A Workflow to Systematically Design Uncertainty-Aware Visual Analytics Applications

Abstract

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, feeding them back into the data. This enhances the data 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 a variety of uncertainties that can interfere with the user decision-making process. Unfortunately, there is no methodical description and handling tool to systematically include uncertainty in VA. We provide a unified workflow to transform the classic VA cycle into an uncertainty-aware Visual Analytics (UAVA) cycle consisting of five steps. Three real-world applications represent examples of the implementation of the UAVA cycle and the described workflow to prove its usability.