Abstract Machine learning (ML) models are nowadays used in complex applications in
various domains, such as medicine, bioinformatics, and other sciences. Due to their black …

There is fast‐growing literature on provenance‐related research, covering aspects such as
its theoretical framework, use cases, and techniques for capturing, visualizing, and …

Visual analytics systems integrate interactive visualizations and machine learning to enable
expert users to solve complex analysis tasks. Applications combine techniques from various …

A typical problem in Visual Analytics (VA) is that users are highly trained experts in their
application domains, but have mostly no experience in using VA systems. Thus, users often …

Smartphone addiction is a serious social problem that necessitates research. To identify
trends in intervention programs for smartphone addiction, distribution of research topics, and …

Co-adaptive guidance aims to enable efficient human-machine collaboration in visual
analytics, as proposed by multiple theoretical frameworks. This paper bridges the gap …

The exploratory visual analysis (EVA) of time series data uses visualization as the main
output medium and input interface for exploring new data. However, for users who lack …

Zero-shot classification is a promising paradigm to solve an applicable problem when the
training classes and test classes are disjoint. Achieving this usually needs experts to …

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 …

Guidance has been proposed as a conceptual framework to understand how mixed‐
initiative visual analytics approaches can actively support users as they solve analytical …