Information Modeling and Relational Databases: From by Terry Halpin

By Terry Halpin

Information Modeling and Relational Databases offers an advent to ORM (Object position Modeling)-and even more. in truth, it's the single publication to head past introductory insurance and supply the entire in-depth guide you must rework wisdom from area specialists right into a sound database design.

Inside, ORM authority Terry Halpin blends conceptual details with functional guideline that might allow you to commence utilizing ORM successfully once attainable. Supported through examples, routines, and beneficial history details, his step by step procedure teaches you to advance a natural-language-based ORM version after which, the place wanted, summary ER and UML types from it. This publication will quick make you expert within the modeling procedure that's proving important to the advance of actual and effective databases that top meet genuine company ambitions.

* the main in-depth insurance of item function Modeling to be had anywhere-written by means of a pioneer within the improvement of ORM.
* presents extra assurance of Entity dating (ER) modeling and the Unified Modeling Language-all from an ORM perspective.
* meant for a person with a stake within the accuracy and efficacy of databases: platforms analysts, info modelers, database designers and directors, teachers, managers, and programmers.
* Explains and illustrates required strategies from arithmetic and set theory.
* through a spouse site, offers solutions to routines, appendices masking the heritage of computing device generations, subtype matrices, and complex SQL queries, and hyperlinks to downloadable ORM instruments.

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Extra info for Information Modeling and Relational Databases: From Conceptual Analysis to Logical Design (The Morgan Kaufmann Series in Data Management Systems)

Example text

Customary system of measurement to metric units. One team worked in customary units and sent its data to a second team working in metric, but no conversion was made. If I tell you that I weigh 180, do I need to go on a drastic diet? No if I mean 180 lb, but yes if I mean 180 kg. Data by itself is not enough—what we really need is information, the meaning or semantics behind the data. Since computers lack common sense, we need to pay especial attention to semantics when we use computers to model some aspect of reality.

This is called an exclusion constraint, and verbalizes as no Person directed and reviewed the same Movie. Or reading it the other way: no Movie was directed by and was reviewed by the same Person. To validate this rule with the domain expert, you should verbalize the rule and also provide a counterexample. For example, is it possible for movie 1 to be directed by Ron Howard and also reviewed by Ron Howard? 6 includes this counterexample. If the exclusion constraint really does apply, at least one of those two facts must be wrong.

Although these arcs are a useful annotation, they are not part of the schema notation. If we are familiar with the application, we can verbalize these arcs into relationships. For example, we might verbalize the five facts as Department 10 is located in Building 69. Department 10 has Budget 200000 USD. Product ‘IS2’ has ProductName ‘InfoStar 2’. Employee 357 has EmployeeName ‘Jones E’. Employee 357 is of Sex ‘F’. Are there any more facts? Yes! The parent-child links encode the following two facts: Department 10 develops Product ‘IS2’.

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