DevLan/vispubs huggingface dataset:
Line Weaver: Importance-Driven Order Enhanced Rendering of Dense Line Charts
Authors: Thomas Trautner, Stefan Bruckner
Published at EuroVis, 2021

Line charts are an effective and widely used technique for visualizing series of ordered two‐dimensional data points. The relationship between consecutive points is indicated by connecting line segments, revealing potential trends or clusters in the underlying data. However, when dealing with an increasing number of lines, the render order substantially influences the resulting visualization. Rendering transparent lines can help but unfortunately the blending order is currently either ignored or naively used, for example, assuming it is implicitly given by the order in which the data was saved in a file. Due to the non‐commutativity of classic alpha blending, this results in contradicting visualizations of the same underlying data set, so‐called “hallucinators”. In this paper, we therefore present line weaver, a novel visualization technique for dense line charts. Using an importance function, we developed an approach that correctly considers the blending order independently of the render order and without any prior sorting of the data. We allow for importance functions which are either explicitly given or implicitly derived from the geometric properties of the data if no external data is available. The importance can then be applied globally to entire lines, or locally per pixel which simultaneously supports various types of user interaction. Finally, we discuss the potential of our contribution based on different synthetic and real‐world data sets where classic or naive approaches would fail.
Local Extraction of 3D Time-Dependent Vector Field Topology
Authors: Lutz Hofmann, Filip Sadlo
Published at EuroVis, 2021

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.
Optimal Axes for Data Value Estimation in Star Coordinates and Radial Axes Plots
Authors: Manuel Rubio-Sánchez, Dirk J. Lehmann, Alberto Sánchez 0001, José Luis Rojo-Álvarez
Published at EuroVis, 2021

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.
Parameterized Splitting of Summed Volume Tables
Authors: Christian Reinbold, Rüdiger Westermann
Published at EuroVis, 2021

Summed Volume Tables (SVTs) allow one to compute integrals over the data values in any cubical area of a three‐dimensional orthogonal grid in constant time, and they are especially interesting for building spatial search structures for sparse volumes. However, SVTs become extremely memory consuming due to the large values they need to store; for a dataset of n values an SVT requires 𝒪(n log n) bits. The 3D Fenwick tree allows recovering the integral values in 𝒪(log3 n) time, at a memory consumption of 𝒪(n) bits. We propose an algorithm that generates SVT representations that can flexibly trade speed for memory: From similar characteristics as SVTs, over equal memory consumption as 3D Fenwick trees at significantly lower computational complexity, to even further reduced memory consumption at the cost of raising computational complexity. For a 641 × 9601 × 9601 binary dataset, the algorithm can generate an SVT representation that requires 27.0GB and 46 · 8 data fetch operations to retrieve an integral value, compared to 27.5GB and 1521·8 fetches by 3D Fenwick trees, a decrease in fetches of 97%. A full SVT requires 247.6GB and 8 fetches per integral value. We present a novel hierarchical approach to compute and store intermediate prefix sums of SVTs, so that any prescribed memory consumption between 𝒪(n) bits and 𝒪(n log n) bits is achieved. We evaluate the performance of the proposed algorithm in a number of examples considering large volume data, and we perform comparisons to existing alternatives.
ParSetgnostics: Quality Metrics for Parallel Sets
Authors: Frederik L. Dennig, Maximilian T. Fischer, Michael Blumenschein, Johannes Fuchs 0001, Daniel A. Keim, Evanthia Dimara
Published at EuroVis, 2021

While there are many visualization techniques for exploring numeric data, only a few work with categorical data. One prominent example is Parallel Sets, showing data frequencies instead of data points ‐ analogous to parallel coordinates for numerical data. As nominal data does not have an intrinsic order, the design of Parallel Sets is sensitive to visual clutter due to overlaps, crossings, and subdivision of ribbons hindering readability and pattern detection. In this paper, we propose a set of quality metrics, called ParSetgnostics (Parallel Sets diagnostics), which aim to improve Parallel Sets by reducing clutter. These quality metrics quantify important properties of Parallel Sets such as overlap, orthogonality, ribbon width variance, and mutual information to optimize the category and dimension ordering. By conducting a systematic correlation analysis between the individual metrics, we ensure their distinctiveness. Further, we evaluate the clutter reduction effect of ParSetgnostics by reconstructing six datasets from previous publications using Parallel Sets measuring and comparing their respective properties. Our results show that ParSetgostics facilitates multi‐dimensional analysis of categorical data by automatically providing optimized Parallel Set designs with a clutter reduction of up to 81% compared to the originally proposed Parallel Sets visualizations.
ProBGP: Progressive Visual Analytics of Live BGP Updates
Authors: Alex Ulmer, David Sessler, Jörn Kohlhammer
Published at EuroVis, 2021

