EuroVis21: Eurographics Conference on Visualization

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https://diglib.eg.org/handle/10.2312/3607293

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Visual Analysis of Electronic Densities and Transitions in Molecules
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Masood, Talha Bin; Thygesen, Signe Sidwall; Linares, Mathieu; Abrikosov, Alexei I.; Natarajan, Vijay; Hotz, Ingrid; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
The study of electronic transitions within a molecule connected to the absorption or emission of light is a common task in the process of the design of new materials. The transitions are complex quantum mechanical processes and a detailed analysis requires a breakdown of these processes into components that can be interpreted via characteristic chemical properties. We approach these tasks by providing a detailed analysis of the electron density field. This entails methods to quantify and visualize electron localization and transfer from molecular subgroups combining spatial and abstract representations. The core of our method uses geometric segmentation of the electronic density field coupled with a graph-theoretic formulation of charge transfer between molecular subgroups. The design of the methods has been guided by the goal of providing a generic and objective analysis following fundamental concepts. We illustrate the proposed approach using several case studies involving the study of electronic transitions in different molecular systems.
Public Data Visualization: Analyzing Local Running Statistics on Situated Displays
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Coenen, Jorgos; Moere, Andrew Vande; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
Popular sports tracking applications allow athletes to share and compare their personal performance data with others. Visualizing this data in relevant public settings can be beneficial in provoking novel types of opportunistic and communal sense-making. We investigated this premise by situating an analytical visualization of running performances on two touch-enabled public displays in proximity to a local community running trail. Using a rich mixed-method evaluation protocol during a three-week-long in-the-wild deployment, we captured its social and analytical impact across 235 distinct interaction sessions. Our results show how our public analytical visualization supported passers-by to create novel insights that were rather of casual nature. Several textual features that surrounded the visualization, such as titles that were framed as provocative hypotheses and predefined attention-grabbing data queries, sparked interest and social debate, while a narrative tutorial facilitated more analytical interaction patterns. Our detailed mixed-methods evaluation approach led to a set of actionable takeaways for public visualizations that allow novice audiences to engage with data analytical insights that have local relevance.
ClusterSets: Optimizing Planar Clusters in Categorical Point Data
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Geiger, Jakob; Cornelsen, Sabine; Haunert, Jan-Henrik; Kindermann, Philipp; Mchedlidze, Tamara; Nöllenburg, Martin; Okamoto, Yoshio; Wolff, Alexander; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
In geographic data analysis, one is often given point data of different categories (such as facilities of a university categorized by department). Drawing upon recent research on set visualization, we want to visualize category membership by connecting points of the same category with visual links. Existing approaches that follow this path usually insist on connecting all members of a category, which may lead to many crossings and visual clutter. We propose an approach that avoids crossings between connections of different categories completely. Instead of connecting all data points of the same category, we subdivide categories into smaller, local clusters where needed. We do a case study comparing the legibility of drawings produced by our approach and those by existing approaches. In our problem formulation, we are additionally given a graph G on the data points whose edges express some sort of proximity. Our aim is to find a subgraph G0 of G with the following properties: (i) edges connect only data points of the same category, (ii) no two edges cross, and (iii) the number of connected components (clusters) is minimized. We then visualize the clusters in G0. For arbitrary graphs, the resulting optimization problem, Cluster Minimization, is NP-hard (even to approximate). Therefore, we introduce two heuristics. We do an extensive benchmark test on real-world data. Comparisons with exact solutions indicate that our heuristics do astonishing well for certain relative-neighborhood graphs.
Guided Stable Dynamic Projections
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Vernier, Eduardo Faccin; Comba, João L. D.; Telea, Alexandru C.; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
Projections aim to convey the relationships and similarity of high-dimensional data in a low-dimensional representation. Most such techniques are designed for static data. When used for time-dependent data, they usually fail to create a stable and suitable low dimensional representation. We propose two dynamic projection methods (PCD-tSNE and LD-tSNE) that use global guides to steer projection points. This avoids unstable movement that does not encode data dynamics while keeping t-SNE's neighborhood preservation ability. PCD-tSNE scores a good balance between stability, neighborhood preservation, and distance preservation, while LD-tSNE allows creating stable and customizable projections. We compare our methods to 11 other techniques using quality metrics and datasets provided by a recent benchmark for dynamic projections.
