EuroVisShort2023

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https://diglib.eg.org/handle/10.2312/2633339

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Accelerated Volume Rendering with Volume Guided Neural Denoising
(The Eurographics Association, 2023) Jabbireddy, Susmija; Li, Shuo; Meng, Xiaoxu; Terrill, Judith E.; Varshney, Amitabh; Hoellt, Thomas; Aigner, Wolfgang; Wang, Bei
Monte Carlo path tracing techniques create stunning visualizations of volumetric data. However, a large number of computationally expensive light paths are required for each sample to produce a smooth and noise-free image, trading performance for quality. High-quality interactive volume rendering is valuable in various fields, especially education, communication, and clinical diagnosis. To accelerate the rendering process, we combine learning-based denoising techniques with direct volumetric rendering. Our approach uses additional volumetric features that improve the performance of the denoiser in the post-processing stage. We show that our method significantly improves the quality of Monte Carlo volume-rendered images for various datasets through qualitative and quantitative evaluation. Our results show that we can achieve volume rendering quality comparable to the state-of-the-art at a significantly faster rate using only one sample path per pixel.
ARrow: A Real-Time AR Rowing Coach
(The Eurographics Association, 2023) Iannucci, Elena; Chen, Zhutian; Armeni, Iro; Pollefeys, Marc; Pfister, Hanspeter; Beyer, Johanna; Hoellt, Thomas; Aigner, Wolfgang; Wang, Bei
Rowing requires physical strength and endurance in athletes as well as a precise rowing technique. The ideal rowing stroke is based on biomechanical principles and typically takes years to master. Except for time-consuming video analysis after practice, coaches currently have no means to quantitatively analyze a rower's stroke sequence and body movement. We propose ARrow, an AR application for coaches and athletes that provides real-time and situated feedback on a rower's body position and stroke. We use computer vision techniques to extract the rower's 3D skeleton and to detect the rower's stroke cycle. ARrow provides visual feedback on three levels: Tracking of basic performance metrics over time, visual feedback and guidance on a rower's stroke sequence, and a rowing ghost view that helps synchronize the body movement of two rowers. We developed ARrow in close colaboration with international rowing coaches and demonstrate its usefulness in a user study with athletes and coaches.
Ask and You Shall Receive (a Graph Drawing): Testing ChatGPT's Potential to Apply Graph Layout Algorithms
(The Eurographics Association, 2023) Bartolomeo, Sara Di; Severi, Giorgio; Schetinger, Victor; Dunne, Cody; Hoellt, Thomas; Aigner, Wolfgang; Wang, Bei
Large language models (LLMs) have recently taken the world by storm. They can generate coherent text, hold meaningful conversations, and be taught concepts and basic sets of instructions-such as the steps of an algorithm. In this context, we are interested in exploring the application of LLMs to graph drawing algorithms by performing experiments on ChatGPT, one of the most recent cutting-edge LLMs made available to the public. These algorithms are used to create readable graph visualizations. The probabilistic nature of LLMs presents challenges to implementing algorithms correctly, but we believe that LLMs' ability to learn from vast amounts of data and apply complex operations may lead to interesting graph drawing results. For example, we could enable users with limited coding backgrounds to use simple natural language to create effective graph visualizations. Natural language specification would make data visualization more accessible and user-friendly for a wider range of users. Exploring LLMs' capabilities for graph drawing can also help us better understand how to formulate complex algorithms for LLMs; a type of knowledge that could transfer to other areas of computer science. Overall, our goal is to shed light on the exciting possibilities of using LLMs for graph drawing-using the Sugiyama algorithm as a sample case-while providing a balanced assessment of the challenges and opportunities they present. A free copy of this paper with all supplemental materials to reproduce our results is available on osf.io .
BOLT: A Natural Language Interface for Dashboard Authoring
(The Eurographics Association, 2023) Srinivasan, Arjun; Setlur, Vidya; Hoellt, Thomas; Aigner, Wolfgang; Wang, Bei
Authoring dashboards is often a complex process, requiring expertise in both data analysis and visualization design. With current tools, authors lack the means to express their objectives for creating a dashboard (e.g., summarizing data changes or comparing data categories), making it difficult to discover and assemble content relevant to the dashboard. Addressing this challenge, we propose the idea of employing natural language (NL) for dashboard authoring with a prototype interface, BOLT. In this paper, we detail BOLT's design and implementation, describing how the system maps NL utterances to prevalent dashboard objectives and generates dashboard recommendations. Utilizing BOLT as a design probe, we validate the proposed idea of NL-based dashboard authoring through a preliminary user study. Based on the study feedback, we highlight promising application scenarios and future directions to support richer dashboard authoring workflows.
