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They applied their framework in an example case with relational database models.
This has the advantage of filtering much of the noise from the engine level and doing sophisticated information consolidation before populating a relational database.
The results of the repository module can be subsequently fed into a relational database to support applications.
The problem of defining and retrieving consistent information from an inconsistent relational database has been studied in the context of relational databases.
Their prototype system has been applied to a relational database model.
The resulting framework borrows heavily from rule-based systems for relational database models, however limiting the scope to those parts that have obvious high performance implementation.
It has been used to write applications such as a window-based text editor, an object based drawing program, a relational database, and a spreadsheet program.
Such data structures can be represented using a relational database, and would be manageable even when the number of components and associated functions grow.
Using a relational database for the permanent storage of ontology data, and fast read and write access to that data, is another.
In a relational database, the sole "bulk" data type is the set.
The canonical example of structured information is a relational database table.
However, users may be interested in retrieving call-types with more sophisticated query mechanisms that suggests that the library should be maintained in a relational database.
It would be most true to the relational database metaphor to represent predicates as arcs between entity-nodes.
The structure of the data precluded the use of a standard relational database system.
Assume that the initial raw data are stored in a relational database, that is, a collection of tables or relations.
