The need for a formal methodology for dealing with data concepts and structures became apparent to the early builders of business processing systems. The concepts of data modeling were pioneered by the early practitioners of software engineering. No matter what approach is taken for building industry strength application software, data modeling is always attempted. The traditional approaches to software engineering (the waterfall method) and the moderns “agile” approaches – all use data modeling of some kind. Establishing the formal meaning of related and structured data: Primacy of unvarying underlying structures over process-defined views:
data structure in an information system need not be complete but it should be accurate . The need for a formal methodology for dealing with data concepts and structures became apparent to the early builders of business processing systems. The concepts of data modeling were pioneered by the early practitioners of software engineering. No matter what approach is taken for building industry strength application software, data modeling is always attempted. The traditional approaches to software engineering (the waterfall method) and the moderns “agile” approaches – all use data modeling of some kind. Conceptually similar to class modeling: Data modelling identifies entity types – class modelling is focussed on classes. In data modelling, attributes are assinged to entities – whereas in class modelling attributes and operations are assinged to each class. Like between entities, there as associations between classes - relationships, inheritance, composition, and aggregation are all applicable concepts in data modeling – as in the case of data modeling. Establishing the formal meaning of related and structured data: Primacy of unvarying underlying structures over process-defined views:
Exploring domain concepts with stake holders
Anybody concerned with data oriented aspects of a project.