Dominik Sacha

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Visual analytics enables us to analyze huge information spaces in order to support complex decision making and data exploration. Humans play a central role in generating knowledge from the snippets of evidence emerging from visual data analysis. Although prior research provides frameworks that generalize this process, their scope is often narrowly focused(More)
—Soccer is one the most popular sports today and also very interesting from an scientific point of view. We present a system for analyzing high-frequency position-based soccer data at various levels of detail, allowing to interactively explore and analyze for movement features and game events. Our Visual Analytics method covers single-player, multi-player(More)
Figure 1. Tangible Data Analysis: The image shows in the background a multi-touch table visualizing a geographic map of the US east coast. A user is exploring census data with the system by using Sifteo Cubes as smart tangibles. ABSTRACT We present a tangible approach for exploring and comparing multi-dimensional data points collaboratively by combining(More)
Visual analytics supports humans in generating knowledge from large and often complex datasets. Evidence is collected, collated and cross-linked with our existing knowledge. In the process, a myriad of analytical and visualisation techniques are employed to generate a visual representation of the data. These often introduce their own uncertainties, in(More)
Dimensionality Reduction (DR) is a core building block in visualizing multidimensional data. For DR techniques to be useful in exploratory data analysis, they need to be adapted to human needs and domain-specific problems, ideally, interactively, and on-the-fly. Many visual analytics systems have already demonstrated the benefits of tightly integrating DR(More)
We present a novel interactive approach for the visual analysis of intonation contours. Audio data are processed algo-rithmically and presented to researchers through interactive visualizations. To this end, we automatically analyze the data using machine learning in order to find groups or patterns. These results are visualized with respect to meta-data.(More)
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