Defining and implementing domains with multiple types using mesodata modelling techniques
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The integration of data from different sources often leads to the adoption of schemata that entail a loss of information in respect of one or more of the data sets being combined. The coercion of data to conform to the type of the unified attribute is one of the major reasons for this information loss. We argue that for maximal information retention it would be useful to be able to define attributes over domains capable of accommodating multiple types, that is, domains that potentially allow an attribute to take its values from more than one base type. Mesodata is a concept that provides an intermediate conceptual layer between the definition of a relational structure and that of attribute definition to aid the specification of complex domain structures within the database. Mesodata modelling techniques involve the use of data types and operations for common data structures defined in the mesodata layer to facilitate accurate modelling of complex data domains, so that any commonality between similar domains used for different purposes can be exploited. This paper shows how the mesodata concept can be extended to facilitate the creation of domains defined over multiple base types, and also allow the same set of base values to be used for domains with different semantics. Using an example domain containing values representing three different types of incomplete knowledge about the data item (coarse granularity, vague terms, or intervals) we show how operations and data structures for types already existing within the mesodata can simplify the task of developing a new intelligent domain.