Research topics

An overview of our research work

A group of researchers sits and stands in front of an desktop computer. You can see various colourful data visualizations on the computer screen.
The analysis of complex and big data can be improved greatly by data visualizations.

High-dimensional time-based data

High-dimensional time-based data, such as those generated in Internet-of-Things (IoT) and Industry 4.0 applications, pose many challenges to research due to their sheer volume and size. We develop new visualization techniques or make existing ones more effective to make this data visible and readable.

Using augmented reality, a 3D hydroelectric turbine is projected into an office space.
Immersive analytics detaches the images from the screen: Here you can see a hydropower turbine in augmented reality.

Immersive analytics

In the field of immersive analytics, we are looking at how we can think further about analysis processes and detach them from the conventional desktop computer. In order to build more efficient bridges between the user and the data, we are primarily asking ourselves technical questions: In what ways can data be displayed? How can interaction with data beyond keyboard and computer mouse still work and for which application purposes does this make sense?

More and more companies are turning to web or cloud-based solutions for their interactive data analysis systems.

Web and cloud-based solutions

As more and more data accumulates in the industry, which is also more frequently stored in a centralized manner nowadays, the importance of web and cloud-based solutions continues to grow. Such solutions are also convenient for users: everything is in one place and, at best, accessible through a web browser (which is always available). At VRVis we create real solutions for the industry and optimize network traffic or research particularly user-friendly interaction systems.

The researchers of the Visual Analytics Group with laptop and VRVis banner.
At VRVis, a multi-member research team explores new visual analytics solutions.

Trustworthy visualizations

In order to be able to deal with different problems adequately, we at VRVis are researching different visual methods that can be integrated into existing visual analytics tools. By equipping visual analytics with statistical control mechanisms, we contribute to the design of "trustworthy" visualizations.

On display are several interactive data visualization dashboards that visualize data from VRVis partner AVL.
Simulation and web-based interactive visualizations are relevant for industries that routinely need to work out large amounts of data.

Visual analytics for ensemble simulations

Since 2005, we have been working intensively on future-oriented applications of visual analytics for ensemble simulations. In the context of the cooperation with our industry partner AVL, we were able to develop innovative visualization methods and completely new explorative analysis methods for simulation results in the automotive industry, which can also be translated to other industries and research fields that work with a large number of data and parameters.

A researcher sits in front of a desktop computer with two screens displaying colorful data visualizations, and two other VRVis researchers stand next to him. One researcher points to the screen.
The research group Interactive Visualization works together with partner AVL on complex simulation ensembles or the visualization of simulation results.

Visual parameter space analyses

Visual parameter space analyses are of great importance when working with simulation models, since a large amount of available input and output data has to be made understandable in all its complexity. At VRVis, we develop tools to accelerate simulation processes by and with these analyses: e.g. by using the visual analysis of the respective parameters to provide information in advance about the meaningfulness or success of the simulation result.

A researcher points with a pen to a screen where a brain data platform can be seen.
Data-driven life science and biotechnology research relies on methods of organization, integration, and analysis to evaluate, for example, multi-omics and clinical data, data from behavioral experiments, or image data from state-of-the-art imaging techniques.

Visual analytic methods for big data exploration

Modern imaging techniques help to better understand biological processes using massive amounts of data. In the life sciences, visual-analytical methods for exploring large data volumes can therefore help to improve the understanding of data and optimize analysis workflows. In this context, we not only take care of the design of applications that allow the most effective answering of scientific questions, but also of the associated data preparation, statistical analysis and indexing, as well as the development of high-performance data structures.

Application examples

The back of a man and the back of a woman's head looking at a visualization depicting various heat zones of a city in shades of red.
Climate data, especially in urban areas, is above all very large, very varied - and highly complex. Visual analytics creates an ideal basis for experts to visually bring together this data and thus help make cities fit for the climatic changes of the future. (c) PID / David Bohmann

Visual analytics of climate data

The role of visual analytics in the analysis of climate and weather data will be an even greater one in the future, as it is the only way to capture the complex interrelationships in their entirety and use them for better climate change adaptation solutions. VRVis has several experts specialized in climate and environmental data, for example working with Typical Meterological Year (TMY) time series or developing visual cockpits for biosolar technology performance assessment.

Bild einer Fischwanderhilfe und eines Staudamms mit Landschaft
Modern hydropower plants produce large amounts of sensor data that must be continuously evaluated: this is where visual analytics comes into play. (c) Verbund

Visual analytics for energy data

Austria has relied heavily on hydropower for many years, using renewable energy for a climate-friendly future. Some hydropower plants are older structures and need retrofitting with modern sensors and data analytics to create a modern and sustainable digital hydropower twin. Visual analytics and intuitive visualization dashboards make real-time monitoring and better maintenance possible.

Die Oberfläche einer Lichtplanungssoftware, mit welcher intelligente Lichtplanung vorgenommen werden kann.
Intelligent light planning: with the help of the latest visualizations, all work steps are clearly presented. Alternative solutions are continuously suggested to the user in order to make previously unknown possibilities tangible.

Visual Analytics for interactive lighting concepts

In our research work within the HILITE and Sharc projects, we develop tools and methods that optimize simulation workflows for interactive lighting design. Since the processing of enormous amounts of data of different origin - quality of light sources, angles, spatial situation, etc. - has to be taken into account, we use principles of visual parameter space analysis to massively accelerate the workflow on the one hand and to compute automatically generated suggestions for improvement of lighting situations on the other hand. These research projects emphasize the necessity of the interplay between spatial and abstract data worlds to make such and similar analyses possible in the first place.

Dashboard von VIVID zur Analyse von Patientenkohortenstudien
With the visual analytics application VIVID, we developed a solution together with epidemiologists from the University of Greifswald to detect missing values in clinical cohort studies and determine their character. VIVID also integrates methods to replace missing data and assess their validity.

Visual analytics for life science and medicine

At VRVis we successfully use visual analytics methods to help physicians and scientists explore complex, heterogeneous data in medicine and the life sciences. These data come, for example, from studies on patient cohorts and observations, behavioral data from animal experiments, gene data, but also, for example, from large collections of spatial measurement, image and network data on the brain. Our visual analytics solutions help when initial hypotheses are to be developed based on large amounts of data, but also when there is not enough data to develop meaningful statistical results and models. Examples of our work include a visual analytics framework that supports the search for biomarkers for very rare childhood cancers (Visual Analytics and Data Science for Healthcare and Medical Research and VISIOMICS) and comprehensive data management, data mining and visual analytics solutions for neuroscience, some of which are in use worldwide (Neuroscience - Visual Computing, Data Science and Big Data and Brain*).