Texture Browser: Feature-based Texture Exploration
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Luo, Xuejiao; Scandolo, Leonardo; Eisemann, Elmar; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
Texture is a key characteristic in the definition of the physical appearance of an object and a crucial element in the creation process of 3D artists. However, retrieving a texture that matches an intended look from an image collection is difficult. Contrary to most photo collections, for which object recognition has proven quite useful, syntactic descriptions of texture characteristics is not straightforward, and even creating appropriate metadata is a very difficult task. In this paper, we propose a system to help explore large unlabeled collections of texture images. The key insight is that spatially grouping textures sharing similar features can simplify navigation. Our system uses a pre-trained convolutional neural network to extract high-level semantic image features, which are then mapped to a 2-dimensional location using an adaptation of t-SNE, a dimensionality-reduction technique. We describe an interface to visualize and explore the resulting distribution and provide a series of enhanced navigation tools, our prioritized t-SNE, scalable clustering, and multi-resolution embedding, to further facilitate exploration and retrieval tasks. Finally, we also present the results of a user evaluation that demonstrates the effectiveness of our solution.
Visual Analysis of Spatio-temporal Phenomena with 1D Projections
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Franke, Max; Martin, Henry; Koch, Steffen; Kurzhals, Kuno; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
It is crucial to visually extrapolate the characteristics of their evolution to understand critical spatio-temporal events such as earthquakes, fires, or the spreading of a disease. Animations embedded in the spatial context can be helpful for understanding details, but have proven to be less effective for overview and comparison tasks. We present an interactive approach for the exploration of spatio-temporal data, based on a set of neighborhood-preserving 1D projections which help identify patterns and support the comparison of numerous time steps and multivariate data. An important objective of the proposed approach is the visual description of local neighborhoods in the 1D projection to reveal patterns of similarity and propagation. As this locality cannot generally be guaranteed, we provide a selection of different projection techniques, as well as a hierarchical approach, to support the analysis of different data characteristics. In addition, we offer an interactive exploration technique to reorganize and improve the mapping locally to users' foci of interest. We demonstrate the usefulness of our approach with different real-world application scenarios and discuss the feedback we received from domain and visualization experts.
VICE: Visual Identification and Correction of Neural Circuit Errors
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Gonda, Felix; Wang, Xueying; Beyer, Johanna; Hadwiger, Markus; Lichtman, Jeff W.; Pfister, Hanspeter; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
A connectivity graph of neurons at the resolution of single synapses provides scientists with a tool for understanding the nervous system in health and disease. Recent advances in automatic image segmentation and synapse prediction in electron microscopy (EM) datasets of the brain have made reconstructions of neurons possible at the nanometer scale. However, automatic segmentation sometimes struggles to segment large neurons correctly, requiring human effort to proofread its output. General proofreading involves inspecting large volumes to correct segmentation errors at the pixel level, a visually intensive and time-consuming process. This paper presents the design and implementation of an analytics framework that streamlines proofreading, focusing on connectivity-related errors. We accomplish this with automated likely-error detection and synapse clustering that drives the proofreading effort with highly interactive 3D visualizations. In particular, our strategy centers on proofreading the local circuit of a single cell to ensure a basic level of completeness. We demonstrate our framework's utility with a user study and report quantitative and subjective feedback from our users. Overall, users find the framework more efficient for proofreading, understanding evolving graphs, and sharing error correction strategies.
iQUANT: Interactive Quantitative Investment Using Sparse Regression Factors
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Yue, Xuanwu; Gu, Qiao; Wang, Deyun; Qu, Huamin; Wang, Yong; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
The model-based investing using financial factors is evolving as a principal method for quantitative investment. The main challenge lies in the selection of effective factors towards excess market returns. Existing approaches, either hand-picking factors or applying feature selection algorithms, do not orchestrate both human knowledge and computational power. This paper presents iQUANT, an interactive quantitative investment system that assists equity traders to quickly spot promising financial factors from initial recommendations suggested by algorithmic models, and conduct a joint refinement of factors and stocks for investment portfolio composition. We work closely with professional traders to assemble empirical characteristics of ''good'' factors and propose effective visualization designs to illustrate the collective performance of financial factors, stock portfolios, and their interactions. We evaluate iQUANT through a formal user study, two case studies, and expert interviews, using a real stock market dataset consisting of 3000 stocks x 6000 days x 56 factors.
