EuroVisPosters2024

Permanent URI for this collection

https://diglib.eg.org/handle/10.2312/3606996

Browse

Now showing 1 - 20 of 26

Cooperative Design of a Dashboard for Monitoring the P4D Cohort Study on Major Depression
(The Eurographics Association, 2024) Maharlou, Hamidreza; Bössel-Debbert, Nicole; Lucht, Michael; Maier, Hannah B.; Mücke, Stefanie; Müntefering, Fabian; Neuhaus, Barbara; Prokein, Jana; Reif-Leonhard, Christine; Voges, Jan; Weber, Heike; Weihs, Antoine; Frieling, Helge; Oeltze-Jafra, Steffen; Kucher, Kostiantyn; Diehl, Alexandra; Gillmann, Christina
The P4D (Personalised, Predictive, Precise, and Preventive Medicine for Major Depression) study aims at an improved prediction of treatment outcomes based on a more precise stratification of major depression subtypes. It is collecting very complex data from 1,000 patients across five German university hospitals. We have designed a dashboard to monitor the study and share the collected data among the study partners. We employed a state-of-the-art cooperative dashboard design approach by Setlur et al. [SCST24] in two design cycles: user feedback and dashboard revision. We observed a significant improvement in user satisfaction from the first (Mean=3.57 std=0.95) to the second (Mean=3.87 std=0.80) cycle and an overall positive assessment.
A Dashboard for Simplifying Machine Learning Models using Feature Importances and Spurious Correlation Analysis
(The Eurographics Association, 2024) Cech, Tim; Kohlros, Erik; Scheibel, Willy; Döllner, Jürgen; Kucher, Kostiantyn; Diehl, Alexandra; Gillmann, Christina
Machine Learning models underlie a trade-off between accurracy and explainability. Given a trained, complex model, we contribute a dashboard that supports the process to derive more explainable models, here: Fast-and-Frugal Trees, with further introspection using feature importances and spurious correlation analyses. The dashboard further allows to iterate over the feature selection and assess the trees' performance in comparison to the complex model.
A Design Space for Static Visualizations with Several Orders of Magnitude
(The Eurographics Association, 2024) Batziakoudi, Katerina; Cabric, Florent; Rey, Stéphanie; Fekete, Jean-Daniel; Kucher, Kostiantyn; Diehl, Alexandra; Gillmann, Christina
We describe the design space for visualizations with attributes spanning several orders of magnitude, termed Orders of Magnitude Values (OMVs), and present OMVis, a tool for the interactive exploration of this design space. We divide OMVs into mantissa and exponent for separate visual encoding, similar to scientific notation. We create visualizations combining an OMV with another attribute-nominal, ordinal, time, or quantitative-using various marks and visual channels following the rules of the Grammar of Graphics. We refine this space by enforcing integrity constraints from visualization literature, aiming to enhance the effectiveness of the generated visualizations.
EuroVis 2024 Posters: Frontmatter
(The Eurographics Association, 2024) Kucher, Kostiantyn; Diehl, Alexandra; Gillmann, Christina; Kucher, Kostiantyn; Diehl, Alexandra; Gillmann, Christina
Exploring Designs for Combined Visual Encoding of Absolute and Fractional Values
(The Eurographics Association, 2024) Poddar, Madhav; Beck, Fabian; Kucher, Kostiantyn; Diehl, Alexandra; Gillmann, Christina
Datasets with absolute values that represent fractions of a whole are commonplace. To visualize these datasets, one can decide between visualizations highlighting the absolute value or the fractional value, but there are variants of visualizations that account for both. In this work, we explore the design space of such visualizations that show both the absolute and fractional values. Along with this, we include an initial assessment on what analysis tasks pertain to these designs and how these tasks might be influenced by the input data characteristics.
Hey ChatGPT, can you visualize my data? - A Multi-Dimensional Study on using an LLM for Constructing Data Visualizations
(The Eurographics Association, 2024) Ströbel, Mara; Eckert, Kai; Nagel, Till; Kucher, Kostiantyn; Diehl, Alexandra; Gillmann, Christina
This paper explores the effectiveness of an LLM in creating data visualizations across a spectrum of scenarios, characterized by three key dimensions: the complexity of the underlying data, the user's data visualization competencies, and the requirements of the resulting visualization. Based on an empirical study, we offer insights into the potential role of LLMs as tools for empowering users with varied expertise to effectively visualize data.
Hybrid Multilayer Network Visualization of Bibliographic Data
