Dominik Sacha

TL;DR: A knowledge generation model for visual analytics is proposed that ties together these diverse frameworks, yet retains previously developed models (e.g., KDD process) to describe individual segments of the overall visual analytic processes.

TL;DR: This paper unpacks the uncertainties that propagate through visual analytics systems, illustrates how human's perceptual and cognitive biases influence the user's awareness of such uncertainties, and how this affects the users' trust building.

TL;DR: This work systematically studied the visual analytics and visualization literature to investigate how analysts interact with automatic DR techniques, and proposes a “human in the loop” process model that provides a general lens for the evaluation of visual interactive DR systems.

TL;DR: This work proposes a conceptual framework that models human interactions with ML components in the VA process, and that puts the central relationship between automated algorithms and interactive visualizations into sharp focus.

TL;DR: This paper reinterprets the traditional VA pipeline to encompass model-development workflows and introduces necessary definitions, rules, syntaxes, and visual notations for formulating VIS4ML and makes use of semantic web technologies for implementing it in the Web Ontology Language (OWL).