A workflow to systematically design uncertainty-aware visual analytics applications

Abstract

Visual analytics (VA) is a paradigm for insight generation by using visual analysis techniques and automated reasoning by transforming data into hypotheses and visualization to extract new insights. The insights are fed back into the data to enhance it until the desired insight is found. Many applications use this principle to provide meaningful mechanisms to assist decision-makers in achieving their goals. This process can be affected by various uncertainties that can interfere with the user decision-making process. Currently, there are no methodical description and handling tool to include uncertainty in VA systematically. We provide a unified workflow to transform the classic VA cycle into an uncertainty-aware visual analytics (UAVA) cycle consisting of five steps. To prove its usability, three real-world applications represent examples of the UAVA cycle implementation and the described workflow.