EuroVisPosters2022

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https://diglib.eg.org/handle/10.2312/2633198

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On Visualizing Music Storage Media for Modern Access to Historic Sources
(The Eurographics Association, 2022) Khulusi, Richard; Fricke, Heike; Krone, Michael; Lenti, Simone; Schmidt, Johanna
Finding a balance between conserving historic objects and using them for research is one of the big issues in historic collections. Digitization holds the opportunity to offer a safe and non-destructible access to historic objects, making them available for research. With this poster, we want to give insight into our planned visualization system, using close and distant reading access for visual analysis approaches and allowing musicologists novel approaches to normally fragile and endangered media.
PSEUDo: Interactive Pattern Search in Multivariate Time Series with Locality-Sensitive Hashing and Relevance Feedback
(The Eurographics Association, 2022) Yu, Yuncong; Kruyff, Dylan; Jiao, Jiao; Becker, Tim; Behrisch, Michael; Krone, Michael; Lenti, Simone; Schmidt, Johanna
We present PSEUDo, a visual pattern retrieval tool for multivariate time series. It aims to overcome the uneconomic (re- )training with deep learning-based methods. Very high-dimensional time series emerge on an unprecedented scale due to increasing sensor usage and data storage. Visual pattern search is one of the most frequent tasks on such data. Automatic pattern retrieval methods often suffer from inefficient training, a lack of ground truth, and a discrepancy between the similarity perceived by the algorithm and the user. Our proposal is based on a query-aware locality-sensitive hashing technique to create a representation of multivariate time series windows. It features sub-linear training and inference time with respect to data dimensions. This performance gain allows an instantaneous relevance-feedback-driven adaption and converges to users' similarity notion. We are benchmarking PSEUDo in accuracy and speed with representative and state-of-the-art methods, evaluating its steerability through simulated user behavior, and designing expert studies to test PSEUDo's usability.
Visual Exploration of Genetic Sequence Variants in Pangenomes
(The Eurographics Association, 2022) van den Brandt, Astrid; Jonkheer, Eef M.; van Workum, Dirk-Jan M.; Smit, Sandra; Vilanova, Anna; Krone, Michael; Lenti, Simone; Schmidt, Johanna
To study the genetic sequence variation underlying traits of interest, the field of comparative genomics is moving away from analyses with single reference genomes to pangenomes; abstract representations of multiple genomes in a species or population. Pangenomes are beneficial because they represent a diverse set of genetic material and therefore avoid bias towards a single reference. While pangenomes allow for a complete map of the genetic variation, their large size and complex data structure hinder contextualization and interpretation of analysis results. Current visualization strategies fall short because they are created for single references or do not illustrate links to metadata. We present a work in progress version of a novel visual analytics strategy for pangenomic variant analysis. Our strategy is designed through an intensive involvement of genome scientists. The current design uniquely exploits interactive sorting, aggregation, and linkage relations from different perspectives of the data, to help the genome scientists explore and evaluate variant-trait associations in the context of multiple references and metadata.
Toward an Interaction-Driven Framework for Modeling Big Data Visualization Systems
(The Eurographics Association, 2022) Benvenuti, Dario; Fiordeponti, Giovanni; Cheng, Hao; Catarci, Tiziana; Fekete, Jean-Daniel; Santucci, Giuseppe; Angelini, Marco; Battle, Leilani; Krone, Michael; Lenti, Simone; Schmidt, Johanna
Designing big data visualization applications is challenging due to their complex yet isolated development. One of the most common issues is an increase in latency that can be experienced while interacting with the system. There exists a variety of optimization techniques to handle this issue in specific scenarios, but we lack models for integrating them in a holistic way, hindering the integration of complementary functionality and hampering consistent evaluation across systems. In response, we present a framework for modeling the big data visualization pipeline which builds a bridge between the Visualization, Human-Computer Interaction, and Database communities by integrating their individual contributions within a single, easily interpretable pipeline. With this framework, visualization applications can become aware of the full end-to-end context, making it easier to determine which subset of optimizations best suits the current context.
