Visual analytics systems integrate interactive visualizations and machine learning to enable expert users to solve complex analysis tasks. Applications combine techniques from various fields of research and are consequently not trivial to evaluate. …
Mixed-initiative visual data analysis processes are characterized by the co-adaptation of users and systems over time. As the analysis progresses, both actors – users and systems – gather information, update their analysis behavior, and work on …
Communication consists of both meta-information as well as content. Currently, the automated analysis of such data often focuses either on the network aspects via social network analysis or on the content, utilizing methods from text-mining. However, …
Mixed-initiative visual analytics systems support collaborative human-machine decision-making processes. However, many multiobjective optimization tasks, such as topic model refinement, are highly subjective and context-dependent. Hence, systems need …
Linguistic insight in the form of high-level relationships and rules in text builds the basis of our understanding of language. However, the data-driven generation of such structures often lacks labeled resources that can be used as training data for …
Visual analytics enables the coupling of machine learning models and humans in a tightly integrated workflow, addressing various analysis tasks. Each task poses distinct demands to analysts and decision-makers. In this survey, we focus on one …
The increasing use of artificial intelligence (AI) technologies across application domains has prompted our society to pay closer attention to AI’s trustworthiness, fairness, interpretability, and accountability. In order to foster trust in AI, it is …
We present VisInReport, a visual analytics tool that supports the manual analysis of discourse transcripts and generates reports based on user interaction. As an integral part of scholarly work in the social sciences and humanities, discourse …
Just like the numerous applications of visualization, there are plenty of theoretical arguments for why visualization can aid in knowledge generation and communication. Meanwhile, to date, these arguments have been presented independently, which …
We propose a framework for interactive and explainable machine learning that enables users to (1) understand machine learning models; (2) diagnose model limitations using different explainable AI methods; as well as (3) refine and optimize the …