FrameworkforConceptual Data Modeling : De nition , Application

For successful information systems development, conceptual data modeling is essential. Nowadays a plethora of techniques for conceptual data modeling exist. Many of these techniques lack a formal foundation and a lot of theory, e.g. concerning updates or schema transformations, is highly data model speciic. As such there is a need for a unifying formal framework providing a suuciently high level of abstraction. In this paper the use of category theory for this purpose is addressed. Well-known conceptual data modeling concepts, such as relationship types, generalization, specialization, and collection types are discussed from a categorical point of view. An important advantage of this framework is its \conngurable semantics". Features such as null values, uncertainty, and temporal behavior can be added by selecting appropriate instance categories. The addition of these features usually requires a complete redesign of the formalization in traditional set-based approaches to semantics. Applications of the framework in the context of schema transformations and improved automated modeling support are discussed.