Learning in Constraint Databases

For several years, Inductive Logic Programming (ILP) has been developed into two main directions: on one hand, the classical symbolic framework of ILP has been extended to deal with numeric values and a few works have emerged, stating that an interesting domain for modeling symbolic and numeric values in ILP was Constraint Logic Programming; on the other hand, applications of ILP in the context of Data Mining have been developed, with the bene t that ILP systems were able to deal with databases composed of several relations. In this paper, we propose a new framework for learning, expressed in terms of Constraint Databases: from the point of view of ILP, it gives a uniform way to deal with symbolic/numeric values and it extends the classical framework by allowing the representation of infinite sets of positive/negative examples; from the point of view of Data Mining, it can be applied not only to relational databases, but also to spatial databases. A prototype has been i..