| dc.description.abstract | The comparison of two or more objects is getting an increasingly important task in data analysis.
Visualization systems successively have to move from representing one phenomenon to allowing
users to analyze several datasets at once. Visualization systems can support the users in several
ways. Firstly, comparison tasks can be supported in a very intuitive way by allowing users to
place objects that should be compared in an appropriate context. Secondly, visualization systems
can explicitly compute differences among the datasets and present the results to the user. In
comparative visualization, researchers are working on new approaches for computer-supported
techniques that provide data comparison functionality. Techniques from this research field can
be used to compare two objects with each other, but often reach their limits if a multitude of
objects (i.e., 100 or more) have to be compared. Large data collections that contain a lot of
individual, but related, datasets with slightly different characteristics can be called ensembles. The
individual datasets being part of an ensemble are called the ensemble members. Ensembles have
been created in the simulation domain, especially for weather and climate research, for already
quite some time. These domains were greatly driving the development of ensemble visualization
techniques. Due to the availability of affordable computing resources and the multitude of different
analysis algorithms (e.g., for segmentation), other domains nowadays also face similar problems.
All together, this shows a great need for ensemble visualization techniques in various domains.
Ensembles can either be analyzed in a feature-based or in a location-based way. In the case of
a location-based analysis, the ensemble members are compared based on certain spatial data
positions of interest. For such an analysis, local selection and analysis techniques for ensembles
are needed.
In the course of this thesis different visual analytics techniques for the comparative visualization
of datasets have been researched. A special focus has been set on providing scalable techniques,
which makes them also suitable for ensemble datasets. The proposed techniques operate on
different dataset types in 2D and 3D. In the first part of the thesis, a visual analytics approach for
the analysis of 2D image datasets is introduced. The technique analyzes localized differences in
2D images. The approach not only identifies differences in the data, but also provides a technique
to quickly find out what the differences are, and judge upon the underlying data. This way patterns
can be found in the data, and outliers can be identified very quickly. As a second part of the thesis,
a scalable application for the comparison of several similar 3D mesh datasets is described. Such
meshes may be, for example, created by point-cloud reconstruction algorithms, using different
parameter settings. Similar to the proposed technique for the comparison of 2D images, this
application is also scalable to a large number of individual datasets. The application enables the automatic comparison of the meshes, searches interesting regions in the data, and allows users to
also concentrate on local regions of interest. The analysis of the local regions is in this case done
in 3D. The application provides the possibility to arrange local regions in a parallel coordinates
plot. The regions are represented by the axes in the plot, and the input meshes are depicted as
polylines. This way it can be very quickly spotted whether meshes produce good/bad results
in a certain local region. In the third and last part of the thesis, a technique for the interactive
analysis of local regions in a volume ensemble dataset is introduced. Users can pick regions of
interest, and these regions can be arranged in a graph according to their similarity. The graph can
then be used to detect similar regions with a similar data distribution within the ensemble, and to
compare individual ensemble members against the rest of the ensemble. All proposed techniques
and applications have been tested with real-world datasets from different domains. The results
clearly show the usefulness of the techniques for the comparative analysis of ensembles. | en_US |
