DSpace Repository :: Browsing by Author "Ertl, Thomas"

Browsing by Author "Ertl, Thomas"

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Air Quality Temporal Analyser: Interactive Temporal Analyses with Visual Predictive Assessments
(The Eurographics Association, 2021) Harbola, Shubhi; Koch, Steffen; Ertl, Thomas; Coors, Volker; Dutta, Soumya and Feige, Kathrin and Rink, Karsten and Zeckzer, Dirk
This work presents Air Quality Temporal Analyser (AQTA), an interactive system to support visual analyses of air quality data with time. This interactive AQTA allows the seamless integration of predictive models and detailed patterns analyses. While previous approaches lack predictive air quality options, this interface provides back-and-forth dialogue with the designed multiple Machine Learning (ML) models and comparisons for better visual predictive assessments. These models can be dynamically selected in real-time, and the user could visually compare the results in different time conditions for chosen parameters. Moreover, AQTA provides data selection, display, visualisation of past, present, future (prediction) and correlation structure among air parameters, highlighting the predictive models effectiveness. AQTA has been evaluated using Stuttgart (Germany) city air pollutants, i:e:, Particular Matter (PM) PM10, Nitrogen Oxide (NO), Nitrogen Dioxide (NO2), and Ozone (O3) and meteorological parameters like pressure, temperature, wind and humidity. The initial findings are presented that corroborate the city’'s COVID lockdown (year 2020) conditions and sudden changes in patterns, highlighting the improvements in the pollutants concentrations. AQTA, thus, successfully discovers temporal relationships among complex air quality data, interactively in different time frames, by harnessing the user's knowledge of factors influencing the past, present and future behavior, with the aid of ML models. Further, this study also reveals that the decrease in the concentration of one pollutant does not ensure that the surrounding air quality would improve as other factors are interrelated.
Analyzing Protein Similarity by Clustering Molecular Surface Maps
(The Eurographics Association, 2020) Schatz, Karsten; Frieß, Florian; Schäfer, Marco; Ertl, Thomas; Krone, Michael; Kozlíková, Barbora and Krone, Michael and Smit, Noeska and Nieselt, Kay and Raidou, Renata Georgia
Many biochemical and biomedical applications like protein engineering or drug design are concerned with finding functionally similar proteins, however, this remains to be a challenging task. We present a new imaged-based approach for identifying and visually comparing proteins with similar function that builds on the hierarchical clustering of Molecular Surface Maps. Such maps are two-dimensional representations of complex molecular surfaces and can be used to visualize the topology and different physico-chemical properties of proteins. Our method is based on the idea that visually similar maps also imply a similarity in the function of the mapped proteins. To determine map similarity we compute descriptive feature vectors using image moments, color moments, or a Convolutional Neural Network and use them for a hierarchical clustering of the maps. We show that image similarity as found by our clustering corresponds to functional similarity of mapped proteins by comparing our results to the BRENDA database, which provides a hierarchical function-based annotation of enzymes. We also compare our results to the TM-score, which is a similarity value for pairs of arbitrary proteins. Our visualization prototype supports the entire workflow from map generation, similarity computing to clustering and can be used to interactively explore and analyze the results.
Interactive CPU-based Ray Tracing of Solvent Excluded Surfaces
(The Eurographics Association, 2019) Rau, Tobias; Zahn, Sebastian; Krone, Michael; Reina, Guido; Ertl, Thomas; Kozlíková, Barbora and Linsen, Lars and Vázquez, Pere-Pau and Lawonn, Kai and Raidou, Renata Georgia
Depictions of molecular surfaces such as the Solvent Excluded Surface (SES) can provide crucial insight into functional molecular properties, such as the molecule's potential to react. The interactive visualization of single and multiple molecule surfaces is essential for the data analysis by domain experts. Nowadays, the SES can be rendered at high frame rates using shader-based ray casting on the GPU. However, rendering large molecules or larger molecule complexes requires large amounts of memory that has the potential to exceed the memory limitations of current hardware. Here we show that rendering using CPU ray tracing also reaches interactive frame rates without hard limitations to memory. In our results large molecule complexes can be rendered with only the precomputation of each individual SES, and no further involved representation or transformation. Additionally, we provide advanced visualization techniques like ambient occlusion opacity mapping (AOOM) to enhance the comprehensibility of the molecular structure. CPU ray tracing not only provides very high image quality and global illumination, which is beneficial for the perception of spatial structures, it also opens up the possibility to visualize larger data sets and to render on any HPC cluster. Our results demonstrate that simple instancing of geometry keeps the memory consumption for rendering large molecule complexes low, so the examination of much larger data is also possible.