Accessible Visualization: Design Space, Opportunities, and Challenges
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Kim, Nam Wook; Joyner, Shakila Cherise; Riegelhuth, Amalia; Kim, Yea-Seul; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
Visualizations are now widely used across disciplines to understand and communicate data. The benefit of visualizations lies in leveraging our natural visual perception. However, the sole dependency on vision can produce unintended discrimination against people with visual impairments. While the visualization field has seen enormous growth in recent years, supporting people with disabilities is much less explored. In this work, we examine approaches to support this marginalized user group, focusing on visual disabilities. We collected and analyzed papers published for the last 20 years on visualization accessibility. We mapped a design space for accessible visualization that includes seven dimensions: user group, literacy task, chart type, interaction, information granularity, sensory modality, assistive technology. We described the current knowledge gap in light of the latest advances in visualization and presented a preliminary accessibility model by synthesizing findings from existing research. Finally, we reflected on the dimensions and discussed opportunities and challenges for future research.
VEHICLE: Validation and Exploration of the Hierarchical Integration of Conflict Event Data
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Mayer, Benedikt; Lawonn, Kai; Donnay, Karsten; Preim, Bernhard; Meuschke, Monique; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
The exploration of large-scale conflicts, as well as their causes and effects, is an important aspect of socio-political analysis. Since event data related to major conflicts are usually obtained from different sources, researchers developed a semi-automatic matching algorithm to integrate event data of different origins into one comprehensive dataset using hierarchical taxonomies. The validity of the corresponding integration results is not easy to assess since the results depend on user-defined input parameters and the relationships between the original data sources. However, only rudimentary visualization techniques have been used so far to analyze the results, allowing no trustworthy validation or exploration of how the final dataset is composed. To overcome this problem, we developed VEHICLE, a web-based tool to validate and explore the results of the hierarchical integration. For the design, we collaborated with a domain expert to identify the underlying domain problems and derive a task and workflow description. The tool combines both traditional and novel visual analysis techniques, employing statistical and map-based depictions as well as advanced interaction techniques. We showed the usefulness of VEHICLE in two case studies and by conducting an evaluation together with conflict researchers, confirming domain hypotheses and generating new insights.
VisEvol: Visual Analytics to Support Hyperparameter Search through Evolutionary Optimization
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Chatzimparmpas, Angelos; Martins, Rafael M.; Kucher, Kostiantyn; Kerren, Andreas; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
During the training phase of machine learning (ML) models, it is usually necessary to configure several hyperparameters. This process is computationally intensive and requires an extensive search to infer the best hyperparameter set for the given problem. The challenge is exacerbated by the fact that most ML models are complex internally, and training involves trial-and-error processes that could remarkably affect the predictive result. Moreover, each hyperparameter of an ML algorithm is potentially intertwined with the others, and changing it might result in unforeseeable impacts on the remaining hyperparameters. Evolutionary optimization is a promising method to try and address those issues. According to this method, performant models are stored, while the remainder are improved through crossover and mutation processes inspired by genetic algorithms. We present VisEvol, a visual analytics tool that supports interactive exploration of hyperparameters and intervention in this evolutionary procedure. In summary, our proposed tool helps the user to generate new models through evolution and eventually explore powerful hyperparameter combinations in diverse regions of the extensive hyperparameter space. The outcome is a voting ensemble (with equal rights) that boosts the final predictive performance. The utility and applicability of VisEvol are demonstrated with two use cases and interviews with ML experts who evaluated the effectiveness of the tool.
Learning Contextualized User Preferences for Co-Adaptive Guidance in Mixed-Initiative Topic Model Refinement
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Sperrle, Fabian; Schäfer, Hanna; Keim, Daniel; El-Assady, Mennatallah; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
Mixed-initiative visual analytics systems support collaborative human-machine decision-making processes. However, many multiobjective optimization tasks, such as topic model refinement, are highly subjective and context-dependent. Hence, systems need to adapt their optimization suggestions throughout the interactive refinement process to provide efficient guidance. To tackle this challenge, we present a technique for learning context-dependent user preferences and demonstrate its applicability to topic model refinement. We deploy agents with distinct associated optimization strategies that compete for the user's acceptance of their suggestions. To decide when to provide guidance, each agent maintains an intelligible, rule-based classifier over context vectorizations that captures the development of quality metrics between distinct analysis states. By observing implicit and explicit user feedback, agents learn in which contexts to provide their specific guidance operation. An agent in topic model refinement might, for example, learn to react to declining model coherence by suggesting to split a topic. Our results confirm that the rules learned by agents capture contextual user preferences. Further, we show that the learned rules are transferable between similar datasets, avoiding common cold-start problems and enabling a continuous refinement of agents across corpora.