Visual Exploration of Preference-based Routes in Ski Resorts
(The Eurographics Association, 2022) Rauscher, Julius; Miller, Matthias; Keim, Daniel A.; Krone, Michael; Lenti, Simone; Schmidt, Johanna
Ski resorts exhibit a variety of available pistes and lifts, to which every skier has intrinsic preferences. While novices tend to favor easy pistes, experts might opt for more advanced pistes. In large resorts, the vast possibilities render manual, optimized routing according to specific piste and lift preferences extremely tedious. So far, existing visualizations of ski resorts lack these routing capabilities.We present a visual analytics interface that allows the user to find an optimal route between arbitrary locations in a ski resort according to individual personal preferences. Furthermore, we encode steepness information along the pistes to expose segments that deviate from the difficulty classification and thus are incompatible with the given user preferences.
VisualBib(va): A Visual Analytics Platform for Authoring and Reviewing Bibliographies
(The Eurographics Association, 2022) Dattolo, Antonina; Corbatto, Marco; Angelini, Marco; Krone, Michael; Lenti, Simone; Schmidt, Johanna
Researchers are daily engaged in bibliographic tasks concerning literature search and review, both in the role of authors of scientific papers and when they are reviewers or evaluators. Current indexing platforms poorly support the visual exploration and comparative metadata analysis coming from subsequent searches. To address these issues, we designed and realized VisualBib(va), an online visual analytics solution, where a visual environment includes analysis control, bibliography exploration, automatic metadata extraction, and metrics visualization for real-time scenarios. We introduce and discuss here the relevant functions that VisualBib(va) supports through one usage scenarios related to the creation of a bibliography.
Using Data Comics to Enhance Visualization Literacy
(The Eurographics Association, 2022) Boucher, Magdalena; Stoiber, Christina; Aigner, Wolfgang; Krone, Michael; Lenti, Simone; Schmidt, Johanna
Visualization Literacy as a skill is becoming important, as growing amounts of data require complex ways of visualizing and interpreting them. Yet, it is hardly taught during general education, and not many resources conveying visualization knowledge in an easily accessible way exist. We draw on the notion of data comics, which are already well-suited for communicating visualization insights, but so far have not been explored in the context of teaching visualization skills. We aim to map the research landscape around this idea through a systematic literature research and present a first overview of related areas and how they might influence data comics used to enhance visualization literacy.
Sustainable Urban Wastewater Treatment Visualizations
(The Eurographics Association, 2022) Vega, Juan Marin; Uri-Carreño, Nerea; Kusnick, Jakob; Jänicke, Stefan; Krone, Michael; Lenti, Simone; Schmidt, Johanna
The handling and treatment of urban wastewater are essential to protecting human health and the environment. However, its existence and importance are mostly invisible to the general public. In this work, we present a set of visualization system designed to communicate and visually explore the wastewater treatment system of Vandcenter Syd (VCS), one of the largest water utilities in Denmark on the island Fyn. It operates eight wastewater treatments, and our solution enables geographically exploring and comparing data collected in different facilities with an interactive map. We further provide a set of interactive visual interfaces to support exploring the energy consumption and production trends over the past 10 years. A case study on Ejby Mølle, VCS's largest facility, illustrates its transition from an energy consuming into an energy producing facility.
MOBS - Multi-Omics Brush for Subgraph Visualisation
(The Eurographics Association, 2022) Heylen, Dries; Peeters, Jannes; Ertaylan, Gökhan; Hooyberghs, Jef; Aerts, Jan; Krone, Michael; Lenti, Simone; Schmidt, Johanna
One of the big opportunities in multi-omics analysis is the identification of interactions between molecular entities and their association with diseases. In analyzing and expressing these interactions in the search for new hypotheses, multi-omics data is often either translated into matrices containing pairwise correlations and distances, or visualized as node-link diagrams. A major problem when visualizing large networks however is the occurrence of hairball-like graphs, from which little to none information can be extracted. It is of interest to investigate subgroups of markers that are closely associated with each other, rather than just looking at the overload of all interactions. Hence, we propose MOBS (Multi-Omics Brush for Subgraph visualisation), a web-based visualisation interface that can provide both an overview and detailed views on the data. By means of a two dimensional brush on a heatmap that includes hierarchical clustering, relationships of interest can be extracted from a fully connected graph, to enable detailed analysis of the subgraph of interest.
