EuroVis17: Eurographics Conference on Visualization

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

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Visual Exploration of Global Trade Networks with Time-Dependent and Weighted Hierarchical Edge Bundles on GPU
(The Eurographics Association and John Wiley & Sons Ltd., 2017) Hofmann, Johannes; Größler, Michael; Rubio-Sánchez, Manuel; Pichler, Peter-Paul; Lehmann, Dirk J.; Heer, Jeffrey and Ropinski, Timo and van Wijk, Jarke
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.
Interactive Regression Lens for Exploring Scatter Plots
(The Eurographics Association and John Wiley & Sons Ltd., 2017) Shao, Lin; Mahajan, Aishwarya; Schreck, Tobias; Lehmann, Dirk J.; Heer, Jeffrey and Ropinski, Timo and van Wijk, Jarke
Data analysis often involves finding models that can explain patterns in data, and reduce possibly large data sets to more compact model-based representations. In Statistics, many methods are available to compute model information. Among others, regression models are widely used to explain data. However, regression analysis typically searches for the best model based on the global distribution of data. On the other hand, a data set may be partitioned into subsets, each requiring individual models. While automatic data subsetting methods exist, these often require parameters or domain knowledge to work with. We propose a system for visual-interactive regression analysis for scatter plot data, supporting both global and local regression modeling. We introduce a novel regression lens concept, allowing a user to interactively select a portion of data, on which regression analysis is run in interactive time. The lens gives encompassing visual feedback on the quality of candidate models as it is interactively navigated across the input data. While our regression lens can be used for fully interactive modeling, we also provide user guidance suggesting appropriate models and data subsets, by means of regression quality scores. We show, by means of use cases, that our regression lens is an effective tool for user-driven regression modeling and supports model understanding.
Constructing and Evaluating Visualisation Task Classifications: Process and Considerations
(The Eurographics Association and John Wiley & Sons Ltd., 2017) Kerracher, Natalie; Kennedy, Jessie; Heer, Jeffrey and Ropinski, Timo and van Wijk, Jarke
Categorising tasks is a common pursuit in the visualisation research community, with a wide variety of taxonomies, typologies, design spaces, and frameworks having been developed over the last three decades. While these classifications are universally purported to be useful in both the design and evaluation processes and in guiding future research, remarkably little attention has been paid to how these frameworks have-and can be-constructed and evaluated. In this paper we review the task classification literature and report on current practices in construction and evaluation. We consider the stages of task classification construction and identify the associated threats to validity arising at each stage and in response to the different methods employed. We provide guidance on suitable validation approaches in order to mitigate these threats. We also consider the appropriateness of evaluation strategies according to the different aspects of the classification which they evaluate. In so doing, we seek to provide guidance for developers of classifications in determining appropriate construction and evaluation strategies when developing a classification, and also for those selecting between competing classifications for use in the design and evaluation processes.
Minimum-Displacement Overlap Removal for Geo-referenced Data Visualization
(The Eurographics Association and John Wiley & Sons Ltd., 2017) Garderen, Mereke van; Pampel, Barbara; Nocaj, Arlind; Brandes, Ulrik; Heer, Jeffrey and Ropinski, Timo and van Wijk, Jarke
Given a set of rectangles embedded in the plane, we consider the problem of adjusting the layout to remove all overlap while preserving the orthogonal order of the rectangles. The objective is to minimize the displacement of the rectangles. We call this problem MINIMUM-DISPLACEMENT OVERLAP REMOVAL (MDOR). Our interest in this problem is motivated by the application of displaying metadata of archaeological sites. Because most existing overlap removal algorithms are not designed to minimize displacement while preserving orthogonal order, we present and compare several approaches which are tailored to our particular usecase. We introduce a new overlap removal heuristic which we call REARRANGE. Although conceptually simple, it is very effective in removing the overlap while keeping the displacement small. Furthermore, we propose an additional procedure to repair the orthogonal order after every iteration, with which we extend both our new heuristic and PRISM, a widely used overlap removal algorithm. We compare the performance of both approaches with and without this order repair method. The experimental results indicate that REARRANGE is very effective for heterogeneous input data where the overlap is concentrated in few dense regions.