(The Eurographics Association, 2024) Durant, Eloi; Tappini, Alessandra; Didimo, Walter; Liotta, Giuseppe; Ghoniem, Mohammad; Kucher, Kostiantyn; Diehl, Alexandra; Gillmann, Christina
To reify the concept of layers, multilayer network visualizations often lay out nodes on distinct hyperplanes, one per layer. In this work, we consider the case of a 2D representation where layer nodes are laid out on parallel rectilinear axes. The adoption of classic edge drawing strategies here would lead to much visual clutter due to overlapping inter-layer and intra-layer edges. Moreover, distinguishing between these two types of edges would be fairly difficult. In this preliminary work, we explore the potential of using a hybrid visualization blending the adjacency matrix and node-link metaphors to distinguish undirected intraand inter-layer edges, respectively.We apply this approach to the analysis of bibliographic data, and discuss current limitations.
Interactive Human-guided Dimensionality Reduction using Landmark Positioning
(The Eurographics Association, 2024) Cech, Tim; Raue, Christian; Sadrieh, Frederic; Scheibel, Willy; Döllner, Jürgen; Kucher, Kostiantyn; Diehl, Alexandra; Gillmann, Christina
Dimensionality Reduction Techniques (DRs) are used for projecting high-dimensional data onto a two-dimensional plane. One subclass of DRs are such techniques that utilize landmarks. Landmarks are a subset of the original data space that are projected by a slow and more precise technique. The other data points are then placed in relation to these landmarks with respect to their distance in the high-dimensional space. We propose a technique to refine the placement of the landmarks by a human user. We test two different techniques for unprojecting the movement of the low-dimensional landmarks into the high-dimensional data space. We showcase that such a movement can increase certain quality metrics while decreasing others. Therefore, users may use our technique to challenge their understanding of the high-dimensional data space.
Interactive Visual Exploration of Arctic Sea Ice Extent 1978-2023
(The Eurographics Association, 2024) Pedersen, Ditte Parsberg; Hansen, Lærke Ina Krogaard; Sørensen, Esben Bay; Walsh, Gareth; Kusnick, Jakob; Jänicke, Stefan; Kucher, Kostiantyn; Diehl, Alexandra; Gillmann, Christina
The Arctic region and it ecosystem is undergoing rapid and significant environmental transformations by climate change. Traditional visualizations on this lack interactivity, hindering in-depth exploration by domain experts. This paper presents a participatory approach to developing a more interactive visualization tool for exploring the extent of Arctic sea ice. Leveraging data from the National Snow and Ice Data Center, our prototype offers insights into overall trends, seasonal variations, regional differences, and historical comparisons. By combining geospatial and temporal overviews, users can analyze changes comprehensively. Our visualization tool is a step towards interactively exploring the Arctic sea ice developments and thereby facilitating researchers to gain informed insights into the complex dynamics of a key aspect in the Arctic ecosystem.
LaNe Plot: A Visual Fingerprinting Technique for Sequential Data
(The Eurographics Association, 2024) Rathish, Harith; Picón, Ginés Carreto; Schulz, Hans-Jörg; Kucher, Kostiantyn; Diehl, Alexandra; Gillmann, Christina
Visual summaries of sequential data are often used to identify common trends at a glance. In this poster, we propose a visualization technique to fingerprint sequential data by showing the difference between contiguous data points. For each data point in the sequence, we visualize the difference between itself and the last data point as well as the next data point. As an application, we visualized the revision histories of Wikipedia articles to demonstrate the exploratory value of this technique.
Manifold Modelling with Minimum Spanning Trees
(The Eurographics Association, 2024) Bot, Daniël M.; Huo, Peiyang; Arleo, Alessio; Paulovich, Fernando; Aerts, Jan; Kucher, Kostiantyn; Diehl, Alexandra; Gillmann, Christina
Recent dimensionality reduction algorithms operate on a manifold assumption and expect data to be uniformly sampled from that underlying manifold. While some algorithms attempt to be robust for non-uniform sampling, their reliance on k-nearest neighbours to approximate manifolds limits how well they can span sampling gaps without introducing shortcuts. We present a minimum-spanning-tree-based manifold approximation approach that overcomes this problem and demonstrate it crosses sampling-gaps without introducing shortcuts while creating networks with few edges. A python package implementing our algorithm is available at https://github.com/vda-lab/multi_mst.