The global routing network is the backbone of the Internet. However, it is quite vulnerable to attacks that cause major disruptions or routing manipulations. Prior related works have visualized routing path changes with node link diagrams, but it requires strong domain expertise to understand if a routing change between autonomous systems is suspicious. Geographic visualization has an advantage over conventional node‐link diagrams by helping uncover such suspicious routes as the user can immediately see if a path is the shortest path to the target or an unreasonable detour. In this paper, we present ProBGP, a web‐based progressive approach to visually analyze BGP update routes. We created a novel progressive data processing algorithm for the geographic approximation of autonomous systems and combined it with a progressively updating visualization. While the newest log data is continuously loaded, our approach also allows querying the entire log recordings since 1999. We present the usefulness of our approach with a real use case of a major route leak from June 2019. We report on multiple interviews with domain experts throughout the development. Finally, we evaluated our algorithm quantitatively against a public peering database and qualitatively against AS network maps.
Public Data Visualization: Analyzing Local Running Statistics on Situated Displays
Authors: Jorgos Coenen, Andrew Vande Moere
Published at EuroVis, 2021

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.
Scalar Field Comparison with Topological Descriptors: Properties and Applications for Scientific Visualization
Authors: Lin Yan, Talha Bin Masood, Raghavendra Sridharamurthy, Farhan Rasheed, Vijay Natarajan, Ingrid Hotz, Bei Wang 0001
Published at EuroVis, 2021

In topological data analysis and visualization, topological descriptors such as persistence diagrams, merge trees, contour trees, Reeb graphs, and Morse–Smale complexes play an essential role in capturing the shape of scalar field data. We present a state‐of‐the‐art report on scalar field comparison using topological descriptors. We provide a taxonomy of existing approaches based on visualization tasks associated with three categories of data: single fields, time‐varying fields, and ensembles. These tasks include symmetry detection, periodicity detection, key event/feature detection, feature tracking, clustering, and structure statistics. Our main contributions include the formulation of a set of desirable mathematical and computational properties of comparative measures, and the classification of visualization tasks and applications that are enabled by these measures.
SenVis: Interactive Tensor-based Sensitivity Visualization
Authors: Haiyan Yang, Rafael Ballester-Ripoll, Renato Pajarola
Published at EuroVis, 2021

Sobol's method is one of the most powerful and widely used frameworks for global sensitivity analysis, and it maps every possible combination of input variables to an associated Sobol index. However, these indices are often challenging to analyze in depth, due in part to the lack of suitable, flexible enough, and fast‐to‐query data access structures as well as visualization techniques. We propose a visualization tool that leverages tensor decomposition, a compressed data format that can quickly and approximately answer sophisticated queries over exponential‐sized sets of Sobol indices. This way, we are able to capture the complete global sensitivity information of high‐dimensional scalar models. Our application is based on a three‐stage visualization, to which variables to be analyzed can be added or removed interactively. It includes a novel hourglass‐like diagram presenting the relative importance for any single variable or combination of input variables with respect to any composition of the rest of the input variables. We showcase our visualization with a range of example models, whereby we demonstrate the high expressive power and analytical capability made possible with the proposed method.
SumRe: Design and Evaluation of a Gist-based Summary Visualization for Incident Reports Triage
Authors: Tabassum Kakar, Xiao Qin 0003, Thang La, Sanjay K. Sahoo, Suranjan De, Elke A. Rundensteiner, Lane Harrison
Published at EuroVis, 2021

Incident report triage is a common endeavor in many industry sectors, often coupled with serious public safety implications. For example, at the US Food and Drug Administration (FDA), analysts triage an influx of incident reports to identify previously undiscovered drug safety problems. However, these analysts currently conduct this critical yet error‐prone incident report triage using a generic table‐based interface, with no formal support. Visualization design, task‐characterization methodologies, and evaluation models offer several possibilities for better supporting triage workflows, including those dealing with drug safety and beyond. In this work, we aim to elevate the work of triage through a task‐abstraction activity with FDA analysts. Second, we design an alternative gist‐based summary of text documents used in triage (SumRe). Third, we conduct a crowdsourced evaluation of SumRe with medical experts. Results of the crowdsourced study with medical experts (n = 20) suggest that SumRe better supports accuracy in understanding the gist of a given report, and in identifying important reports for followup activities. We discuss implications of these results, including design considerations for triage workflows beyond the drug domain, as well as methodologies for comparing visualization‐enabled text summaries.
Texture Browser: Feature-based Texture Exploration
Authors: Xuejiao Luo, Leonardo Scandolo, Elmar Eisemann
Published at EuroVis, 2021

