Issue No. 05 - September/October (1998 vol. 10)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/69.729722
<p><b>Abstract</b>—Constraint relational databases use constraints to both model and query data. A constraint relation contains a finite set of <it>generalized tuples</it>. 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 <it>generalized relational algebra</it>). 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, we introduce two distinct classes of constraint algebras, one based on the relational algebra, and one based on the nested relational algebra, and we present an algebra of the latter type. The algebra is proved equivalent to the generalized relational algebra when input relations are modified by introducing generalized tuple identifiers. However, from a user point of view, it is more suitable. Thus, the difference existing between such algebras is similar to the difference existing between the relational algebra and the nested relational algebra, dealing with only one level of nesting. We also show how external functions can be added to the proposed algebra.</p>
Constraints, generalized relations, relational algebra, nested relational algebra, external functions.
A. Belussi, E. Bertino and B. Catania, "An Extended Algebra for Constraint Databases," in IEEE Transactions on Knowledge & Data Engineering, vol. 10, no. , pp. 686-705, 1998.