Peeking at Visualization Research on Information Diffusion
(The Eurographics Association, 2024) Usul, Mert; Arleo, Alessio; Kucher, Kostiantyn; Diehl, Alexandra; Gillmann, Christina
Diffusion Processes are a widely researched topic of interest to different scientific domains. One of the most popular research directions is Information Diffusion, pertaining how information spreads over a tightly connected network. From the modeling perspective, many different approaches are known in the literature; however, in the visualization community, this still represents an under-investigated problem. In this work, we present a succinct overview of the current state-of-the-art in Visual Analytics techniques employed in representing and understanding diffusion processes happening over networks. We consider different application domains and introduce a taxonomy that categorizes and provides structure to our selection of papers, fostering further research in the field of Visual Analytics of Information Diffusion processes.
Personal Mobile Devices to Assist with Wrist Rehabilitation at Home
(The Eurographics Association, 2024) Grioui, Fairouz; Antoniadis, Pantelis; Yu, Xingyao; Blascheck, Tanja; Kucher, Kostiantyn; Diehl, Alexandra; Gillmann, Christina
We present two modalities using mobile devices to assist patients with home-based wrist rehabilitation exercises. The first modality is a standalone smartwatch application that tracks the wrist's Range of Motion (ROM) and visualizes real-time exercise data. The second modality uses a smartphone to mirror the visualizations displayed on the smartwatch to overcome screen invisibility while rotated. In this poster, we report on our pilot study and the qualitative results of the two solutions. Results show that in terms of usability, the smartwatch-only modality score surpassed the mirrored-display. However, participants preferred the mirrored-display modality more for home-based usage.
A Quality Metric to Improve Scatterplots for Explainable AI
(The Eurographics Association, 2024) Liu, Liqun; Ruddle, Roy A.; Bogachev, Leonid V.; Rezaei, Mahdi; Khara, Arjun; Kucher, Kostiantyn; Diehl, Alexandra; Gillmann, Christina
Scatterplots are widely utilised in Explainable Artificial Intelligence (XAI) to investigate misclassifications and patterns among instances. However, when datasets are large, overplotting diminishes the effectiveness of scatterplots. This poster introduces a new quality metric to measure the overplotting of scatterplots in the context of XAI. Initially, we assess the significance of each data point within a scatterplot by continuous density transformation, Mahalanobis Distance and a mapping function. Building on this foundation, we develop a quality metric for scatterplots. Our metric performs well accounting for rendering orders and marker sizes in scatterplots, showcasing the metric's potential to improve the effectiveness of XAI scatterplots.
Supporting Astrophysical Visualization with Sonification
(The Eurographics Association, 2024) Gorenko, Ivar; Besançon, Lonni; Forsell, Camilla; Rönnberg, Niklas; Kucher, Kostiantyn; Diehl, Alexandra; Gillmann, Christina
This poster presents initial design steps exploring how sonification can be used to support visualization for comprehension of space and time in astronomical data. Radio signals travel at the speed of light. With a visualization of the universe, it is possible to travel faster than light and pass the radio waves leaving earth. We can then travel back in time. We propose to use sonification consisting of songs representing each year as a musical journey through space and time to create an engaging experience.
Towards a Visual Analytics System for Emotion Trajectories in Multiparty Conversations
(The Eurographics Association, 2024) Huang, Zeyang; Kucher, Kostiantyn; Kerren, Andreas; Kucher, Kostiantyn; Diehl, Alexandra; Gillmann, Christina
Visualizing sentiments in textual data has received growing interest; however, representing emotions within interlocutor relationships and associating them with the temporal progression of dialogues remains challenging. In this poster abstract, we describe the ongoing work on a visual analytics tool designed for analyzing emotion trajectories within dialogue collections composed of utterances from multiple speakers. The proposed tool provides exploration at different levels of detail to complex multigraphs, where edges represent direct responses between speakers through their utterances. Our approach includes several selection strategies for connecting different views: summaries of emotion transitions across dialogue groups, detailed analyses of individual utterances within specific dialogues of interest in interlocutor networks, and close reading. The tool aims to support model development in natural language processing by allowing users to explore text corpora interactively.
