Recent

2017.05.04 The AViDA group now has a blog. Any new news item will from now on go to the blog instead of here.

 

Research 

In 2013, the total amount of data collected by mankind was 4.4 zettabytes. This is projected to rise to 44 zettabytes by 2020 as we are generating 2.5 exabytes every day according to EMC². Managing this vast amount of data is challenging and research areas in data science and big data are one important area in assisting with this task. However, processing data at this rate and analyzing it properly remains a challenging task. Visualization and data analytics involve research areas that aim at providing solutions for this problem.

Visualization as a research area has impacted an enormous number of engineering disciplines. In particular, it helps users get better insight into their data. In many areas, it is almost impossible to reasonably analyze data without an appropriate visualization due to the overwhelming amount of information present in the data. Data sets resulting from simulations or experiments in various application fields, for example computational fluid dynamics (CFD) or medical imaging, have to be analyzed and represented in a way that the user is enabled to easily investigate and explore the data. The Advanced Visual Data Analysis group is devoted to solve some of the visualization and analysis challenges arising from these applications. This includes, for example, large-scale visualization and analysis using feature detection and image processing techniques. Examples of our work can be found in the projects section.

As the area of visualization expands and the lines get blurred, the AViDA group extends their research areas into multiple additional areas beyond the ones listed above. These include virtual environments which is supported by the vast array of immersive display systems available in the Appenzeller Visualization Laboratory. We have available a variety of different frameworks for creating such virtual environments which can, for example, be used for training or experimentation. Similarly, these frameworks support the creation of highly interactive data visualization content.

Additional research areas include topics such as image processing, including but not limited to medical areas, information visualization, parallel processing and high-performance computing, and general purpose GPU programming (GPGPU).