Design and Development of a Compound DSS for Laboratory Research - Efficient Decision Support Systems: Practice and Challenges from Current to Future - page 478

ES consists of three elements - knowledge base, inference engine, and user interface.
Knowledge base contains the relevant knowledge necessary for understanding, formulating
and solving problem. Typically, it includes two elements - facts that represent the theory of
the problem area, and rules or heuristics that use knowledge to solve specific problem.
Inference engine is the control structure or the rule interpreter of ES. It is a computer
program that derives answers from knowledge base and formulates conclusions. Inference
engine is a special case of reasoning engine, which can use more general methods of
reasoning. User interface is a language processor that provides user-friendly, problem-
oriented communication between the users and the expert system.
The aim of data mining (DM) is to make sense of large amounts of mostly unsupervised data
in some domain (Cios et al., 2007). Data mining techniques can help users discover hidden
relationships and patterns in data. They can be used either for hypothesis testing or for
knowledge discovery. According to Power (Power, 2000), there are two main kinds of models
in data mining - predictive and descriptive. Predictive models can be used to forecast explicit
values, based on patterns determined from known results. Descriptive models describe
patterns in existing data, and are generally used to create meaningful data subgroups.
Data mining software may use one or more of several DM techniques. These technique and
DM tools can be classified based on the data structure and used algorithm. The most
common techniques are:
 Statistical methods , such as regression, correlations, or cluster analysis,
 Decision trees , that break down problems into increasingly discrete subsets by working
from generalization to increasingly more specific information,
 Case Based Reasoning , that uses historical cases to recognize patterns (see Section 3.2),
 Intelligent agents , that retrieve information from (especially) external databases, and
are typically used for web-based data mining,
 Genetic algorithms , that seek to define new and better solution using optimization
similar to linear programming,
 Neural computing , that uses artificial neural networks (ANN) to examine historical
data for patterns and applying them to classification or prediction of data relationships
(see Section 3.4),
 Other tools , such as rule indication and data visualization, fuzzy query and analysis,
etc.
Knowledge-driven DSS are usually built using several proposed Rapid Prototyping
approaches.
3. Diagnostic DSS
Diagnostic decision support systems are mainly associated with the domain of clinical
medicine. They are developed since the early seventies, and are designed to provide expert
support in diagnosis, treatment of disease, patient assessment and prevention. Between the
medical and technical diagnosis is an obvious similarity. In technical diagnosis, the
examined subject is also analyzed to obtain a diagnosis. On its basis, corrective and
preventive actions can be proposed and taken, and examined subject's condition can be
monitored, evaluated and predicted.
Although the use of diagnostic decision support systems overlaps with other fields, such as
maintenance planning and management (Liu & Li, 2007), or the prediction of product life
cycle (Lolas & Olatunbosum, 2008)(Li & Yeh, 2008), the vast majority of available