2022, Jentner, Wolfgang, Sperrle, Fabian, Seebacher, Daniel, Kraus, Matthias, Sevastjanova, Rita, Fischer, Maximilian T., Schlegel, Udo, Streeb, Dirk, Miller, Matthias, Spinner, Thilo, Cakmak, Eren, Sharinghousen, Matthew, Meschenmoser, Philipp, Görtler, Jochen, Deussen, Oliver, Stoffel, Florian, Kabitz, Hans-Joachim, Keim, Daniel A., El-Assady, Mennatallah, Buchmüller, Juri F.

Die COVID-19-Pandemie und ihre rasante Entwicklung innerhalb weniger Wochen stellen völlig neue Anforderungen an die Auswertung von Infektionsstatistiken. CoronaVis stellt interaktive Visualisierungen zur Verfügung, durch die Fallinzidenzen und die Bettenkapazitäten von Intensivstationen (ICUs) in ganz Deutschland analysiert werden können. CoronaVis ist in erster Linie dazu bestimmt, Ärzte, Krisenstäbe und medizinische Entscheidungsträger zu unterstützen und informierte Entscheidungen, zum Beispiel zur Patientenverteilung bei drohender Überlast, zu ermöglichen. CoronaVis skaliert durch flexible Aggregationsmöglichkeiten von der lokalen bis auf die nationale Ebene. Dieser Beitrag stellt die Analysemöglichkeiten von CoronaVis vor und geht näher auf die Leistungsfähigkeit interaktiver Visualisierungen in Hinsicht auf die Unterstützung bei dynamischen Lagen ein.

2016, Buchmüller, Juri F., Stein, Manuel, Jäger, Alexander, Schmidt, Sabrina, Senaratne, Hansi, Janetzko, Halldor

Supervising a large complex environment is quite challenging due to the high amount of data incoming each second and asking for the operators’ attention. We increase the situational awareness by fusing events, tasks, and spatial awareness employing a combination of the visual and the acoustical channel. Our control room setup allows collaborative hypothesis generation and verification in a shared working environment. Global views can be put on each operator’s touch screen for detailed analyses and annotations. All information is shared automatically between all operators.

2021, Wulms, Jules, Buchmüller, Juri F., Meulemans, Wouter, Verbeek, Kevin, Speckmann, Bettina

The availability of devices that track moving objects has led to an explosive growth in trajectory data. When exploring the resulting large trajectory collections, visual summaries are a useful tool to identify time intervals of interest. A typical approach is to represent the spatial positions of the tracked objects at each time step via a one-dimensional ordering; visualizations of such orderings can then be placed in temporal order along a time line. There are two main criteria to assess the quality of the resulting visual summary: spatial quality - how well does the ordering capture the structure of the data at each time step, and stability - how coherent are the orderings over consecutive time steps or temporal ranges?In this paper we introduce a new Stable Principal Component (SPC) method to compute such orderings, which is explicitly parameterized for stability, allowing a trade-off between the spatial quality and stability. We conduct extensive computational experiments that quantitatively compare the orderings produced by ours and other stable dimensionality-reduction methods to various state-of-the-art approaches using a set of well-established quality metrics that capture spatial quality and stability. We conclude that stable dimensionality reduction outperforms existing methods on stability, without sacrificing spatial quality or efficiency; in particular, our new SPC method does so at a fraction of the computational costs.

2011-10, Bertini, Enrico, Buchmüller, Juri F., Fischer, Fabian, Huber, Stephan, Lindemeier, Thomas, Maaß, Fabian, Ramm, Thomas, Regenscheit, Michael, Rohrdantz, Christian, Scheible, Christian, Schreck, Tobias, Sellien, Stephan, Stoffel, Florian, Tautzenberger, Mark, Zieker, Matthias, Keim, Daniel A., Christian Scheible

The task of the VAST 2011 Grand Challenge was to investigate potential terrorist activities and their relation to the spread of an epidemic. Three different data sets were provided as part of three Mini Challenges (MCs). MC 1 was about analyzing geo-tagged microblogging (Twitter) messages to characterize the spread of an epidemic. MC 2 required analyzing threats to a computer network using a situational awareness approach. In MC 3 possible criminal and terrorist activities were to be analyzed based on a collection of news articles. To solve the Grand Challenge, insight from each of the individual MCs had to be integrated appropriately.

2019, Jentner, Wolfgang, Buchmüller, Juri F., Sperrle, Fabian, Sevastjanova, Rita, Spinner, Thilo, Schlegel, Udo, Streeb, Dirk, Schäfer, Hanna

We present N.E.A.T. - a Visual Analytics approach to the collaborative management of large-scale emergencies. N.E.A.T. unifies the analysis and annotation of heterogeneous, uncertainty-afflicted data sources in a single, adjustable screen. Stakeholders can create individual or shared workspaces providing configurable views tailored to the needs of different emergency responders. Within each workspace, annotated findings are automatically shared in real-time for effective collaboration. We illustrate the functionality of the tool and showcase exemplary findings on the St. Himark incident.