Habilitation Theses:

K. Matkovic:
"Interactive Visual Analysis of Multi-Parameter Scientific Data";
Institut für Computergraphik und Algorithmen, 2015.

English abstract:
Increasing complexity and a large number of control parameters make the design and understanding of modern engineering systems impossible without simulation. Advances in simulation technology and the ability to run multiple simulations with different sets of parameters pose new challenges for analysis techniques. The resulting data is often heterogeneous. A single data point does not contain scalars or vectors only, as usual. Instead, a single data point contains scalars, time series, and other types of mappings. Such a data model is common in many domains. Interactive visual analysis utilizes a tight feedback loop of computation/visualization and user interaction to facilitate knowledge discovery in complex datasets.

Our research extends the visual analysis technology to challenging heterogeneous data, in particular to a combination of multivariate data and more complex data types, such as functions, for example. Furthermore, we focus on developing a structured model for interactive visual analysis which supports a synergetic combination of user interaction and computational analysis.

The concept of height surfaces and function graphs is a proven and well developed mechanism for the analysis of a single mapping. The state of the art when a set of such mappings is analyzed suggested a use of different descriptors or aggregates in the analysis. Our research makes it possible to analyze a whole set of mappings (function graphs, or height surfaces, for example) while keeping the original data. We advance the interactive visual analysis to cope with complex scientific data. Most of the analysis techniques consider the data as a static source. Such an approach often hinders the analysis. We introduce a concept of interactive visual steering for simulation ensembles. We link the data generation and data exploration and analysis tasks in a single workflow. This makes it possible to tune and optimize complex systems having high dimensional parameter space and complex outputs.

Electronic version of the publication:

Created from the Publication Database of the Vienna University of Technology.