Designing decision trees requires both domain knowledge and knowledge of the purpose of the model, which is why this process is very difficult to automate. TreePOD successfully takes a new approach and automatically generates several decision tree candidates with different parameter settings, allowing the most appropriate decision tree to be selected. TreePOD can be used for a variety of tasks and can also be integrated into existing workflows for creating and selecting decision trees. Click here to watch a video about TreePOD.
WeightLifter is a novel, interactive visualization technique that facilitates the exploration of weighting spaces with up to ten criteria. Our technique makes it possible to better understand the sensitivity of a decision to changes in weights, to efficiently locate weight ranges where a particular solution ranks high, and to filter out solutions that are not ranked high enough for a plausible combination of weights. Click here to watch a video about Weightlifter.
Intelligent simulations to improve interactive lighting concepts
In our research work within the HILITE and Sharc projects we develop tools and methods that optimize simulation processes for interactive lighting design. Since the processing of enormous amounts of data from different sources - quality of light sources, angles, spatial situation, etc. - must be taken into account, we use principles of visual parameter space analysis to accelerate the workflow massively and to calculate automatically generated suggestions for improvement of lighting situations. Such research projects emphasize the necessity of the interaction between spatial and abstract data worlds to make such and similar analyses possible in the first place.
We successfully apply Visual Analytics methods to help physicians and scientists to explore complex, heterogeneous data in the fields of Medicine and Life Sciences. These data come, for example, from studies on patient cohorts and observations, behavioral data from animal experiments, genetic 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 need to be developed based on large amounts of data, but also when there is not enough data available 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 cancers in children (see also: Visual Analytics and Data Science for the Healthcare System and Medical Research and our research project VISIOMICS) and comprehensive data management, data mining and Visual Analytics solutions for the Neurosciences, some of which are used worldwide (see also: Neuroscience - Visual Computing, Data Science and Big Data and Brain*).
Training AI algorithms requires a great amount of data. However, raw data often contains sensitive information. Homomorphic encryption offers a solution for secure machine learning - with protected sensitive data.
Renewable energies, such as hydropower, are an essential backbone for a climate-friendly future. To enable better real-time monitoring and thus maintenance of hydropower infrastructure, innovative digital hydropower twins are being developed in this project.
The Rail4Future project is focusing on the design of a digital rail system for the future. To this end, a novel and fully virtual validation platform for large-scale simulations of entire rail lines is being developed to increase the efficiency of existing rail infrastructure.
The aim of the application project IVC Multi is to research novel intelligent visual computing methods supporting decision-making in automotive industry, medicine, and life sciences based on ensembles of heterogeneous, multi-scale and/or multi-temporal data.
The strategic project of the area is the organizational and scientific hub for the realization of the area-wide intelligent visual computing approach for analytics and modelling based on ensembles of dense grid-based data, derived data, and digital embedding.
The primary goal of project INGRESS is to accelerate and improve the process of data scientists working with Industry 4.0 and Internet of Things (IoT) data, by enabling a closer integration of visual analysis into the existing workflows.
The RAILING project deals with the research and development of interactive, scalable and trust-building visualization and analysis tools for the exploration of time-dependent and complex data.
Within the project Larvalbrain 2.0, a dynamic multi-scale multi-level atlas and data collection of structural, molecular, physiological, and behavioral results of Drosophila melanogaster larvae will be established.
To take full advantage of data from automotive simulations and measurements, we combine interactive and automatic Visual Computing methods to find intuitive, efficient and effective solutions that are applicable in the daily routine of engineers.
The goal of the applied research project En2VA (“Visual Analytics for Energy and Engineering Applications”) is to increase the efficiency and the quality of advanced analytics for high-dimensional data from manufacturing, engineering, and the energy sector.
Novel visual analysis technologies for high-dimensional data in automotive engineering, industrial manufacturing and the energy sector
Seamless visual analysis of data involving 3D geometry, relational information and multivariate attributes.
DEXHELPP develops new methods to support analysis, planning and control in health care by combining decision analysis, data security, data management, statistics, mathematical modelling, simulation and visual analysis.
Visual Analytics for Modeling and Simulation: Improvement of simulation setup and design scenarios with tools and methods of Visual Analytics.