Decision Support Systems (DSS)
Collecting data from one sensor, or many
sensors, is only the first step in the overall
decisionmaking process, which might be inspection,
monitoring, tracking, etc. Often, many other
components, such as databases, simulation
models, and mathematical optimization, must
be combined to form a fully developed decision
support system (DSS).
The final output of a DSS is a recommendation,
interpretation, or prediction regarding the
situation of interest, such as crop treatment,
food safety, or water quality. Such systems
can become quite complex, with many interacting
components, and can be either embedded and
real-time systems or off-line systems.
When multiple data streams are collected,
one must decide how those data will be fused
in the decision process. Separate data streams
can be:
- Mixed mode (including both quantitative
and qualitative data).
- Redundant (validating the data from another
stream).
- Complementary (providing measures of
multiple characteristics of an object).
- Supportive (helping verify some interpretation
of another data stream).
Given the wealth of sensors available today
and the volume of information they generate,
sensor fusion research and development has
become a growing and highly active community.
The final result of the sensing process (which
may include sensor fusion) is an accurate
and reliable interpretation of the data regarding
an object.
Once reliable information has been collected
in the sensing step, it may need to be combined
with information and knowledge from other
sources. For example, a crop simulation model
may be used to project a future crop condition
(or value) based on current measurements.
Or, data on a fermentation process may be
compared to historical trends stored in a
data base to assess whether they lie within
acceptable limits.
More qualitative knowledge, in the form
of expert rule bases, may also be applied.
A DSS may also incorporate economic models
or calculations to determine which courses
of action are reasonable. Other factors that
might need to be considered include operational
cultures within the organization or the industry
or current financial markets.
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