Robin Georg Claus Maack

• Effective communication of uncertainty is crucial for informed decision-making in data analysis workflows to estimate the reliability of available data and resulting insights. Here, visual analytics is a modern process combining algorithmic data analysis techniques and interactive visualization to extract insights from datasets. Integrating uncertainty in visual analytics is not trivial, and the growing field of uncertainty-aware visual analytics applications shows the importance of integrating uncertainty information for decision-making. Still, there is no general systematic model and guideline to integrate uncertainty in visual analytics applications to assist application designers. This raised the questions "How to define an uncertainty-aware visual analytics process?" and "Which steps are necessary to map a visual analytics process to an uncertainty-aware visual analytics process?". Accordingly, this thesis addresses these questions in three steps. First, an uncertainty-aware visual analytics process describes all components and transitions when dealing with data containing uncertainty, providing a model to check for compatibility of an application. A guideline for creating a compatible uncertainty-aware application delivers detailed development steps that allow converting existing applications and creating new ones from scratch. At last, multiple uncertainty-aware visual analytics applications are showcased to provide realizations of the uncertainty-aware visual analytics process. Those examples either represent a post hoc mapping to the uncertainty-aware visual analytics process or deliver design decisions for recreating an existing application using the guideline. In the end, the research questions and their solutions are discussed, closing with open challenges and future research directions.