Interactive Hierarchical Quote Extraction for Content Insights
(The Eurographics Association, 2019) Knittel, Johannes; Koch, Steffen; Ertl, Thomas; Madeiras Pereira, João and Raidou, Renata Georgia
This work presents a new approach to visually summarize large micro-document collections such as tweets. We extract frequent patterns of phrases as shortened quotes to present analysts an overview of popular snippets and statements, enabling more specific insights into large text collections compared to keyword-based visualizations. In our hierarchical structure, each quote can be the starting point to extract more fine-grained patterns on a subset of sentences that match the parent pattern. We show that our approach is scalable by applying it to millions of tweets.
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.
Putting Annotations to the Test
(The Eurographics Association, 2023) Becker, Franziska; Ertl, Thomas; Gillmann, Christina; Krone, Michael; Lenti, Simone
When users work with interactive visualization systems, they get to see more accessible representations of raw data and interact with these, e.g. by filtering the data or modifying the visualization parameters like color. Internal representations such as hunches about trends, outliers or data points of interest, relationships and more are usually not visualized and integrated in systems, i.e. they are not externalized. In addition, how externalizations in visualization systems can affect users in terms of memory, post-analysis recall, speed or analysis quality is not yet completely understood. We present a visualization-agnostic externalization framework that lets users annotate visualizations, automatically connect them to related data and store them for later retrieval. In addition, we conducted a pilot study to test the framework's usability and users' recall of exploratory analysis results. In two tasks, one without and one with annotation features available, we asked participants to answer a question with the help of visualizations and report their findings with concrete examples afterwards. Qualitative analysis of the summaries showed that there are only minor differences in terms of detail or completeness, which we suspect is due to the short task time and consequently more shallow analyses made by participants. We discuss how to improve our framework's usability and modify our study design for future research to gain more insight into externalization effects on post-analysis recall.
Visual Analysis of Two‐Phase Flow Displacement Processes in Porous Media
(© 2022 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2022) Frey, Steffen; Scheller, Stefan; Karadimitriou, Nikolaos; Lee, Dongwon; Reina, Guido; Steeb, Holger; Ertl, Thomas; Hauser, Helwig and Alliez, Pierre
We developed a new visualization approach to gain a better understanding of the displacement of one fluid phase by another in porous media. This is based on a recent experimental parameter study with varying capillary numbers and viscosity ratios. We analyse the temporal evolution of characteristic values in this two‐phase flow scenario and discuss how to directly compare experiments across different temporal scales. To enable spatio‐temporal analysis, we introduce a new abstract visual representation showing which paths through the porous medium were occupied and for how long. These transport networks allow to assess the impact of different acting forces and they are designed to yield expressive comparability and linking to the experimental parameter space both supported by additional visual cues. This joint work of porous media experts and visualization researchers yields new insights regarding two‐phase flow on the microscale, and our visualization approach contributes towards the overarching goal of the domain scientists to characterize porous media flow based on capillary numbers and viscosity ratios.
