Traditional, semantic, and hypersemantic approaches to data modeling

An overview is given of past present data-modeling trends, and future directions are identified. The three traditional and commonly used data models that gained wide acceptance in the late 1960s and early 1970s and are used extensively today, namely the relational, hierarchical, and network models, are reviewed. Semantic data models that attempt to enhance the representation of operational information by capturing more of the meaning about data values and relationships are described. Enhancements to semantic data models that characterize hypersemantic data models and emphasize capturing inferential relationships are discussed.<>