Scalable Comparative Visualization (PhD Test Talk)

The comparison of two or more objects is getting a more and more important task in data analysis. Visualization systems increasingly 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, allowing the user to place objects that should be compared in an appropriate context, supports comparison tasks in a very intuitive way. 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 support data comparison. Techniques from this research field can be used to compare up to two objects against each other, but often reach their limits if a multitude of objects (i.e., 100 or more) have to be compared. Large datasets that contain a lot of individual, but related, datasets that show slightly different characteristics, may be called ensembles. The individual datasets being part of an ensemble may be called the 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, which shows a great need for ensemble visualization techniques. Ensembles can either be analyzed in a feature-based, or in a location-based way. In the case of 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.