Visual Representation of Region Transitions in Multi-dimensional Parameter Spaces
(The Eurographics Association, 2019) Fernandes, Oliver; Frey, Steffen; Reina, Guido; Ertl, Thomas; Agus, Marco and Corsini, Massimiliano and Pintus, Ruggero
We propose a novel visual representation of transitions between homogeneous regions in multi-dimensional parameter space. While our approach is generally applicable for the analysis of arbitrary continuous parameter spaces, we particularly focus on scientific applications, like physical variables in simulation ensembles. To generate our representation, we use unsupervised learning to cluster the ensemble members according to their mutual similarity. In doing this, clusters are sorted such that similar clusters are located next to each other. We then further partition the clusters into connected regions with respect to their location in parameter space. In the visualization, the resulting regions are represented as glyphs in a matrix, indicating parameter changes which induce a transition to another region. To unambiguously associate a change of data characteristics to a single parameter, we specifically isolate changes by dimension. With this, our representation provides an intuitive visualization of the parameter transitions that influence the outcome of the underlying simulation or measurement. We demonstrate the generality and utility of our approach on diverse types of data, namely simulations from the field of computational fluid dynamics and thermodynamics, as well as an ensemble of raycasting performance data.
Visualizing Temporal-Thematic Patterns in Text Collections
(The Eurographics Association, 2021) Knabben, Moritz; Baumann, Martin; Blascheck, Tanja; Ertl, Thomas; Koch, Steffen; Andres, Bjoern and Campen, Marcel and Sedlmair, Michael
Visualizing the temporal evolution of texts is relevant for many domains that seek to gain insight from text repositories. However, existing visualization methods for text collections do not show fine-grained temporal-thematic patterns. Therefore, we developed and analyzed a new visualization method that aims at uncovering such patterns. Specifically, we project texts to one dimension, which allows positioning texts in a 2D diagram of projection space and time. For projection, we employed two manifold learning algorithms: the self-organizing map (SOM) and UMAP. To assess the utility of our method, we experimented with real-world datasets and discuss the resulting visualizations. We find our method facilitates relating patterns and extracting associated texts beyond what is possible with previous techniques. We also conducted interviews with historians to show that our prototypical system supports domain experts in their analysis tasks.
Visually Comparing Rendering Performance from Multiple Perspectives
(The Eurographics Association, 2022) Tarner, Hagen; Bruder, Valentin; Frey, Steffen; Ertl, Thomas; Beck, Fabian; Bender, Jan; Botsch, Mario; Keim, Daniel A.
Evaluation of rendering performance is crucial when selecting or developing algorithms, but challenging as performance can largely differ across a set of selected scenarios. Despite this, performance metrics are often reported and compared in a highly aggregated way. In this paper we suggest a more fine-grained approach for the evaluation of rendering performance, taking into account multiple perspectives on the scenario: camera position and orientation along different paths, rendering algorithms, image resolution, and hardware. The approach comprises a visual analysis system that shows and contrasts the data from these perspectives. The users can explore combinations of perspectives and gain insight into the performance characteristics of several rendering algorithms. A stylized representation of the camera path provides a base layout for arranging the multivariate performance data as radar charts, each comparing the same set of rendering algorithms while linking the performance data with the rendered images. To showcase our approach, we analyze two types of scientific visualization benchmarks.
Voronoi-Based Foveated Volume Rendering
(The Eurographics Association, 2019) Bruder, Valentin; Schulz, Christoph; Bauer, Ruben; Frey, Steffen; Weiskopf, Daniel; Ertl, Thomas; Johansson, Jimmy and Sadlo, Filip and Marai, G. Elisabeta
Foveal vision is located in the center of the field of view with a rich impression of detail and color, whereas peripheral vision occurs on the side with more fuzzy and colorless perception. This visual acuity fall-off can be used to achieve higher frame rates by adapting rendering quality to the human visual system. Volume raycasting has unique characteristics, preventing a direct transfer of many traditional foveated rendering techniques. We present an approach that utilizes the visual acuity fall-off to accelerate volume rendering based on Linde-Buzo-Gray sampling and natural neighbor interpolation. First, we measure gaze using a stationary 1200 Hz eye-tracking system. Then, we adapt our sampling and reconstruction strategy to that gaze. Finally, we apply a temporal smoothing filter to attenuate undersampling artifacts since peripheral vision is particularly sensitive to contrast changes and movement. Our approach substantially improves rendering performance with barely perceptible changes in visual quality. We demonstrate the usefulness of our approach through performance measurements on various data sets.

Browsing by Author "Ertl, Thomas"

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