A Business Intelligence Dashboard for the Phone: Small-scale Visualizations Embedded into a Mobile Analysis and Monitoring Solution
(The Eurographics Association, 2023) Höll, Nils; Latif, Shahid; Beck, Fabian; Hoellt, Thomas; Aigner, Wolfgang; Wang, Bei
Although smartphones have become ubiquitous, most of the visualization applications are still designed for large-screen devices. In a business intelligence context, dashboard solutions for monitoring key performance indicators and performing simple analysis tasks can profit from being available on the phone.We identify usage scenarios and design requirements by interviewing 20 business experts. Our solution adapts existing diagrams and proposes novel visualizations for the small-screen environment, and integrates them into an easy-to-use visual dashboard.
CatNetVis: Semantic Visual Exploration of Categorical High-Dimensional Data with Force-Directed Graph Layouts
(The Eurographics Association, 2023) Thane, Michael; Blum, Kai M.; Lehmann, Dirk J.; Hoellt, Thomas; Aigner, Wolfgang; Wang, Bei
We introduce CatNetVis, a novel method of representing semantical relations in categorical high-dimensional data. Traditional methods provide insights into many aspects of visual exploration of data. However, most of them lack information on relations in between categories or even clusters of categories. The force-directed network layout utilized by CatNetVis enables a lightweight approach in order to explore such semantical relations. The connections within the network are perceived as an intuitive metaphor for clusters of connections/relations in categorical data denoted as communities. While the user interacts, visual encodings such as information about the entropy and frequencies allow a fast perception of relation between categories and its frequencies, respectively. We illustrate how CatNetVis performs as an effective addition to traditional methods by demonstrating the method on an example data sets and comparing it to conventional methods.
cols4all: a Color Palette Analysis Tool
(The Eurographics Association, 2023) Tennekes, Martijn; Puts, Marco J. H.; Hoellt, Thomas; Aigner, Wolfgang; Wang, Bei
cols4all is a software tool to analyse and compare color palettes, using several properties, including color blind friendliness and fairness, which checks whether all palette colors stand out about equally.
Detection and Visual Analysis of Pathological Abnormalities in Diffusion Tensor Imaging with an Anomaly Lens
(The Eurographics Association, 2023) Bareth, Marlo; Groeschel, Samuel; Gruen, Johannes; Pretzel, Pablo; Schultz, Thomas; Hoellt, Thomas; Aigner, Wolfgang; Wang, Bei
In clinical practice, Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) is usually evaluated by visual inspection of grayscale maps of Fractional Anisotropy or mean diffusivity. However, the fact that those maps only contain part of the information that is captured in DT-MRI implies a risk of missing signs of disease. In this work, we propose a visualization system that supports a more comprehensive analysis with an anomaly score that accounts for the full diffusion tensor information. It is computed by comparing the DT-MRI scan of a given patient to a control group of healthy subjects, after spatial coregistration. Moreover, our system introduces an Anomaly Lens which visualizes how a user-specified region of interest deviates from the controls, indicating which aspects of the tensor (norm, anisotropy, mode, rotation) differ most, whether they are elevated or reduced, and whether their covariation matches the covariances within the control group. Applying our system to patients with metachromatic leukodystrophy clearly indicates regions affected by the disease, and permits their detailed analysis.
Effect of Color Palettes in Heatmaps Perception: a Study
(The Eurographics Association, 2023) Molina, Elena; Middel, Carolina; Vázquez, Pere-Pau; Hoellt, Thomas; Aigner, Wolfgang; Wang, Bei
Heatmaps are a widely used technique in visualization. Unfortunately, they have not been investigated in depth and little is known about the best parameterizations so that they are properly interpreted. The effect of different palettes on our ability to read values is still unknown. To address this issue, we conducted a user study, in which we analyzed the effect of two commonly used color palettes, Blues and Viridis, on value estimation and value search. As a result, we provide some suggestions for what to expect from the heatmap configurations analyzed.
EuroVis 2023 Short Papers: Frontmatter
(The Eurographics Association, 2023) Hoellt, Thomas; Aigner, Wolfgang; Wang, Bei; Hoellt, Thomas; Aigner, Wolfgang; Wang, Bei
Identifying Cluttering Edges in Near-Planar Graphs
(The Eurographics Association, 2023) Wageningen, Simon van; Mchedlidze, Tamara; Telea, Alexandru; Hoellt, Thomas; Aigner, Wolfgang; Wang, Bei
Planar drawings of graphs tend to be favored over non-planar drawings. Testing planarity and creating a planar layout of a planar graph can be done in linear time. However, creating readable drawings of nearly planar graphs remains a challenge. We therefore seek to answer which edges of nearly planar graphs create clutter in their drawings generated by mainstream graph drawing algorithms. We present a heuristic to identify problematic edges in nearly planar graphs and adjust their weights in order to produce higher quality layouts with spring-based drawing algorithms. Our experiments show that our heuristic produces significantly higher quality drawings for augmented grid graphs, augmented triangulations, and deep triangulations.
