Information Technology Reference
In-Depth Information
24
Design and Development of a Compound DSS
for Laboratory Research
Tomáš Hujer
University of West Bohemia in Pilsen
Czech Republic
1. Introduction
Using of decision support systems is today far away from being only the domain of top
business management. DSSs are successfully applied in many areas of human activities,
from traditional finance, financial forecasting and financial management, through clinical
medicine, pharmacy, agronomy, metallurgy, logistics and transportation, to maintenance of
machinery and equipment.
Despite this, the use of decision support systems in the domain of laboratory research is still
relatively unexplored area. The main idea behind the application of DSS in this particular
domain is increasing the quality and shortening the duration of research, together with
reducing costs. To achieve these objectives, making the right decisions at the right time
using the right information is needed. Unfortunately, the disadvantage of decision support
in the field of laboratory research is mainly the lack of historical data. The rules for decision-
making are still nascent during the research. This makes the issue of applying DSS for
laboratory research very interesting.
It is obvious that requirements for computer support of laboratory research will vary from
case to case, sometimes even substantially. On the other hand, there is a characteristic
common to all laboratory research. The laboratory research consists of series of tests and
measurements which generates data and knowledge as their outputs. To make a research
effective, it is necessary to apply an appropriate process control to diagnostics, as well as
knowledge acquisition techniques and knowledge management tools. Moreover, knowledge
is very often hidden in the relationships between measured data and has to be discovered
by using sophisticated techniques, such as Artificial Intelligence.
There are several options for building DSS application. This chapter is focused on
in-house development as the best way to develop DSS application with maximal
possible compliance with user's demands and requirements. Especially evolutional
prototyping enables rapid development and deployment of the system features and
functions according to the actual user's requirements. On the other hand, in-house
development puts certain requirements on IT skills, which may be an intractable obstacle
in some cases.
The objectives of this work are not to describe the universal, ready to use DSS, but to reveal
possibilities, means and ways, to describe the methodology of design and in-house
development of DSS for laboratory research with the most possible fit to user's
requirements.
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