  Data mining is a useful decision support technique that can be used to
discover production rules in warehouses or corporate data. Data mining research
has made much effort to apply various mining algorithms efficiently on large
databases. However, a serious problem in their practical application is the
long processing time of such algorithms. Nowadays, one of the key challenges is
to integrate data mining methods within the framework of traditional database
systems. Indeed, such implementations can take advantage of the efficiency
provided by SQL engines. In this paper, we propose an integrating approach for
decision trees within a classical database system. In other words, we try to
discover knowledge from relational databases, in the form of production rules,
via a procedure embedding SQL queries. The obtained decision tree is defined by
successive, related relational views. Each view corresponds to a given
population in the underlying decision tree. We selected the classical Induction
Decision Tree (ID3) algorithm to build the decision tree. To prove that our
implementation of ID3 works properly, we successfully compared the output of
our procedure with the output of an existing and validated data mining
software, SIPINA. Furthermore, since our approach is tuneable, it can be
generalized to any other similar decision tree-based method.
