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5 CONCLUSIONS
The use of analysis patterns can contribute to the
improvement of GDB conceptual models because
they are tested and aproved solutions. This can
reduce the time needed to the conceptual project and
also reduce the possibility of making mistakes. The
obtainment of these patterns can be done by the
KDD process application. One of the important
phases of this process is the data preprocessing.
Specifically in GDB conceptual schemas the data
preprocessing consists in the integration of the
conceptual schemas designed based on different data
models and with naming variations to the same real
world concepts. Thus the integration must be
performed in two levels, syntacticaly and
semanticaly which was the focus of this paper. The
semantic integration among distinct conceptual
schemas must be aided by an ontology, which allows
searching by names and also searching by structure
as attributes and associations.
Another benefit of using ontologies, is the fact
the knowledge is stored and can be updated and
interchanged. Not only analysis patterns can be
deduced but also the ontologies existing concepts
can help the designer in modeling a new conceptual
schema. However to explore all the ontologies
potentialities and in an efficient way it is necessary
to combine it with another technique very used in
heterogeneous databases, known as similarity
matching.
The next steps of this research are the study of
the similarity matching techniques and more
important the definition of a set of criteria to be
considered in the similarity coefficient calculus and
also the weight of each one. The implementation of
the algorithm proposed in section 4 is also a future
work to test the efficiency of this solution to the
semantic unification.
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