Uncertainty-aware Visual Analytics - Scope, Oppertunities and Challenges

Abstract

In many applications, Visual Analytics(VA) has developed into a standard tool to ease data access and knowledge generation. Unfortunately, many data sources, used in the VA process, are affected by uncertainty. In addition, the VA cycle itself can introduce uncertainty to the knowledge generation process. The classic VA cycle does not provide a mechanism to handle these sources of uncertainty. In this manuscript, we aim to provide an extended VA cycle that is capable of handling uncertainty by quantification, propagation, and visualization. Different data types and application scenarios that can be handled by such a cycle, examples, and a list of open challenges in the area of uncertainty-aware VA are provided.