Visualizing Similarities between American Rap-Artists
(The Eurographics Association, 2022) Meinecke, Christofer; Schebera, Jeremias; Eschrich, Jakob; Wiegreffe, Daniel; Krone, Michael; Lenti, Simone; Schmidt, Johanna
Rap music is one of the biggest music genres in the world today. Since the early days of rap music, references not only to pop culture but also to other rap artists have been an integral part of the lyrics' artistry. In addition, rap musicians reference each other by adopting fragments of lyrics, for example, to give credit. This kind of text reuse can be used to create connections between individual artists. Due to the large amount of lyrics, only automated detection methods can efficiently detect similarities between the songs and the artists. Here, we present a visualization system for analyzing rap music lyrics. We also trained a network tailored specifically for rap lyrics to compute similarities in lyrics. Here a video of the prototype can be seen.
Situated Visualization in Motion for Video Games
(The Eurographics Association, 2022) Bucchieri, Federica; Yao, Lijie; Isenberg, Petra; Krone, Michael; Lenti, Simone; Schmidt, Johanna
We contribute a systematic review of situated visualizations in motion in the context of video games. Video games produce rich dynamic datasets during gameplay that are often visualized to help players succeed in a game. Often these visualizations are moving either because they are attached to moving game elements or due to camera changes. We want to understand to what extent this motion and contextual game factors impact how players can read these visualizations. In order to ground our work, we surveyed 160 visualizations in motion and their embeddings in the game world. Here, we report on our analysis and categorization of these visualizations.
ANARI: ANAlytic Rendering Interface
(The Eurographics Association, 2022) Griffin, Kevin; Amstutz, Jefferson; DeMarle, Dave; Günther, Johannes; Progsch, Jakob; Sherman, William; Stone, John E.; Usher, Will; Kooten, Kees van; Krone, Michael; Lenti, Simone; Schmidt, Johanna
The ANARI API enables users to build the description of a scene to generate imagery, rather than specifying the details of the rendering process, providing simplified visualization application development and cross-vendor portability to diverse rendering engines, including those using state-of-the-art ray tracing.
Enhancing Evaluation of Room Scale VR Studies to POI Visualizations in Minimaps
(The Eurographics Association, 2022) Ajdadilish, Batoul; Kohl, Steffi; Schröder, Kay; Krone, Michael; Lenti, Simone; Schmidt, Johanna
Understanding and evaluating user behavior in virtual reality environments is challenging for researchers. Stereoscopic perception is highly dependent on the point of view, so it is necessary to account for multiple spatial positions. Robust tools and methods to analyze these spatio-temporal data are lacking. We propose a design solution for spatio-temporal data visualization for room-scale VR studies. Our result is a top-down minimap that plots 3D point of interest coordinates of room-scale virtual reality environments to a 2D visualization. The video stream from the head mount display is next to the minimap showing the top-down view of the scene, reflecting the visual stimuli that were perceivable by the user. Both views are linked such that replaying the user session is synchronized in time. The minimap enables researchers to review and replay the recorded user session for in-depth study, allowing them to gain insightful information about users' behavior in virtual environments.
Visual Queries on Bipartite Multivariate Dynamic Social Networks
(The Eurographics Association, 2022) Pister, Alexis; Prieur, Christophe; Fekete, Jean-Daniel; Krone, Michael; Lenti, Simone; Schmidt, Johanna
We present a new system for the visualization, visual query, and comparison of social networks modeled as bipartite multivariate dynamic graphs. Historians and sociologists study collections of documents -such as marriage acts, birth certificates, or contracts-from a region and time of interest and can transform them into Bipartite Multivariate Dynamic Social Networks to follow an in-depth analysis. However, few social network analysis tools are designed for this type of network, and existing ones rarely provide enough interactions to answer complex sociological questions.We present a new visual analytics system allowing to explore this type of network. In particular, we designed a visual query system specifically for this data model, allowing social scientists to easily apply filters on the topology and attributes of a network. We demonstrate how the interface coupled with the query system can be used to answer sociological questions with one real-world use case based on construction contracts.
