Interactive Visualization of Complex Data

Today's digital world would be unthinkable without complex data sets. Whether in private, business or industrial environments, complex data pro vide the basis for important and critical decisions and determine many processes, some of which are automated. This is often associated with Big Data. However, often only one aspect of the usual Big Data definitions
is sufficient and a human observer can no longer capture the data com pletely and correctly. In this thesis, different approaches are presented in order to master selected challenges in a more effective, efficient and user friendly way. The approaches range from easier pre-processing of data sets for later analysis and the identification of design guidelines of such assis tants, new visualization techniques for presenting uncertainty, extensions of existing visualizations for categorical data, concepts for time-saving se lection methods for subsets of data points and faster navigation and zoom interaction–especially in the web-based area with enormous amounts of data–to new and innovative orientation-based interaction metaphors for
mobile devices as well as stationary working environments. Evaluations and appropriate use case of the individual approaches show the usability also in comparison with state-of-the-art techniques.