, a large number of graphical marks. While GPU-based rendering can drastically improve performance, that paradigm has a steep learning curve, usually requiring expertise in the computer graphics pipeline and shader programming. In addition, the recent growth of virtual and augmented reality poses a challenge for supporting multiple display environments beyond regular canvases, such as a Head Mounted Display (HMD) and Cave Automatic Virtual Environment (CAVE). In this paper, we introduce a new web-based visualization library called Stardust, which provides a familiar API while leveraging GPU's processing power. Stardust also enables developers to create both 2D and 3D visualizations for diverse display environments using a uniform API. To demonstrate Stardust's expressiveness and portability, we present five example visualizations and a coding playground for four display environments. We also evaluate its performance by comparing it against the standard HTML5 Canvas, D3, and Vega.
Dynamic Visual Abstraction of Soccer Movement
(The Eurographics Association and John Wiley & Sons Ltd., 2017) Sacha, Dominik; Al-Masoudi, Feeras; Stein, Manuel; Schreck, Tobias; Keim, Daniel A.; Andrienko, Gennady; Janetzko, Halldór; Heer, Jeffrey and Ropinski, Timo and van Wijk, Jarke
Trajectory-based visualization of coordinated movement data within a bounded area, such as player and ball movement within a soccer pitch, can easily result in visual crossings, overplotting, and clutter. Trajectory abstraction can help to cope with these issues, but it is a challenging problem to select the right level of abstraction (LoA) for a given data set and analysis task. We present a novel dynamic approach that combines trajectory simplification and clustering techniques with the goal to support interpretation and understanding of movement patterns. Our technique provides smooth transitions between different abstraction types that can be computed dynamically and on-the-fly. This enables the analyst to effectively navigate and explore the space of possible abstractions in large trajectory data sets. Additionally, we provide a proof of concept for supporting the analyst in determining the LoA semi-automatically with a recommender system. Our approach is illustrated and evaluated by case studies, quantitative measures, and expert feedback. We further demonstrate that it allows analysts to solve a variety of analysis tasks in the domain of soccer.
Measuring Symmetry in Drawings of Graphs
(The Eurographics Association and John Wiley & Sons Ltd., 2017) Welch, Eric; Kobourov, Stephen; Heer, Jeffrey and Ropinski, Timo and van Wijk, Jarke
Layout symmetry is an important and desired feature in graph drawing. While there is a substantial body of work in computer vision around the detection and measurement of symmetry in images, there has been little effort to define and validate meaningful measures of the symmetry of graph drawings. In this paper, we evaluate two algorithms that have been proposed for measuring graph drawing symmetry, comparing their judgments to those of human subjects, and investigating the use of stress as an alternative measure of symmetry. We discuss advantages and disadvantages of these measures, possible ways to improve them, and implications for the design of algorithms that optimize the symmetry in the layout.
Visualizing Probabilistic Multi-Phase Fluid Simulation Data using a Sampling Approach
(The Eurographics Association and John Wiley & Sons Ltd., 2017) Hummel, Mathias; Jöckel, Lisa; Schäfer, Jan; Hlawitschka, Mark Werner; Garth, Christoph; Heer, Jeffrey and Ropinski, Timo and van Wijk, Jarke
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.
Glyph-Based Comparative Stress Tensor Visualization in Cerebral Aneurysms
(The Eurographics Association and John Wiley & Sons Ltd., 2017) Meuschke, Monique; Voß, Samuel; Beuing, Oliver; Preim, Bernhard; Lawonn, Kai; Heer, Jeffrey and Ropinski, Timo and van Wijk, Jarke
We present the first visualization tool that enables a comparative depiction of structural stress tensor data for vessel walls of cerebral aneurysms. Such aneurysms bear the risk of rupture, whereas their treatment also carries considerable risks for the patient. Medical researchers emphasize the importance of analyzing the interaction of morphological and hemodynamic information for the patient-specific rupture risk evaluation and treatment analysis. Tensor data such as the stress inside the aneurysm walls characterizes the interplay between the morphology and blood flow and seems to be an important rupture-prone criterion. We use different glyph-based techniques to depict local stress tensors simultaneously and compare their applicability to cerebral aneurysms in a user study. We thus offer medical researchers an effective visual exploration tool to assess the aneurysm rupture risk.We developed a GPU-based implementation of our techniques with a flexible interactive data exploration mechanism. Our depictions are designed in collaboration with domain experts, and we provide details about the evaluation.
Integrating Visual Analytics Support for Grounded Theory Practice in Qualitative Text Analysis
(The Eurographics Association and John Wiley & Sons Ltd., 2017) Chandrasegaran, Senthil; Badam, Sriram Karthik; Kisselburgh, Lorraine; Ramani, Karthik; Elmqvist, Niklas; Heer, Jeffrey and Ropinski, Timo and van Wijk, Jarke
We present an argument for using visual analytics to aid Grounded Theory methodologies in qualitative data analysis. Grounded theory methods involve the inductive analysis of data to generate novel insights and theoretical constructs. Making sense of unstructured text data is uniquely suited for visual analytics. Using natural language processing techniques such as parts-ofspeech tagging, retrieving information content, and topic modeling, different parts of the data can be structured and semantically associated, and interactively explored, thereby providing conceptual depth to the guided discovery process. We review grounded theory methods and identify processes that can be enhanced through visual analytic techniques. Next, we develop an interface for qualitative text analysis, and evaluate our design with qualitative research practitioners who analyze texts with and without visual analytics support. The results of our study suggest how visual analytics can be incorporated into qualitative data analysis tools, and the analytic and interpretive benefits that can result.
