Smart Surrogate Widgets for Direct Volume Manipulation

Interaction is an essential aspect in volume visualization, yet common manipulation tools such as bounding boxes or clipping plane widgets provide rather crude tools as they neglect the complex structure of the underlying data. In this paper, we introduce a novel volume interaction approach based on smart widgets that are automatically placed directly into the data in a visibility-driven manner. By adapting to what the user actually sees, they act as proxies that allow for goal-oriented modifications while still providing an intuitive set of simple operations that is easy to control. In particular, our method is well-suited for direct manipulation scenarios such as touch screens, where traditional user interface elements commonly exhibit limited utility. To evaluate out approach we conducted a qualitative user study with nine participants with various backgrounds.acceptedVersio

Honeycomb Plots: Visual Enhancements for Hexagonal Maps

Aggregation through binning is a commonly used technique for visualizing large, dense, and overplotted two-dimensional data sets. However, aggregation can hide nuanced data-distribution features and complicates the display of multiple data-dependent variables, since color mapping is the primary means of encoding. In this paper, we present novel techniques for enhancing hexplots with spatialization cues while avoiding common disadvantages of three-dimensional visualizations. In particular, we focus on techniques relying on preattentive features that exploit shading and shape cues to emphasize relative value differences. Furthermore, we introduce a novel visual encoding that conveys information about the data distributions or trends within individual tiles. Based on multiple usage examples from different domains and real-world scenarios, we generate expressive visualizations that increase the information content of classic hexplots and validate their effectiveness in a user study.publishedVersio

Wake Shape and Height Profile Measurements in a Concave Open Channel Flow Regarding the Target in DONES

Wakes appearing downstream of disturbances on the surface of a water flow in a concave open channel were examined experimentally. The investigated channel geometry was similar to the liquid lithium target in DONES (Demonstration fusion power plant Oriented NEutron Source). The objective of the measurements was to analyze the effect of a disturbance on the downstream layer thickness. For measuring the height profiles in the channel, an optical measurement system based on laser triangulation was developed. It was shown that the wake of the undisturbed flow emerged from the nozzle corner, which was in accordance with analytical solutions. For sufficiently large disturbances at the nozzle edge, the height profiles located downstream showed symmetrical minima and maxima on both sides of the disturbance. The wake depth strongly depended on the diameter and penetration depth of the disturbance, as well as the circumferential position in the channel, which yields to a critical wake depth of one millimeter for the lithium target in DONES

The News Angler Project: Exploring the Next Generation of Journalistic Knowledge Platforms

The News Angler project aims to support journalists in finding new and unexpected connections and angles in the news. The project therefore explores how recent artificial intelligence (AI) techniques — such as knowledge graphs, natural-language processing (NLP) and machine learning (ML) — can support high-quality journalism that exploits big and open data sources. A central contribution is News Hunter, a series of prototype journalistic knowledge platforms (JKPs)

Smart Surrogate Widgets for Direct Volume Manipulation

Interaction is an essential aspect in volume visualization, yet common manipulation tools such as bounding boxes or clipping plane widgets provide rather crude tools as they neglect the complex structure of the underlying data. In this paper, we introduce a novel volume interaction approach based on smart widgets that are automatically placed directly into the data in a visibility-driven manner. By adapting to what the user actually sees, they act as proxies that allow for goal-oriented modifications while still providing an intuitive set of simple operations that is easy to control. In particular, our method is well-suited for direct manipulation scenarios such as touch screens, where traditional user interface elements commonly exhibit limited utility. To evaluate out approach we conducted a qualitative user study with nine participants with various backgrounds

Honeycomb Plots: Visual Enhancements for Hexagonal Maps

Aggregation through binning is a commonly used technique for visualizing large, dense, and overplotted two-dimensional data sets. However, aggregation can hide nuanced data-distribution features and complicates the display of multiple data-dependent variables, since color mapping is the primary means of encoding. In this paper, we present novel techniques for enhancing hexplots with spatialization cues while avoiding common disadvantages of three-dimensional visualizations. In particular, we focus on techniques relying on preattentive features that exploit shading and shape cues to emphasize relative value differences. Furthermore, we introduce a novel visual encoding that conveys information about the data distributions or trends within individual tiles. Based on multiple usage examples from different domains and real-world scenarios, we generate expressive visualizations that increase the information content of classic hexplots and validate their effectiveness in a user study

Output-Sensitive Filtering of Streaming Volume Data

Real-time volume data acquisition poses substantial challenges for the traditional visualization pipeline where data enhancement is typically seen as a pre-processing step. In the case of 4D ultrasound data, for instance, costly processing operations to reduce noise and to remove artefacts need to be executed for every frame. To enable the use of high-quality filtering operations in such scenarios, we propose an output-sensitive approach to the visualization of streaming volume data. Our method evaluates the potential contribution of all voxels to the final image, allowing us to skip expensive processing operations that have little or no effect on the visualization. As filtering operations modify the data values which may affect the visibility, our main contribution is a fast scheme to predict their maximum effect on the final image. Our approach prioritizes filtering of voxels with high contribution to the final visualization based on a maximal permissible error per pixel. With zero permissible error, the optimized filtering will yield a result that is identical to filtering of the entire volume. We provide a thorough technical evaluation of the approach and demonstrate it on several typical scenarios that require on-the-fly processing

Memento: Localized time‐warping for spatio‐temporal selection

Interaction techniques for temporal data are often focused on affecting the spatial aspects of the data, for instance through the use of transfer functions, camera navigation or clipping planes. However, the temporal aspect of the data interaction is often neglected. The temporal component is either visualized as individual time steps, an animation or a static summary over the temporal domain. When dealing with streaming data, these techniques are unable to cope with the task of re‐viewing an interesting local spatio‐temporal event, while continuing to observe the rest of the feed. We propose a novel technique that allows users to interactively specify areas of interest in the spatio‐temporal domain. By employing a time‐warp function, we are able to slow down time, freeze time or even travel back in time, around spatio‐temporal events of interest. The combination of such a (pre‐defined) time‐warp function and brushing directly in the data to select regions of interest allows for a detailed review of temporally and spatially localized events, while maintaining an overview of the global spatio‐temporal data. We demonstrate the utility of our technique with several usage scenarios