Relations between entities are represented in hierarchies and networks. Visualizations of these structures facilitate the exploration of complex relational information such as can be found in social networks or modular software systems. Our research focuses on the scalable representation of hierarchies and networks, in particular, considering their dynamic changes and differences between variants of the same entities.

Visualizations at the size of a word allow a close integration of text and data. We investigate different scenarios to enrich natural language texts and source code. For example, we use of such embeddings to communicate bibliographic data or report findings of eye tracking studies. A particular challenge and opportunity is to establish an interactive interplay between text and word-sized visualizations.

Mixing visualizations with text in interactive documents promises to produce self-explanatory, expressive data representations. We investigate how automatic methods to generate text from data can enrich the visual representations and how the two representations can best be interactively linked for a close integration.

Movement creates data in space and time, with specific events attached. We provide visual analysis solutions for such data, focusing on the integration of various data streams and abstracting the low-level data to high-level, interpretable patterns. Specific examples we study are the analysis of eye movement data and the behavior of virtual agents and human users.