Robin Georg Claus Maack

"Uncertainty-Aware Visual Analytics Applications: From theory to usage"

Real-world data acquisition processes are usually affected by uncertainty that can lead to huge impacts on decision-making. The uncertainty principle addresses this effect on the data, allowing it to quantify, propagate and communicate uncertainty present in the data. Despite the effort of many authors to address this principle, it is still not fully evaluated and standardized workflows to achieve uncertainty-awareness in Visual Analytics applications are still missing. This work is dedicated to extending the Visual Analytics cycle to include uncertainty in every step of the cycle, giving creators of new applications a guideline for the incorporation of uncertainty. Furthermore, the effectiveness of uncertainty in Visual Analytics applications will be investigated, giving developers indicators on whether they should incorporate uncertainty in their solution. Finally, various example applications from the fields of biochemistry, medicine, topology, and simulation are presented to show the applicability of the developed approach.

Termin als Datei downloaden und in den Kalender importieren.