Information Technology Reference
In-Depth Information
The data-driven part of the DSS is based on Oracle XE and uses PL/SQL for data processing
routines. This part is responsible for storing the raw measured data and their metadata as
well as their preprocessing such as filtering, computing, transforming etc. It is also
responsible for executing user's queries and analysis.
Due to relatively expensive and time-consuming measurements associated with organic
semiconductors research, the main attention was paid to the knowledge-driven part of the
DSS. The main aim of this part is satisfactory prediction of observed parameters based on
several initial measurements. The secondary aim is discovering of hidden relationships and
patterns in measured data.
For these purposes, the artificial neural network approach was chosen. The neural network
system of the DSS is build using Neuroph - a Java neural network framework. This open
source product allows development of common neural network architectures. Neuroph
also provides GUI neural network editor and includes own IDE based on NetBeans
platform.
For prediction tasks of the DSS, mainly the multilayer Perceptron (MLP) architecture with
backpropagation training algorithms is used. The neural networks are deployed as Java
applications. These ANN applications are integrated and executable using GlassFish Server
OSE. It is an open source Java EE compatible application server. It is the free version of
Oracle GlassFish Server. It provides more or less the same functionality with a broad
support and developer community.
Undoubtedly, the best approach for building decision support system for laboratory
research is evolutionary prototyping. Laboratory research projects are not static and it is
clear that the requirements for decision-making support will still appear and change during
all phases of research. Evolutionary prototyping approach enables developers to flexibly
respond to current needs and requirements of users. In this way, the new functions and
functionalities of the system could be implemented on demand and the system itself could
be constantly up to date and satisfactory.
6. Conclusion
The usage of decision support systems in the field of laboratory research is still relatively
unexplored area. The main aims of deployment of DSS for research purposes are shorten the
duration of research and make the research more efficient. These objectives can be
successfully achieved using artificial neural networks. Using DSS also brings the advantages
in managing and processing of related data.
Such a system need to be built in the shortest possible time, and precisely tailored to the
user's requirements. For these reasons, the in-house application development using
evolutionary prototyping has been chosen as the most satisfactory approach. The
architecture of proposed DSS consists of four interconnected components - database server,
application server, web server, and graphical user interface. The application server is more
or less optional, dependent mainly on functions of database and web server, and on the
requirements of the neural network system.
This work proposes the approach to building decision support system for laboratory
research. Based on characteristics, properties, and demands of laboratory research, the
appropriate DSS types are discussed. Selection of applicable technology is derived from the
capabilities of the four main categories of diagnostic DSS, used mainly in clinical medicine.
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