Level of Detail Visual Analysis of Structures in Solid-State Materials
(The Eurographics Association, 2023) Thygesen, Signe Sidwall; Abrikosov, Alexei I.; Steneteg, Peter; Masood, Talha Bin; Hotz, Ingrid; Hoellt, Thomas; Aigner, Wolfgang; Wang, Bei
We propose a visual analysis method for the comparison and evaluation of structures in solid-state materials based on the electron density field using topological analysis. The work has been motivated by a material science application, specifically looking for new so-called layered materials whose physical properties are required in many modern technological developments. Due to the incredibly large search space, this is a slow and tedious process, requiring efficient data analysis to characterize and understand the material properties. The core of our proposed analysis pipeline is an abstract bar representation that serves as a concise signature of the material, supporting direct comparison and also an exploration of different material candidates.
LOKI: Reusing Custom Concepts in Interactive Analytic Workflows
(The Eurographics Association, 2023) Setlur, Vidya; Beers, Andrew; Hoellt, Thomas; Aigner, Wolfgang; Wang, Bei
Natural language (NL) interaction enables users to be expressive with their queries when exploring data. Users often specify complex NL queries that involve a combination of grouping, aggregations, and conditionals of data attributes and values. Such queries are often reused several times by users during their analytical workflows. Existing systems offer limited support to save these bespoke queries as concepts that can be referenced in subsequent NL queries, leading to users having to respecify these queries repeatedly. To address this issue, we describe a system, LOKI that allows users to save complex and bespoke queries as reusable concepts and use these concepts in other NL queries and analytics tools. For example, users can save an NL query, ''show me the opportunity amount by customer for open opportunities'' in a sales dataset, as a concept 'followup customers' and reference this custom concept in a query such as ''show me the total opportunity amount for followup customers.'' A qualitative evaluation of LOKI indicates the usefulness of supporting the reuse of custom concepts across various analytical workflows. We identify future research directions around in-situ semantic enrichment and dynamic concept maps for data exploration.
MoneyVis: Open Bank Transaction Data for Visualization and Beyond
(The Eurographics Association, 2023) Firat, Elif E.; Vytla, Dharmateja; Singh, Navya Vasudeva; Jiang, Zhuoqun; Laramee, Robert S.; Hoellt, Thomas; Aigner, Wolfgang; Wang, Bei
With the rapid evolution of financial technology (FinTech) the importance of analyzing financial transactions is growing in importance. As the prevalence and number of financial transactions grow, so does the necessity of visual analysis tools to study the behavior represented by these transactions. However, real bank transaction data is generally private for security and confidentiality reasons, thus preventing its use for visual analysis and research. We present MoneyData, an anonymized open bank data set spanning seven years worth of transactions for research and analysis purposes. To our knowledge, this is the first real-world retail bank transaction data that has been anonymized and made public for visualization and analysis by other researchers. We describe the data set, its characteristics, and the anonymization process and present some preliminary analysis and images as a starting point for future research. The transactions are also categorized to facilitate understanding. We believe the availability of this open data will greatly benefit the research community and facilitate further study of finance.
Multi-attribute Visualization and Improved Depth Perception for the Interactive Analysis of 3D Truss Structures
(The Eurographics Association, 2023) Becher, Michael; Groß, Anja; Werner, Peter; Maierhofer, Mathias; Reina, Guido; Ertl, Thomas; Menges, Achim; Weiskopf, Daniel; Hoellt, Thomas; Aigner, Wolfgang; Wang, Bei
In architecture, engineering, and construction (AEC), load-bearing truss structures are commonly modeled as a set of connected beam elements. For complex 3D structures, rendering beam elements as line segments presents several challenges due densely overlapping elements, including visual clutter, and general depth perception issues. Furthermore, line segments provide very little area for displaying additional element attributes. In this paper, we investigate the effectiveness of rendering effects for reducing visual clutter and improving depth perception for truss structures specifically, such as distance-based brightness attenuation and screen-space ambient occlusion (SSAO). Additionally, we provide multiple options for multi-attribute visualization directly on the structure and evaluate both aspects with two expert interviews.
