Database Reference
In-Depth Information
base system must be able to support all the relevant business processes and provide
users with proper information. Therefore, any data model as a prelude to the pro-
posed database system must be a true replica of the information requirements.
A semantic data model captures the true and complete meaning of information
requirements. The model is made up of a complete set of components such as
objects, attributes, and relationships and so is able to represent every aspect of the
information requirements. If there are variations in object types or relationship
types in the information requirements, a semantic data model can correctly reflect
such nuances.
Limitations of Implementation Models
Consider the conventional data models
such as the hierarchical, network, and relational data models. These are models that
are implemented in commercial database systems. Hierarchical, network, and rela-
tional databases are offered by vendors. The conventional or implementation
models are the ones that stipulate how data is perceived, stored, and managed in a
database system. For example, the relational data model lays down the structure
and constraints for how data can be perceived as two-dimensional tables and how
relationships may be established through logical links. As such, the implementation
data models address data modeling from the point of view of storing and manag-
ing data in the database system.
However, the objectives of database development are to ensure that any data
model used must truly replicate all aspects of information requirements. The
conventional data models do not directly perceive data from the point of view of
information requirements; they seem to come from the other side. Therefore, a con-
ventional data model is not usually created directly from information requirements.
Such an attempt may not produce a complete and correct data model.
Need for Generic Model
Imagine a process of creating a conventional data
model from information requirements. First of all, what is the conventional data
model that is being created? If it is a hierarchical data model, then you, as a
data modeler, must know the components of the hierarchical data model thoroughly
and also know how to relate real-world information to these model components.
On the other hand, if your organization opts for a relational data model, again
you, as a data modeler, must know the components of the relational data model
and also know how to relate real-world information to the relational model
components.
However, data modeling must concentrate on correctly representing real-world
information irrespective of whether the implementation is going to be hierarchical,
network, or relational. As a data modeler, if you learn one set of components and
gain expertise in mapping real-world information to this generic set of components,
then you will be concentrating on capturing the true meaning of real-world infor-
mation and not on variations in modeling components.
Simple and Straightforward
The attraction for the model transformation
method of creating a relational model comes from the simplicity of the method.
Once the semantic data model gets completed with due diligence, the rest of the
process is straightforward. There are no complex or convoluted steps. You simply
have to follow an orderly sequence of tasks.
Search WWH ::
Custom Search