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An Extended Algebra for Constraint Databases
 IEEE Transactions on Knowledge and Data Engineering
, 1999
"... Constraint relational databases use constraints to both model and query data. A constraint relation contains a finite set of generalized tuples. Each generalized tuple is represented by a conjunction of constraints on a given logical theory and, depending on the logical theory and the specific conju ..."
Abstract

Cited by 20 (3 self)
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Constraint relational databases use constraints to both model and query data. A constraint relation contains a finite set of generalized tuples. Each generalized tuple is represented by a conjunction of constraints on a given logical theory and, depending on the logical theory and the specific conjunction of constraints, it may possibly represent an infinite set of relational tuples. For their characteristics, constraint databases are well suited to model multidimensional and structured data, like spatial and temporal data. The definition of an algebra for constraint relational databases is important in order to make constraint databases a practical technology. In this paper, we extend the previously defined constraint algebra (called generalized relational algebra). First, we show that the relational model is not the only possible semantic reference model for constraint relational databases and we show how constraint relations can be interpreted under the nested relational model. Then...
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 valu ..."
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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...
• Dottorato di Ricerca in Informatica, conseguito presso l’Universita ̀ degli Studi di Milano in
"... degli Studi di Genova. Confermato in data 12/12/2002. ..."
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