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Set Restrictions for Semantic Groupings
April 1994 (vol. 6 no. 2)
pp. 193-204

Most research on semantic integrity has taken place in the traditional database fields, specifically the relational data model. Advanced models, such as semantic and object-oriented data models, have developed higher level abstractions to increase their expressive power in order to meet the needs of newly emerging application domains. This allows them to incorporate some semantic constraints directly into their schemas. There are, however, many types of restrictions that cannot be expressed solely by these high-level constructs. Therefore we extend the potential of advanced models by augmenting their abstractions with useful set restrictions. In particular, we identify and formulate four of their most common semantic groupings: set groupings, is-a related set groupings, power set groupings, and Cartesian product groupings. For each, we define a number of restrictions that control its structure and composition. We exploit the notion of object identity for the definition of these semantic restrictions. This permits each grouping to capture more subtle distinctions of the concepts in the application environment, as demonstrated by numerous examples throughout this paper. The resulting set of restrictions forms a general framework for integrity constraint management in advanced data models.

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Index Terms:
object-oriented databases; database theory; data integrity; set theory; deductive databases; set restrictions; semantic groupings; semantic integrity; object data modeling; constraint specification; indistinguishability; object equivalence; object identity; semantic abstractions; high level abstractions; expressive power; semantic constraints; semantic restrictions; is-a related set groupings; power set groupings; Cartesian product groupings; application environment,; integrity constraint management; advanced data models
Citation:
E. A. Rundensteiner, L. Bic, J.P. Gilbert, M.-L. Yin, "Set Restrictions for Semantic Groupings," IEEE Transactions on Knowledge and Data Engineering, vol. 6, no. 2, pp. 193-204, April 1994, doi:10.1109/69.277765
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