Towards Presenting Travel Times in a Bus Network as Immersive and Adaptive Data Stories
(The Eurographics Association, 2024) Panzer, Lukas; Beck, Fabian; Kucher, Kostiantyn; Diehl, Alexandra; Gillmann, Christina
Desirable increased usage of public transport is, to some extent, limited by people being unaware of their traveling options. To invite a broad audience to casually explore their local bus network, we present an approach for the interactive analysis of traveling times in an immersive, animated simulation of buses. A first prototype already implements the core travel time visualization for a personal starting point. Additionally, we outline specific plans to extend the system towards telling adaptive stories that summarize the data and guide the users to relevant insights.
Visplorify: Interactive Visual Analysis of Spotify Listening Histories
(The Eurographics Association, 2024) Franzke, Louis; Meinecke, Christofer; Schebera, Jeremias; Wiegreffe, Daniel; Kucher, Kostiantyn; Diehl, Alexandra; Gillmann, Christina
The audio streaming platform Spotify collects various personal data from its users, including an extensive streaming history. Despite providing this data, Spotify lacks tools for visualizing or analyzing it. This work introduces Visplorify, an application to analyze and visualize the extended streaming history. The main goal is to offer interested Spotify users a way to visually explore their listening behavior and gain deeper insights into their music data. Visplorify automatically processes users' streaming history, enriching it with detailed data presented in an interactive dashboard. Users can explore and gain insights from their data using filters and visualizations to examine patterns and trends. Users have found several use cases, such as identifying personal patterns, reflecting on life events, discovering old and new favorite songs, and creating playlists. The application also provides users with insight into potential analyses of their personal data, increasing transparency.
Visual Analysis of Power Plant Data for European Countries
(The Eurographics Association, 2024) Wang, Jinyi; Kucher, Kostiantyn; Kerren, Andreas; Kucher, Kostiantyn; Diehl, Alexandra; Gillmann, Christina
A power plant is a complex real-world system associated with rich multidimensional data relevant to its construction and activity. Thus, choosing an appropriate way to visualize power plant data is important for users to understand and explore more about such systems. Most of the approaches existing in this field support only a static representation of data from a small region. This makes it hard for the users to get an overview or explore specific power plants. In this poster abstract, we describe an interactive visualization tool designed for the analysis of power plant data in Europe. Our approach provides an overview and detail visualization approach for Global Power Plant Database entries. With this tool, users can easily find power plants, see details on demand, filter, compare, and explore the power plant outage scenarios from the nearest neighbor perspective.
A Visual Approach to Fair or Negotiated Resource Division
(The Eurographics Association, 2024) Ribler, Randy L.; Wise, Jackson; Kucher, Kostiantyn; Diehl, Alexandra; Gillmann, Christina
The fair division problem addresses the frequently encountered situation in which a set of resources must be fairly divided between two or more stakeholders. Dividing possessions after a divorce, assigning tasks to workers, and determining the terms of contracts or treaties are all examples of this problem. Algorithms have been developed to provide solutions that optimize for various metrics, but for many reasons, including the lack of agreement on what constitutes fairness, algorithms cannot provide a definitive result. Visualizations, rather than providing a single candidate solution, can be used effectively to browse the search space and generate a pool of candidate allocations that are most likely to be appealing to all parties. Candidate solutions can be used by stakeholders, either separately or cooperatively, as the basis for negotiation. We demonstrate prototype software that provides this capability for a set of indivisible resources that are divided between two stakeholders.

Browse

Browsing EuroVisPosters2024 by Title

Results Per Page

Sort Options