Visually Explaining Publication Ranks in Citation-based Literature Search with PURE Suggest
(The Eurographics Association, 2022) Beck, Fabian; Krause, Cedric; Krone, Michael; Lenti, Simone; Schmidt, Johanna
Tracing citation links helps retrieve related publications. While most tools only allow the user to follow the citations of a single publication, some approaches support jointly analyzing the citations of a set of publications. Along similar lines, PURE suggest provides a detailed visual explanation of the ranking of suggested publications. The ranking is based on a score that combines citation numbers with keyword matching and is shown as a glyph for each publication. A citation network component references this glyph and visually embeds it into a timeline and cluster visualization.
EuroVis 2022 Posters: Frontmatter
(The Eurographics Association, 2022) Krone, Michael; Lenti, Simone; Schmidt, Johanna; Krone, Michael; Lenti, Simone; Schmidt, Johanna
Automatic Segmentation of Tooth Images: Optimization of Multi-parameter Image Processing Workflow
(The Eurographics Association, 2022) Bressan Fogalli, Giovani; Line, Sérgio Roberto Peres; Baum, Daniel; Krone, Michael; Lenti, Simone; Schmidt, Johanna
The development of specific algorithms in image processing are usually related to dataset characteristics. Those characteristics will influence the number of instructions required to solve a problem. Normally, the more complex a set of instructions is, the more parameters need to be set. Dealing with such degrees of freedom, sometimes leading to subjective decision making, is time-consuming and frequently leads to errors or sub-optimal results of the developed model. Here, we deal with a model for segmentation of masks of tooth images containing a pattern of bands called Hunter-Schreger Bands (HSB). They appear on tooth surface when lit from the side. This segmentation process is only one step of a pipeline whose overall goal is human biometric identification to be used, e.g., in forensics. The segmentation algorithm, which exploits the anisotropy of the image, uses several parameters and choosing the optimal combination of them is challenging. The aim of this work was to utilize visual data analysis tools to optimize the chosen parameters and to understand their influence on the performance of the algorithm. Our results reveal that a slightly better combination of parameter values can be found starting from the experimentally determined initial parameters. This approach can be repeatedly performed to achieve even better parameterizations. To more deeply understand the influence of the parameters on the final result, more sophisticated visual interaction tools will be explored in future work.
Visualizing the Evolution of Multi-agent Game-playing Behaviors
(The Eurographics Association, 2022) Agarwal, Shivam; Latif, Shahid; Rothweiler, Aristide; Beck, Fabian; Krone, Michael; Lenti, Simone; Schmidt, Johanna
Analyzing the training evolution of AI agents in a multi-agent environment helps to understand changes in learned behaviors, as well as the sequence in which they are learned. We train an existing Pommerman team from scratch and, at regular intervals, let it battle against another top-performing team. We define thirteen game-specific behaviors and compute their occurrences in 600 matches. To investigate the evolution of these behaviors, we propose a visualization approach and showcase its usefulness in an application example.
Context Specific Visualizations on Smartwatches
(The Eurographics Association, 2022) Islam, Alaul; Blascheck, Tanja; Isenberg, Petra; Krone, Michael; Lenti, Simone; Schmidt, Johanna
We present an analysis of the results of a full-day context-specific ideation exercise for smartwatch visualizations. Participants of the exercise created 34 sketches during a sightseeing activity. Our analysis of these sketches showed where visualizations could be applied and shown, what information needs they could target, and how data could be represented in the sightseeing context.
Parameter Sensitivity and Uncertainty Visualization in DTI
(The Eurographics Association, 2022) Siddiqui, Faizan; Höllt, Thomas; Vilanova, Anna; Krone, Michael; Lenti, Simone; Schmidt, Johanna
Diffusion Tensor Imaging is a powerful technique that provides a unique insight into the complex structure of the brain's white matter. However, several sources of uncertainty limit its widespread use. Data and modeling errors arise due to acquisition noise and modeling transformations. Moreover, the sensitivities of the user-defined parameters and region definitions are not usually evaluated, a small change in these parameters can add large variations in the results. Without showing these uncertainties any visualization of DTI data can potentially be misleading. In our work, we develop a visual analytic tool that provides insight into the accumulated uncertainty in the visualization pipeline. The primary goal of this project is to develop an efficient visualization strategy that will assist the end-user in making critical decisions and make fiber tracking analysis less cumbersome and more reliable, a crucial step towards adoption in the neurosurgical workflow.

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