Illustrative Visualization of Mesoscale Ocean Eddies
(The Eurographics Association and John Wiley & Sons Ltd., 2017) Liu, Li; Silver, Deborah; Bemis, Karen; Kang, Dujuan; Curchitser, Enrique; Heer, Jeffrey and Ropinski, Timo and van Wijk, Jarke
Feature-based time-varying volume visualization is combined with illustrative visualization to tell the story of how mesoscale ocean eddies form in the Gulf Stream and transport heat and nutrients across the ocean basin. The internal structure of these three-dimensional eddies and the kinematics with which they move are critical to a full understanding of ocean eddies. In this work, we apply a feature-based method to track instances of ocean eddies through the time steps of a high-resolution multidecadal regional ocean model and generate a series of eddy paths which reflect the life cycle of individual eddy instances. Based on the computed metadata, several important geometric and physical properties of eddy are computed. Illustrative visualization techniques, including visual effectiveness enhancement, focus+context, and smart visibility, are combined with the extracted volume features to explore eddy characteristics at different levels. An evaluation by domain experts indicates that combining our feature-based techniques with illustrative visualization techniques provides an insight into the role eddies play in ocean circulation. The domain experts expressed a preference for our methods over existing tools.
Empirically Measuring Soft Knowledge in Visualization
(The Eurographics Association and John Wiley & Sons Ltd., 2017) Kijmongkolchai, Natchaya; Abdul-Rahman, Alfie; Chen, Min; Heer, Jeffrey and Ropinski, Timo and van Wijk, Jarke
In this paper, we present an empirical study designed to evaluate the hypothesis that humans' soft knowledge can enhance the cost-benefit ratio of a visualization process by reducing the potential distortion. In particular, we focused on the impact of three classes of soft knowledge: (i) knowledge about application contexts, (ii) knowledge about the patterns to be observed (i.e., in relation to visualization task), and (iii) knowledge about statistical measures. We mapped these classes into three control variables, and used real-world time series data to construct stimuli. The results of the study confirmed the positive contribution of each class of knowledge towards the reduction of the potential distortion, while the knowledge about the patterns prevents distortion more effectively than the other two classes.
Interactive Ambiguity Resolution of Named Entities in Fictional Literature
(The Eurographics Association and John Wiley & Sons Ltd., 2017) Stoffel, Florian; Jentner, Wolfgang; Behrisch, Michael; Fuchs, Johannes; Keim, Daniel A.; Heer, Jeffrey and Ropinski, Timo and van Wijk, Jarke
Named entity recognition (NER) denotes the task to detect entities and their corresponding classes, such as person or location, in unstructured text data. For most applications, state of the art NER software is producing reasonable results. However, as a consequence of the methodological limitations and the well-known pitfalls when analyzing natural language data, the NER results are likely to contain ambiguities. In this paper, we present an interactive NER ambiguity resolution technique, which enables users to create (post-processing) rules for named entity recognition data based on the content and entity context of the analyzed documents. We specifically address the problem that in use-cases where ambiguities are problematic, such as the attribution of fictional characters with traits, it is often unfeasible to train models on custom data to improve state of the art NER software. We derive an iterative process model for improving NER results, show an interactive NER ambiguity resolution prototype, illustrate our approach with contemporary literature, and discuss our work and future research.