Sclow Plots: Visualizing Empty Space
(The Eurographics Association and John Wiley & Sons Ltd., 2017) Giesen, Joachim; Kühne, Lars; Lucas, Philipp; Heer, Jeffrey and Ropinski, Timo and van Wijk, Jarke
Scatter plots are mostly used for correlation analysis, but are also a useful tool for understanding the distribution of highdimensional point cloud data. An important characteristic of such distributions are clusters, and scatter plots have been used successfully to identify clusters in data. Another characteristic of point cloud data that has received less attention so far are regions that contain no or only very few data points. We show that augmenting scatter plots by projections of flow lines along the gradient vector field of the distance function to the point cloud reveals such empty regions or voids. The augmented scatter plots, that we call sclow plots, enable a much better understanding of the geometry underlying the point cloud than traditional scatter plots, and by that support tasks like dimension inference, detecting outliers, or identifying data points at the interface between clusters. We demonstrate the feasibility of our approach on synthetic and real world data sets.
Graffinity: Visualizing Connectivity in Large Graphs
(The Eurographics Association and John Wiley & Sons Ltd., 2017) Kerzner, Ethan; Lex, Alexander; Sigulinsky, Crystal Lynn; Urness, Timothy; Jones, Bryan William; Marc, Robert E.; Meyer, Miriah; Heer, Jeffrey and Ropinski, Timo and van Wijk, Jarke
Multivariate graphs are prolific across many fields, including transportation and neuroscience. A key task in graph analysis is the exploration of connectivity, to, for example, analyze how signals flow through neurons, or to explore how well different cities are connected by flights. While standard node-link diagrams are helpful in judging connectivity, they do not scale to large networks. Adjacency matrices also do not scale to large networks and are only suitable to judge connectivity of adjacent nodes. A key approach to realize scalable graph visualization are queries: instead of displaying the whole network, only a relevant subset is shown. Query-based techniques for analyzing connectivity in graphs, however, can also easily suffer from cluttering if the query result is big enough. To remedy this, we introduce techniques that provide an overview of the connectivity and reveal details on demand.We have two main contributions: (1) two novel visualization techniques that work in concert for summarizing graph connectivity; and (2) Graffinity, an open-source implementation of these visualizations supplemented by detail views to enable a complete analysis workflow. Graffinity was designed in a close collaboration with neuroscientists and is optimized for connectomics data analysis, yet the technique is applicable across domains. We validate the connectivity overview and our open-source tool with illustrative examples using flight and connectomics data.
Reverse-Engineering Visualizations: Recovering Visual Encodings from Chart Images
(The Eurographics Association and John Wiley & Sons Ltd., 2017) Poco, Jorge; Heer, Jeffrey; Heer, Jeffrey and Ropinski, Timo and van Wijk, Jarke
We investigate how to automatically recover visual encodings from a chart image, primarily using inferred text elements. We contribute an end-to-end pipeline which takes a bitmap image as input and returns a visual encoding specification as output. We present a text analysis pipeline which detects text elements in a chart, classifies their role (e.g., chart title, x-axis label, y-axis title, etc.), and recovers the text content using optical character recognition. We also train a Convolutional Neural Network for mark type classification. Using the identified text elements and graphical mark type, we can then infer the encoding specification of an input chart image. We evaluate our techniques on three chart corpora: a set of automatically labeled charts generated using Vega, charts from the Quartz news website, and charts extracted from academic papers. We demonstrate accurate automatic inference of text elements, mark types, and chart specifications across a variety of input chart types.
Computing Contour Trees for 2D Piecewise Polynomial Functions
(The Eurographics Association and John Wiley & Sons Ltd., 2017) Nucha, Girijanandan; Bonneau, Georges-Pierre; Hahmann, Stefanie; Natarajan, Vijay; Heer, Jeffrey and Ropinski, Timo and van Wijk, Jarke
Contour trees are extensively used in scalar field analysis. The contour tree is a data structure that tracks the evolution of level set topology in a scalar field. Scalar fields are typically available as samples at vertices of a mesh and are linearly interpolated within each cell of the mesh. A more suitable way of representing scalar fields, especially when a smoother function needs to be modeled, is via higher order interpolants. We propose an algorithm to compute the contour tree for such functions. The algorithm computes a local structure by connecting critical points using a numerically stable monotone path tracing procedure. Such structures are computed for each cell and are stitched together to obtain the contour tree of the function. The algorithm is scalable to higher degree interpolants whereas previous methods were restricted to quadratic or linear interpolants. The algorithm is intrinsically parallelizable and has potential applications to isosurface extraction.

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