Design and Development of a Compound DSS for Laboratory Research

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 decisionmaking 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.