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Publisher Summary This chapter discusses how the entity–relationship (ER) and Unified Modeling Language (UML) approaches can be applied to the database life cycle, which include the requirements analysis and conceptual data modeling stages of logical database design. An example is used to illustrate the ER modeling principles developed in this chapter. Conceptual data modeling, using either the ER or UML approach, is particularly useful in the early steps of the database life cycle, which involve requirements analysis and logical design. These two steps are often done simultaneously, particularly when requirements are determined from interviews with end users and modeled in terms of data-to-data relationships and process-to-data relationships. The conceptual data modeling step (ER approach) involves the classification of entities and attributes first, then identification of generalization hierarchies and other abstractions, and finally the definition of all relationships among entities. Data modeling of individual requirements typically involves creating a different view for each end user's requirements. Then the designer must integrate those views into a global schema so that the entire database is pictured as an integrated whole. Controlled redundancy can be created later, at the physical design level, to enhance database performance. An entity cluster is a grouping of entities and their corresponding relationships into a higher-level abstract object. Clustering promotes the simplicity that is vital for fast end user comprehension. |