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.
Thin-Volume Visualization on Curved Domains
Authors: Felix Herter, Hans-Christian Hege, Markus Hadwiger, Verena Lepper, Daniel Baum
Published at EuroVis, 2021

Thin, curved structures occur in many volumetric datasets. Their analysis using classical volume rendering is difficult because parts of such structures can bend away or hide behind occluding elements. This problem cannot be fully compensated by effective navigation alone, as structure‐adapted navigation in the volume is cumbersome and only parts of the structure are visible in each view. We solve this problem by rendering a spatially transformed view of the volume so that an unobstructed visualization of the entire curved structure is obtained. As a result, simple and intuitive navigation becomes possible. The domain of the spatial transform is defined by a triangle mesh that is topologically equivalent to an open disc and that approximates the structure of interest. The rendering is based on ray‐casting, in which the rays traverse the original volume. In order to carve out volumes of varying thicknesses, the lengths of the rays as well as the positions of the mesh vertices can be easily modified by interactive painting under view control.We describe a prototypical implementation and demonstrate the interactive visual inspection of complex structures from digital humanities, biology, medicine, and material sciences. The visual representation of the structure as a whole allows for easy inspection of interesting substructures in their original spatial context. Overall, we show that thin, curved structures in volumetric data can be excellently visualized using ray‐casting‐based volume rendering of transformed views defined by guiding surface meshes, supplemented by interactive, local modifications of ray lengths and vertex positions.
Topography of Violence: Considerations for Ethical and Collaborative Visualization Design
Authors: Fabian Ehmel, Viktoria Brüggemann, Marian Dörk
Published at EuroVis, 2021

Based on a collaborative visualization design process involving sensitive historical data and historiographical expertise, we investigate the relevance of ethical principles in visualization design. While fundamental ethical norms like truthfulness and accuracy are already well‐described and common goals in visualization design, datasets that are accompanied by specific ethical concerns need to be processed and visualized with an additional level of carefulness and thought. There has been little research on adequate visualization design incorporating such considerations. To address this gap we present insights from Topography of Violence, a visualization project with the Jewish Museum Berlin that focuses on a dataset of more than 4,500 acts of violence against Jews in Germany between 1930 and 1938. Drawing from the joint project, we develop an approach to the visualization of sensitive data, which features both conceptual and procedural considerations for visualization design. Our findings provide value for both visualization researchers and practitioners by highlighting challenges and opportunities for ethical data visualization.
TourVis: Narrative Visualization of Multi-Stage Bicycle Races
Authors: José Díaz 0003, Marta Fort, Pere-Pau Vázquez
Published at EuroVis, 2021

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.
Uncertainty-aware Visualization in Medical Imaging - A Survey
Authors: Christina Gillmann, Dorothee Saur, Thomas Wischgoll, Gerik Scheuermann
Published at EuroVis, 2021

Medical imaging (image acquisition, image transformation, and image visualization) is a standard tool for clinicians in order to make diagnoses, plan surgeries, or educate students. Each of these steps is affected by uncertainty, which can highly influence the decision‐making process of clinicians. Visualization can help in understanding and communicating these uncertainties. In this manuscript, we aim to summarize the current state‐of‐the‐art in uncertainty‐aware visualization in medical imaging. Our report is based on the steps involved in medical imaging as well as its applications. Requirements are formulated to examine the considered approaches. In addition, this manuscript shows which approaches can be combined to form uncertainty‐aware medical imaging pipelines. Based on our analysis, we are able to point to open problems in uncertainty‐aware medical imaging.
Uncertainty-aware Visualization of Regional Time Series Correlation in Spatio-temporal Ensembles
Authors: Marina Evers, Karim Huesmann, Lars Linsen
Published at EuroVis, 2021

Given a time‐varying scalar field, the analysis of correlations between different spatial regions, i.e., the linear dependence of time series within these regions, provides insights into the structural properties of the data. In this context, regions are connected components of the spatial domain with high time series correlations. The detection and analysis of such regions is often performed globally, which requires pairwise correlation computations that are quadratic in the number of spatial data samples. Thus, operations based on all pairwise correlations are computationally demanding, especially when dealing with ensembles that model the uncertainty in the spatio‐temporal phenomena using multiple simulation runs. We propose a two‐step procedure: In a first step, we map the spatial samples to a 3D embedding based on a pairwise correlation matrix computed from the ensemble of time series. The 3D embedding allows for a one‐to‐one mapping to a 3D color space such that the outcome can be visually investigated by rendering the colors for all samples in the spatial domain. In a second step, we generate a hierarchical image segmentation based on the color images. From then on, we can visually analyze correlations of regions at all levels in the hierarchy within an interactive setting, which includes the uncertainty‐aware analysis of the region's time series correlation and respective time lags.