UncertML is an XML langauge for describing and exchanging uncertainty UncertML has numerous mechanisms for quantifying uncertainty including Statistics, Realisations and Probability Distributions UncertML provides a concise representation of distributions and makes extensive use of a dictionary system

Uncertainty Markup Language: UncertML

UncertML is a conceptual model and XML encoding designed for encapsulating probabilistic uncertainties. This website contains all the information you will need to start using UncertML to quantify and exchange your data uncertainties. For a detailed description of the models and XML schemata, including use case examples, look no further than the encoding specificaiton. Below are a series of questions and answers to give you a brief insight to UncertML.

What is it?

UncertML is a conceptual model, with accompanying XML schemata, that may be used to quantify and exchange complex uncertainties in data. The interoperable model can be used to describe uncertainty in a variety of ways including:

What can it be used for?

Utilising the XML schemata provides an interoperable framework for exchanging uncertainties. This allows uncertainty to be propogated through processing chains.

Can you give me a brief rundown of the model?

Uncertainty can be quantified in several different ways within UncertML. Below is a rundown of each method for describing uncertainty including the common elements.

Realisations

In some situations you may not fully understand the uncertainties of the data you are working with. Typically, in such a situation you may provide a sample of the data which allows the uncertainties to be described implicitly. Unfortunately, a sufficiently large sample of data is required for calculating the uncertainties, introducing the issue of encapsulating large amounts of data efficiently. The following element is available within UncertML for describing a sample of data through realisations.

Statistics

There is an extensive range of options available in UncertML for describing 'summary statistics'. Such statistics are used to provide a summary of a variable ranging from measures of location (mean, mode, median etc) to measures of dispersion (range, standard deviation, variance etc). While certain statistics do not provide any information about uncertainty they are often used in conjunction with other statistics to provide a concise but detailed summary. The following elements are available:

For a complete breakdown of how these elements can be used to describe various statistics please refer to the encoding specification.

Distributions

When the uncertainties of your data are more clearly understood it may be desirable to describe them through the use of probability distributions. The elements listed below are specifically designed to allow a concise encapsulation of all distributions without sacrificing the simplicity of UncertML.

What is the current status of UncertML - is it an accepted standard?

Unfortunately, UncertML is in the early stages of development so is not therefore an official standard encoding specification. However, there has been a lot of interest in UncertML recently and it is currently being included in the Open Geospatial Consortium's OWS-6 test bed. You can find out about all the latest activity by checking the latest news section.