Reduction of uncertainties through Data Model Integration (DMI)
Application of techniques for data model integration (DMI) are
increasingly used in many fields of science, finance, economics,
etc. Every day examples are improvement of geophysical model
descriptions (flows, water levels, waves), improvements and
optimization of daily weather forecasts, detection of errors in
data series, on-line identification of stolen credit card use,
detection of malfunctioning components in manufacturing
processes. The one common element is the prior knowledge of the
behaviour of a process in the form of an explicit model
description, or a set of characteristic data. The second common
element is a set of independent or new data. Neither the
description of the behaviour and the data are 100% certain –
they have uncertainties associated with them. If one has
information on the (statistical) nature of these uncertainties,
smart mathematical techniques can be used to combine these two
information sources and generate new or im