Reliable Visualization: Verification of Visualization based on Uncertainty Analysis

In order to assess the reliability of computational algorithms it is necessary to consider the uncertainty associated with the results of the computation. This is true for visualization as it is for other computational sciences, and realization of this important concept has led researchers to develop methods for assessing uncertainty in visualization. In this work we propose a framework for verification of visualization by performing an analysis of uncertainty, wherein we consider the uncertainty in the data and how it is propagated through visualization algorithms as well as the contribution to uncertainty from the visualization algorithms themselves. Our solution is to use an automated approach to compute bounded uncertainty based on a novel uncertainty model that captures the various forms of uncertainty, including probabilistic, possibilistic, and fuzzy uncertainty. The proposed model is defined within the context of possibility theory, and to this end we derive a law of propagation for bounded uncertainty that preserves inclusion bounds. The proposed representation and propagation of uncertainty together constitute an automated framework for management of uncertainty in visualization. We propose that such uncertainty-aware visualizations can produce more complete depictions of the data, thereby allowing more reliable conclusions and informed decisions.