GraSp: Combining Spatially-aware Mobile Devices and a Display Wall for Graph Visualization and Interaction
(The Eurographics Association and John Wiley & Sons Ltd., 2017) Kister, Ulrike; Klamka, Konstantin; Tominski, Christian; Dachselt, Raimund; Heer, Jeffrey and Ropinski, Timo and van Wijk, Jarke
Going beyond established desktop interfaces, researchers have begun re-thinking visualization approaches to make use of alternative display environments and more natural interaction modalities. In this paper, we investigate how spatially-aware mobile displays and a large display wall can be coupled to support graph visualization and interaction. For that purpose, we distribute typical visualization views of classic node-link and matrix representations between displays. The focus of our work lies in novel interaction techniques that enable users to work with personal mobile devices in combination with the wall. We devised and implemented a comprehensive interaction repertoire that supports basic and advanced graph exploration and manipulation tasks, including selection, details-on-demand, focus transitions, interactive lenses, and data editing. A qualitative study has been conducted to identify strengths and weaknesses of our techniques. Feedback showed that combining mobile devices and a wall-sized display is useful for diverse graph-related tasks. We also gained valuable insights regarding the distribution of visualization views and interactive tools among the combined displays.
Global Feature Tracking and Similarity Estimation in Time-Dependent Scalar Fields
(The Eurographics Association and John Wiley & Sons Ltd., 2017) Saikia, Himangshu; Weinkauf, Tino; Heer, Jeffrey and Ropinski, Timo and van Wijk, Jarke
We present an algorithm for tracking regions in time-dependent scalar fields that uses global knowledge from all time steps for determining the tracks. The regions are defined using merge trees, thereby representing a hierarchical segmentation of the data in each time step. The similarity of regions of two consecutive time steps is measured using their volumetric overlap and a histogram difference. The main ingredient of our method is a directed acyclic graph that records all relevant similarity information as follows: the regions of all time steps are the nodes of the graph, the edges represent possible short feature tracks between consecutive time steps, and the edge weights are given by the similarity of the connected regions. We compute a feature track as the global solution of a shortest path problem in the graph. We use these results to steer the - to the best of our knowledge - first algorithm for spatio-temporal feature similarity estimation. Our algorithm works for 2D and 3D time-dependent scalar fields. We compare our results to previous work, showcase its robustness to noise, and exemplify its utility using several real-world data sets.
Visual Narrative Flow: Exploring Factors Shaping Data Visualization Story Reading Experiences
(The Eurographics Association and John Wiley & Sons Ltd., 2017) McKenna, Sean; Riche, Nathalie Henry; Lee, Bongshin; Boy, Jeremy; Meyer, Miriah; Heer, Jeffrey and Ropinski, Timo and van Wijk, Jarke
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.
Visualizing a Sequence of a Thousand Graphs (or Even More)
(The Eurographics Association and John Wiley & Sons Ltd., 2017) Burch, Michael; Hlawatsch, Marcel; Weiskopf, Daniel; Heer, Jeffrey and Ropinski, Timo and van Wijk, Jarke
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 timevarying 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.
Dynamic Scene Graph: Enabling Scaling, Positioning, and Navigation in the Universe
(The Eurographics Association and John Wiley & Sons Ltd., 2017) Axelsson, Emil; Costa, Jonathas; Silva, Cláudio; Emmart, Carter; Bock, Alexander; Ynnerman, Anders; Heer, Jeffrey and Ropinski, Timo and van Wijk, Jarke
In this work, we address the challenge of seamlessly visualizing astronomical data exhibiting huge scale differences in distance, size, and resolution. One of the difficulties is accurate, fast, and dynamic positioning and navigation to enable scaling over orders of magnitude, far beyond the precision of floating point arithmetic. To this end we propose a method that utilizes a dynamically assigned frame of reference to provide the highest possible numerical precision for all salient objects in a scene graph. This makes it possible to smoothly navigate and interactively render, for example, surface structures on Mars and the MilkyWay simultaneously. Our work is based on an analysis of tracking and quantification of the propagation of precision errors through the computer graphics pipeline using interval arithmetic. Furthermore, we identify sources of precision degradation, leading to incorrect object positions in screen-space and z-fighting. Our proposed method operates without near and far planes while maintaining high depth precision through the use of floating point depth buffers. By providing interoperability with order-independent transparency algorithms, direct volume rendering, and stereoscopy, our approach is well suited for scientific visualization. We provide the mathematical background, a thorough description of the method, and a reference implementation.
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.

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