Design and Development of a Compound DSS for Laboratory Research - Efficient Decision Support Systems: Practice and Challenges from Current to Future - page 483

learning algorithm named Trend and Potency Tracking Method (TPTM). This algorithm
looks for the data trend by considering the occurrence order of the observed data and also
quantifies the potency for each of the existing data by computing the TP value. It was
experimentally verified that this algorithm helps to improve the performance of neural
network prediction. This training mechanism conducts an incremental learning process and
it is practical to earn the knowledge in dynamic early stages of manufacturing.
Li and Liu (Li & Liu, 2009) also deal with the use of neural networks for small data sets.
They developed an unique neural network based on the concept of monitoring of a central
data location (CLTM) for determining the network weights as the rules for learning. The
experimental results confirmed the higher performance of prediction of the new network,
especially in comparison with the traditional back-propagation neural networks.
Artificial neural networks offer very flexible technology broadly usable in the decision
support systems. With the possibility of modification, neural networks are useful for
applications which process a very limited amount of data. Laboratory research is one of the
areas that are characterized by producing small data sets. One of the positive aspects of
building a DSS for laboratory research using artificial neural networks is the fact that there
are many commercial and free software tools for the design, development and
implementation of the ANN.
3.5 Summary
The vast majority of above mentioned diagnostic DSSs are complex and robust tools with
well-defined purpose, which processing huge amounts of data, representing hundreds or
thousands of incidents and events, and having tens to hundreds of users. In contrast, DSS
designed for use in research and development should be used by individuals with a
maximum amount of data corresponding to tens of thousands events. The purpose of such
system should be flexible in a certain manner, with the possibility of its definition according
to the main objective of the research.
Artificial neural networks and Bayesian networks can be certainly considered as the
appropriate technologies for development of DSS for laboratory research. Due to the relative
inputs certainty of such system, the use of fuzzy logic seems to be somewhat excessive.
Using of rule and case based reasoning logically seems to be inappropriate, mainly due to
the absence or small number of already done cases, and the lack of rules for reasoning,
especially in the early stages of research.
4. In-house DSS development overview
As was already mentioned in the introduction, this work is, among others, focused on in-
house development as the best way to design, develop and implement DSS application with
maximal possible compliance with user's demands and requirements. This part brings the
short overview of in-house application development approaches and possibilities.
According to Turban (Turban et al., 2008), there are three basic approaches to DSS
application development. They are:
1. build the system in-house,
2. buy an existing application,
3. lease software from application service provider (ASP).
Because of uniqueness of every research project, e.g. data types and data sources, amount of
users, sharing and security requirements etc., there is basically only one solution to DSS