- Series: The Morgan Kaufmann Series in Data Management Systems
- Hardcover: 976 pages
- Publisher: Morgan Kaufmann; 2 edition (March 17, 2008)
- Language: English
- ISBN-10: 0123735688
- ISBN-13: 978-0123735683
- Product Dimensions: 7.8 x 1.8 x 9.3 inches
- Shipping Weight: 3.9 pounds (View shipping rates and policies)
- Average Customer Review: 4.2 out of 5 stars See all reviews (12 customer reviews)
- Amazon Best Sellers Rank: #815,571 in Books (See Top 100 in Books)
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Information Modeling and Relational Databases, Second Edition (The Morgan Kaufmann Series in Data Management Systems) 2nd Edition
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Frequently Bought Together
This book is an excellent introduction to both information modeling in ORM and relational databases. The book is very clearly written in a step-by-step manner, and contains an abundance of well-chosen examples illuminating practice and theory in information modeling. I strongly recommend this book to anyone interested in conceptual modeling and databases.
Dr. Herman Balsters, Director of the Faculty of Industrial Engineering, University of Groningen, The Netherlands
About the Author
Dr. Terry Halpin is a professor at Northface University. He has led database research teams at several companies including Visio Corporation and Microsoft Corporation, where he worked on the conceptual and logical database modeling technology in Microsoft Visio for Enterprise Architects. His publications include over 100 technical papers and five books.
Top Customer Reviews
If you intend to create genuinely useful business applications without first creating an accurate conceptual data model and deriving the database schema from the model, then I hope your projects have very large budgets and flexible deadlines, because you'll need both. Accurate conceptual data models are not an academic curiousity, they are a practical necessity. Well designed databases are the heart of every business application, and accurate conceptual data models are the foundation of every well designed database.
This book presents a method for data modeling called Object Role Modeling (ORM). If you've never created a data model before, you might as well learn the best method from the start. If you've used E-R (Entity Relationship) modeling before, this is your chance to learn a method that overcomes the limitations of E-R, while building on the knowledge you already have.
ORM is based on facts (assertions about the business sphere you are modeling), not entities and attributes. Business users understand facts much better than they understand data modeling abstractions. By using ORM facts, you create your data model in a language that business users can understand and validate. Poor communication with business users and inadequate understanding of requirements are major causes of design deficiencies. ORM solves these issues through its fact based approach.
ORM is also much more expressive than any other popular data modeling notation, ncluding UML and all major flavors of E-R. Many business rules should be expressed as data constraints, but traditional data modeling languages don't do well at capturing these constraints.Read more ›
I feel like I have been warped back to 1989 when reading this book. Although there are some theoretical benefits to the approaches taken in the book, industry has largely chosen to not use many of the topics preached within. In the forwards it was mentioned that common industry modeling techniques were contrasted fairly (e.g. UML/ER) -- but I did not find this to be true. The book reads like a desperate attempt to make a lesser used modeling technique relevant. I was hopeful when picking up this book it would be a fair comparison and add a powerful tool I could use in my daily work data-modeling.
As a text-book for first-time data modelers this book does provide some value. Unfortunately, the explanations are dry, patronizing on simple topics and skip detail on complex ones. A typical explanation in the book reads: "For brevity, relational style assumes that variables in the rule head are universally quantified and that variables introduced in the body are existentially quantified." The examples are too simplistic to actually fully explain concepts - and frustratingly seem to avoid all real-world pitfalls we typically encounter as data modelers. On the positive side, if you have no background in modeling at all - you will learn something from this book.
For everyone else, this book is only useful if you are looking for a history lesson on alternative modeling approaches or need a different perspective than is offered in books focused on UML.
In the end, I think there are better choices for learning.
Most Recent Customer Reviews
This book is horrible. It's extremely difficult to read. After reading chapter 1 and 2 about four times I just gave up. Read morePublished 14 days ago by Kenneth Hamilton
I'm still not exactly sure what I read. I got an A in the class, so I guess that's all that matters.Published 7 months ago by Stephen P Daugherty
This book is long in the tooth. IT moves fast and this book is an example of how out of date things can get quick. Read morePublished on October 10, 2014 by akrhodey
This book covers in great detail the database design process, and can help the astute reader to bridge the seemingly disparate theory of (relational as well as deductive) databases... Read morePublished on July 30, 2012 by Artyom Shalkhakov
I have been using NIAM for more than 20 years now. It's one of the better data modeling techniques, and so is ORM. Read morePublished on May 7, 2012 by Critical Reviewer
There is no better database modelling method than Object Role Modelling.
There is no better book on Object Role Modelling than this book. Period.
This book is very comprehensive and has an appropriate balance between theoretical and practical issues related to information modeling and database design (logical and physical).Published on January 21, 2009 by Nicolas Velazquez