Extensible Data Modeling for Statistical Databases

A conceptual data model is a critical key factor to achieve the goal of a database being designed. The requirements of today’s statistical database applications call for powerful modeling techniques. There are a number of data modeling problems that cannot be solved by using existing statistical data models. This paper proposes conceptual data modeling techniques to meet the requirements of a large shared statistical database application. An object-oriented statistical data model (OOSDM) is constructed using object-oriented concepts. The OOSDM represents statistical data in different levels of abstraction which enhances the semantic richness and theion which enhances the semantic richness and the extensibility of the conceptual model. The extensibility is achieved through an inheritance mechanism. ‘Ihe semantic richness shows that the model is extensible in a manageable way without damaging the semantics of the model.