SEMANTIC DATA MODEL TO RELATIONAL DATA MODEL - Database Design and Development: An Essential Guide for IT Professionals - page 271

Suppose your organization desires to implement a relational database system.
Obviously, the information requirements must be defined properly no matter which
type of database system is being implemented. Information requirements define the
set of real-world information that must be modeled. A data modeler who special-
izes in semantic data modeling techniques creates a semantic data model based on
information requirements. At this stage, the data modeler need not have any knowl-
edge of the relational data model. All the data modeler does is to represent the
information requirements in the form of semantic model components. The next
straightforward step for the data designer is to review the components of the seman-
tic data model and change each component to a component of the relational data
model.
Easy Mapping of Components An object-based data model is composed of a
small, distinct set of components. It does not matter how large and expansive the
entire data model is; the whole data model is still constructed with a few distinct
components. You may be creating an object-based data model for a large multina-
tional corporation or a small medical group practice. Yet, in both cases, you will be
using a small set of components to put together the object-based data model. This
is also true of an entity-relationship data model.
What then is the implication here? Your semantic data model, however large
it may be, consists of only a few distinct components. This means that you just
need to know how to transform a few distinct components. From the other side, a
relational data model also consists of a few distinct components. So mapping and
transforming the components becomes easy and very manageable.
When to Use This Method
When there is more than one method for creating a relational data model, a natural
question arises as to how you choose to adopt one method over the other. When
do you use the model transformation method and not the normalization method?
In Chapter 8, we had a few hints. The model transformation method applies when
the normalization method is not feasible. Let us now list the conditions that would
warrant the use of the model transformation method:
Large database system. When a proposed database system is large and the data
model is expected to contain numerous component pieces, the model transforma-
tion method is preferable.
Complex information requirements. Some sets of information requirements may
require modeling complex variations and many types of generalization and spe-
cialization. There may be several variations in the relationships, and the attributes
themselves may be of different types. Under such conditions, modeling complex
information requirements directly in the relational model, bypassing the semantic
data model, proves to be very difficult.
Large project. A large project requires many data modelers to work in parallel to
complete the data modeling activity within a reasonable time. Each data modeler
will work on a portion of information requirements and produce a partial seman-