Set restrictions for semantic groupings

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...

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Bibliographic Details
Published inIEEE transactions on knowledge and data engineering Vol. 6; no. 2; pp. 193 - 204
Main Authors Rundensteiner, E.A., Bic, L., Gilbert, J.P., Meng-Lai Yin
Format Journal Article
LanguageEnglish
Published New York, NY IEEE 01.04.1994
IEEE Computer Society
Subjects
Applied sciences
Computer science
Computer science; control theory; systems
Data models
Erbium
Exact sciences and technology
Information systems. Data bases
Memory organisation. Data processing
Object oriented databases
Object oriented modeling
Relational databases
Software
Database
Semantic abstraction
Object equivalence
Constraint specification
Semantic integrity
Object data modeling
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Summary: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.< >
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:1041-4347
1558-2191
DOI:10.1109/69.277765