USMN has been the industry standard for network alignment for many years. However, it has had its critiques, the largest of which is the solution speed. The Monte Carlo simulation process used for un- certainty modeling by USMN can be slow for large networks and the uncertainty clouds generated by the process require additional storage. It is also difficult to use USMN for further uncertainty propagation or modeling purposes beyond the network solution.
The intent of the Uncertainty Context Manager is to provide an alternative means to use point uncertainties in an optimal manner, using covariance matrix analysis. This mathematical approach increases computation speed and reduces data storage. It has also been developed through a context dependency structure, much like the SA tree bar, that facilitates greater control over the progression of uncertain- ties. It allows a user to build a series of dependent solutions, providing more comprehensive modeling capabilities.