Advanced Data Modeling

Data modeling takes on many forms, from domain object modeling through logical entity relationship modeling to relational data modeling: but all of these forms are different stages in the process of modeling data as a project progresses through its development and deployment phases. This course takes the broader perspective on data modeling by following the data modeling process from inception to retirement. The use of best practices to maintain consistency and correctness when transitioning from model type to model type is emphasized throughout the course.For each model type, the course extends the coverage and rigor of the topics introduced in the introductory course. For example, the problems of completeness and correctness are addressed in domain object modelling.The section on entity relationship models stresses the underlying mathematical model incorporating entity predicates, attribute functions and nary relationships to ensure high quality and robust logical models. The relational models are looked at in terms of their design to support nonfunctional requirements of concurrency, locking, indexing schemes and other more advanced topics. This course also introduces two other models that the logical model maps to: the data warehouse dimensional model, and the mapreduction big data model.The second part of the course focuses on the management of data models within an organization over time because data requirements changes force changes in data models. For example, changing the structure of an existing data model to capture information not required before, merging data from two different data models, archiving and restoring data to different models, data transformations for business intelligence or data mining and the always problematic issue of data retirement.