The Data Model Resource Book
Volume 3: Universal Patterns for Data Modeling
By Len Silverston, Paul Agnew
Publisher: Wiley
Final Release Date: March 2011
Pages: 648

"Universal Patterns for Data Modeling is essential reading foranyone undertaking commercial data modeling. The Data ModelResource Book series represents the most important contribution tothe data modeling discipline in the last decade."
—Dr. Graeme Simsion, author of Data Modeling Essentials andData Modeling Theory and Practice

This third volume of the bestselling Data Model Resource Bookseries revolutionizes the data modeling discipline by answering thequestion "How can you save significant time while improving thequality of any type of data modeling effort?" In contrast to thefirst two volumes, this new volume focuses on the fundamental,underlying patterns that affect over 50 percent of most datamodeling efforts. These patterns can be used to considerably reducemodeling time and cost, to jump-start data modeling efforts, asstandards and guidelines to increase data model consistency andquality, and as an objective source against which an enterprise canevaluate data models.

For each pattern, numerous alternatives are provided, rangingfrom very specific to very generalized ways of modeling. LenSilverston and Paul Agnew point out the pros and cons of thesealternatives and provide guidelines to help you make appropriatedecisions depending on the set of circumstances faced. Indeveloping and documenting these patterns, the authors share aninvaluable set of foundational tools for anyone involved in datamodeling, from the novice to the expert. The authors show you howto:

  • Model the most prevalent data modeling constructs such as waysto model roles, hierarchies, classifications, statuses, contactinformation, and business rules

  • Re-use a powerful library of core patterns for data modeling

  • Model at different levels of generalization

  • Evaluate the pros and cons of specific versus generalizedmodels

  • Apply the patterns in many types of data modeling efforts, suchas prototypes, applications, enterprise data models, datawarehouses, and master data management efforts

  • Gain buy-in regarding the use of patterns and/or standardizingon these patterns

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