RiskFix: Supporting Expert Validation of Predictive Timeseries Models in High-Intensity Settings
(The Eurographics Association, 2023) Morgenshtern, Gabriela; Verma, Arnav; Tonekaboni, Sana; Greer, Robert; Bernard, Jürgen; Mazwi, Mjaye; Goldenberg, Anna; Chevalier, Fanny; Hoellt, Thomas; Aigner, Wolfgang; Wang, Bei
Many real-world machine learning workflows exist in longitudinal, interactive machine learning (ML) settings. This longitudinal nature is often due to incremental increasing of data, e.g., in clinical settings, where observations about patients evolve over their care period. Additionally, experts may become a bottleneck in the workflow, as their limited availability, combined with their role as human oracles, often leads to a lack of ground truth data. In such cases where ground truth data is small, the validation of interactive machine learning workflows relies on domain experts. Only those humans can assess the validity of a model prediction, especially in new situations that have been covered only weakly by available training data. Based on our experiences working with domain experts of a pediatric hospital's intensive care unit, we derive requirements for the design of support interfaces for the validation of interactive ML workflows in fast-paced, high-intensity environments. We present RiskFix, a software package optimized for the validation workflow of domain experts of such contexts. RiskFix is adapted to the cognitive resources and needs of domain experts in validating and giving feedback to the model. Also, RiskFix supports data scientists in their model-building work, with appropriate data structuring for the re-calibration (and possible retraining) of ML models.
Semantic Hierarchical Exploration of Large Image Datasets
(The Eurographics Association, 2023) Bäuerle, Alex; Onzenoodt, Christian van; Jönsson, Daniel; Ropinski, Timo; Hoellt, Thomas; Aigner, Wolfgang; Wang, Bei
We present a method for exploring and comparing large sets of images with metadata using a hierarchical interaction approach. Browsing many images at the same time requires either a large screen space or an abundance of scrolling interaction. We address this problem by projecting the images onto a two-dimensional Cartesian coordinate system by combining the latent space of vision neural networks and dimensionality reduction techniques. To alleviate overdraw of the images, we integrate a hierarchical layout and navigation, where each group of similar images is represented by the image closest to the group center. Advanced interactive analysis of images in relation to their metadata is enabled through integrated, flexible filtering based on expressions. Furthermore, groups of images can be compared through selection and automated aggregated metadata visualization. We showcase our method in three case studies involving the domains of photography, machine learning, and medical imaging.
TGVE: a Tool for Analysis and Visualization of Geospatial Data
(The Eurographics Association, 2023) Hama, Layik; Beecham, Roger; Lomax, Nik; Hoellt, Thomas; Aigner, Wolfgang; Wang, Bei
We introduce the Turing Geovisualisation Engine (TGVE), a web-based, open-source tool for interactive visualization and analysis of geospatial data. Built on ReactJS and R, TGVE is designed to support a variety of users, including data scientists and stakeholders who wish to engage the wider public with geospatial data. In this short paper, we provide an overview of TGVE's features and capabilities, including its ability to publish data and customize visualization settings using URL parameters. We highlight the potential impact of TGVE for geospatial research and offer examples of its use in practice. Additionally, we discuss current limitations of the tool and outline future work, such as improving compatibility with other geospatial data formats and addressing performance issues for large datasets.
Visual Exploration of Indirect Bias in Language Models
(The Eurographics Association, 2023) Louis-Alexandre, Judith; Waldner, Manuela; Hoellt, Thomas; Aigner, Wolfgang; Wang, Bei
Language models are trained on large text corpora that often include stereotypes. This can lead to direct or indirect bias in downstream applications. In this work, we present a method for interactive visual exploration of indirect multiclass bias learned by contextual word embeddings. We introduce a new indirect bias quantification score and present two interactive visualizations to explore interactions between multiple non-sensitive concepts (such as sports, occupations, and beverages) and sensitive attributes (such as gender or year of birth) based on this score.
Visualizing Element Interactions in Dynamic Overlapping Sets
(The Eurographics Association, 2023) Agarwal, Shivam; Hoellt, Thomas; Aigner, Wolfgang; Wang, Bei
Elements-the members in sets-may change their memberships over time. Moreover, elements also directly interact with each other, indicating an explicit connection between them. Visualizing both together becomes challenging. Using an existing dynamic set visualization as a basis, we propose an approach to encode the interactions of elements together with changing memberships in sets. We showcase the value in visually analyzing both aspects of elements together through two application examples. The first example shows the evolution of business portfolio and interactions (e.g., acquisitions and partnerships) among companies. A second example analyzes the dynamic collaborative interactions among researchers in computer science.

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