Object-Role Modeling Fundamentals : A Practical Guide to Data Modeling with ORM
Book Details
Format
Paperback / Softback
ISBN-10
1634620747
ISBN-13
9781634620741
Publisher
Technics Publications LLC
Imprint
Technics Publications LLC
Country of Manufacture
US
Country of Publication
GB
Publication Date
May 1st, 2015
Print length
192 Pages
Weight
422 grams
Dimensions
19.40 x 23.60 x 1.50 cms
Product Classification:
Database design & theory
Ksh 7,400.00
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Object-Role Modeling (ORM) is a fact-based approach to data modelling that expresses the information requirements of any business domain simply in terms of objects that play roles in relationships. All facts of interest are treated as instances of attribute-free structures known as fact types, where the relationship may be unary (eg: Person smokes), binary (eg: Person was born on Date), ternary (eg: Customer bought Product on Date), or longer. Fact types facilitate natural expression, are easy to populate with examples for validation purposes, and have greater semantic stability than attribute-based structures such as those used in Entity Relationship Modeling (ER) or the Unified Modeling Language (UML). All relevant facts, constraints and derivation rules are expressed in controlled natural language sentences that are intelligible to users in the business domain being modeled. This allows ORM data models to be validated by business domain experts who are unfamiliar with ORM''s graphical notation. For the data modeler, ORM''s graphical notation covers a much wider range of constraints than can be expressed in industrial ER or UML class diagrams, and thus allows rich visualisation of the underlying semantics. Suitable for both novices and experienced practitioners, this book covers the fundamentals of the ORM approach. Written in easy-to-understand language, it shows how to design an ORM model, illustrating each step with simple examples. Each chapter ends with a practical lab that discusses how to use the freeware NORMA tool to enter ORM models and use it to automatically generate verbalisations of the model and map it to a relational database.
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