Abstract:Visualization uses computer-supported, interactive, visual representations of (abstract) data to amplify cognition. In recent years data complexity and variability has increased considerably. This is due to new data sources as well as the availability of uncertainty, error and tolerance information. Instead of individual objects entire sets, collections, and ensembles are visually investigated. This raises the need for effective comparative visualization approaches. Visual data science and computational sciences provide vast amounts of digital variations of a phenomenon which can be explored through superposition, juxtaposition and explicit difference encoding. A few examples of comparative approaches coming from the various areas of visualization, i.e., scientific visualization, information visualization and visual analytics will be treated in more detail. Comparison and visualization techniques are helpful to carry out parameter studies for the special application area of non-destructive testing using 3D X-ray computed tomography (3DCT). We discuss multi-image views and an edge explorer for comparing and visualizing gray value slices and edges of several datasets simultaneously. Visual steering supports decision making in the presence of alternative scenarios. Multiple, related simulation runs are explored through branching operations. To account for uncertain knowledge about the input parameters, visual reasoning employs entire parameter distributions. This can lead to an uncertainty-aware exploration of (continuous) parameter spaces. VAICo, i.e., Visual Analysis for Image Comparison, depicts differences and similarities in large sets of images. It preserves contextual information, but also allows the user a detailed analysis of subtle variations. The approach identifies local changes and applies cluster analysis techniques to embed them in a hierarchy. The results of this comparison process are then presented in an interactive web application which enables users to rapidly explore the space of differences and drill-down on particular features. Given the amplified data variability, comparative visualization techniques are likely to gain in importance in the future. Research challenges, directions, and issues concerning this innovative area are sketched at the end of the talk.