Daisen: A Framework for Visualizing Detailed GPU Execution
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Sun, Yifan; Zhang, Yixuan; Mosallaei, Ali; Shah, Michael D.; Dunne, Cody; Kaeli, David; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
Graphics Processing Units (GPUs) have been widely used to accelerate artificial intelligence, physics simulation, medical imaging, and information visualization applications. To improve GPU performance, GPU hardware designers need to identify performance issues by inspecting a huge amount of simulator-generated traces. Visualizing the execution traces can reduce the cognitive burden of users and facilitate making sense of behaviors of GPU hardware components. In this paper, we first formalize the process of GPU performance analysis and characterize the design requirements of visualizing execution traces based on a survey study and interviews with GPU hardware designers. We contribute data and task abstraction for GPU performance analysis. Based on our task analysis, we propose Daisen, a framework that supports data collection from GPU simulators and provides visualization of the simulator-generated GPU execution traces. Daisen features a data abstraction and trace format that can record simulator-generated GPU execution traces. Daisen also includes a web-based visualization tool that helps GPU hardware designers examine GPU execution traces, identify performance bottlenecks, and verify performance improvement. Our qualitative evaluation with GPU hardware designers demonstrates that the design of Daisen reflects the typical workflow of GPU hardware designers. Using Daisen, participants were able to effectively identify potential performance bottlenecks and opportunities for performance improvement. The open-sourced implementation of Daisen can be found at gitlab.com/akita/vis. Supplemental materials including a demo video, survey questions, evaluation study guide, and post-study evaluation survey are available at osf.io/j5ghq.
Local Extraction of 3D Time-Dependent Vector Field Topology
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Hofmann, Lutz; Sadlo, Filip; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
We present an approach to local extraction of 3D time-dependent vector field topology. In this concept, Lagrangian coherent structures, which represent the separating manifolds in time-dependent transport, correspond to generalized streak manifolds seeded along hyperbolic path surfaces (HPSs). Instead of expensive and numerically challenging direct computation of the HPSs by intersection of ridges in the forward and backward finite-time Lyapunov exponent (FTLE) fields, our approach employs local extraction of respective candidates in the four-dimensional space-time domain. These candidates are subsequently refined toward the hyperbolic path surfaces, which provides unsteady equivalents of saddle-type critical points, periodic orbits, and bifurcation lines from steady, traditional vector field topology. In contrast to FTLE-based methods, we obtain an explicit geometric representation of the topological skeleton of the flow, which for steady flows coincides with the hyperbolic invariant manifolds of vector field topology. We evaluate our approach on analytical flows, as well as data from computational fluid dynamics, using the FTLE as a ground truth superset, i.e., we also show that FTLE ridges exhibit several types of false positives.
Automatic Improvement of Continuous Colormaps in Euclidean Colorspaces
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Nardini, Pascal; Chen, Min; Böttinger, Michael; Scheuermann, Gerik; Bujack, Roxana; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
Colormapping is one of the simplest and most widely used data visualization methods within and outside the visualization community. Uniformity, order, discriminative power, and smoothness of continuous colormaps are the most important criteria for evaluating and potentially improving colormaps. We present a local and a global automatic optimization algorithm in Euclidean color spaces for each of these design rules in this work. As a foundation for our optimization algorithms, we used the CCC-Tool colormap specification (CMS); each algorithm has been implemented in this tool. In addition to synthetic examples that demonstrate each method's effect, we show the outcome of some of the methods applied to a typhoon simulation.
TourVis: Narrative Visualization of Multi-Stage Bicycle Races
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Díaz, Jose; Fort, Marta; Vázquez, Pere-Pau; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
There are many multiple-stage racing competitions in various sports such as swimming, running, or cycling. The wide availability of affordable tracking devices facilitates monitoring the position along with the race of all participants, even for non-professional contests. Getting real-time information of contenders is useful but also unleashes the possibility of creating more complex visualization systems that ease the understanding of the behavior of all participants during a simple stage or throughout the whole competition. In this paper we focus on bicycle races, which are highly popular, especially in Europe, being the Tour de France its greatest exponent. Current visualizations from TV broadcasting or real-time tracking websites are useful to understand the current stage status, up to a certain extent. Unfortunately, still no current system exists that visualizes a whole multi-stage contest in such a way that users can interactively explore the relevant events of a single stage (e.g. breakaways, groups, virtual leadership: : :), as well as the full competition. In this paper, we present an interactive system that is useful both for aficionados and professionals to visually analyze the development of multi-stage cycling competitions.
Hornero: Thunderstorms Characterization using Visual Analytics
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Diehl, Alexandra; Pelorosso, Rodrigo; Ruiz, Juan; Pajarola, Renato; Gröller, M. Eduard; Bruckner, Stefan; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
Analyzing the evolution of thunderstorms is critical in determining the potential for the development of severe weather events. Existing visualization systems for short-term weather forecasting (nowcasting) allow for basic analysis and prediction of storm developments. However, they lack advanced visual features for efficient decision-making. We developed a visual analytics tool for the detection of hazardous thunderstorms and their characterization, using a visual design centered on a reformulated expert task workflow that includes visual features to overview storms and quickly identify high-impact weather events, a novel storm graph visualization to inspect and analyze the storm structure, as well as a set of interactive views for efficient identification of similar storm cells (known as analogs) in historical data and their use for nowcasting. Our tool was designed with and evaluated by meteorologists and expert forecasters working in short-term operational weather forecasting of severe weather events. Results show that our solution suits the forecasters' workflow. Our visual design is expressive, easy to use, and effective for prompt analysis and quick decision-making in the context of short-range operational weather forecasting.
AutoClips: An Automatic Approach to Video Generation from Data Facts
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Shi, Danqing; Sun, Fuling; Xu, Xinyue; Lan, Xingyu; Gotz, David; Cao, Nan; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
Data videos, a storytelling genre that visualizes data facts with motion graphics, are gaining increasing popularity among data journalists, non-profits, and marketers to communicate data to broad audiences. However, crafting a data video is often timeconsuming and asks for various domain knowledge such as data visualization, animation design, and screenwriting. Existing authoring tools usually enable users to edit and compose a set of templates manually, which still cost a lot of human effort. To further lower the barrier of creating data videos, this work introduces a new approach, AutoClips, which can automatically generate data videos given the input of a sequence of data facts. We built AutoClips through two stages. First, we constructed a fact-driven clip library where we mapped ten data facts to potential animated visualizations respectively by analyzing 230 online data videos and conducting interviews. Next, we constructed an algorithm that generates data videos from data facts through three steps: selecting and identifying the optimal clip for each of the data facts, arranging the clips into a coherent video, and optimizing the duration of the video. The results from two user studies indicated that the data videos generated by AutoClips are comprehensible, engaging, and have comparable quality with human-made videos.
Optimal Axes for Data Value Estimation in Star Coordinates and Radial Axes Plots
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Rubio-Sánchez, Manuel; Lehmann, Dirk J.; Sanchez, Alberto; Rojo-Álvarez, Jose Luis; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
Radial axes plots are projection methods that represent high-dimensional data samples as points on a two-dimensional plane. These techniques define mappings through a set of axis vectors, each associated with a data variable, which users can manipulate interactively to create different plots and analyze data from multiple points of view. However, updating the direction and length of an axis vector is far from trivial. Users must consider the data analysis task, domain knowledge, the directions in which values should increase, the relative importance of each variable, or the correlations between variables, among other factors. Another issue is the difficulty to approximate high-dimensional data values in the two-dimensional visualizations, which can hamper searching for data with particular characteristics, analyzing the most common data values in clusters, inspecting outliers, etc. In this paper we present and analyze several optimization approaches for enhancing radial axes plots regarding their ability to represent high-dimensional data values. The techniques can be used not only to approximate data values with greater accuracy, but also to guide users when updating axis vectors or extending visualizations with new variables, since they can reveal poor choices of axis vectors. The optimal axes can also be included in nonlinear plots. In particular, we show how they can be used within RadViz to assess the quality of a variable ordering. The in-depth analysis carried out is useful for visualization designers developing radial axes techniques, or planning to incorporate axes into other visualization methods.
Design Space of Origin-Destination Data Visualization
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Tennekes, Martijn; Chen, Min; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
Visualization is an essential tool for observing and analyzing origin-destination (OD) data, which encodes flows between geographic locations, e.g., in applications concerning commuting, migration, and transport of goods. However, depicting OD data often encounter issues of cluttering and occlusion. To address these issues, many visual designs feature data abstraction and visual abstraction, such as node aggregation and edge bundling, resulting in information loss. The recent theoretical and empirical developments in visualization have substantiated the merits of such abstraction, while confirming that viewers' knowledge can alleviate the negative impact due to information loss. It is thus desirable to map out different ways of losing and adding information in origin-destination data visualization (ODDV).We therefore formulate a new design space of ODDV based on the categorization of informative operations on OD data in data abstraction and visual abstraction. We apply this design space to existing ODDV methods, outline strategies for exploring the design space, and suggest ideas for further exploration.

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