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Information Modeling and Relational Databases provides an introduction to ORM (Object Role Modeling)-and much more. In fact, it's the only book to go beyond introductory coverage and provide all of the in-depth instruction you need to transform knowledge from domain experts into a sound database design.
Inside, ORM authority Terry Halpin blends conceptual information with practical instruction that will let you begin using ORM effectively as soon as possible. Supported by examples, exercises, and useful background information, his step-by-step approach teaches you to develop a natural-language-based ORM model and then, where needed, abstract ER and UML models from it. This book will quickly make you proficient in the modeling technique that is proving vital to the development of accurate and efficient databases that best meet real business objectives.
• The most in-depth coverage of Object Role Modeling available anywhere-written by a pioneer in the development of ORM.
• Provides additional coverage of Entity Relationship (ER) modeling and the Unified Modeling Language-all from an ORM perspective.
• Intended for anyone with a stake in the accuracy and efficacy of databases: systems analysts, information modelers, database designers and administrators, instructors, managers, and programmers.
• Explains and illustrates required concepts from mathematics and set theory.
• Via a companion Web site, provides answers to exercises, appendices covering the history of computer generations, subtype matrices, and advanced SQL queries, and links to downloadable ORM tools.
Audience: Systems analysts, information modelers, database designers and administrators, instructors, managers, and programmers.
2 Information levels and frameworks
3 Conceptual modeling: first steps
4 Uniqueness constraints
5 Mandatory roles
6 Value, set comparison and subtype constraints
7 Other constraints and final checks
8 Entity relationship modeling
9 Data modeling in UML
10 Relational mapping
11 Relational languages
12 Schema transformations
13 Other modeling aspects and trends
Posted February 27, 2001
I used to think that the best book one could read in order to really learn the science and the art of data modeling was Conceptual Schema and Relational Database Design. I used to think that, that is, until I read the Information Modeling and Relational Databases: From Conceptual Analysis to Logical Design. Originally intended to be the third edition of the ¿Conceptual Schema¿ text, this new book offers the same definitive information as its predecessor with a large amount of added information. So much more information, in fact, that the book has grown by roughly 250 pages! The text begins with a warning. Halpin refers to the 1999 Mars Climate Orbiter accident in which a simple conversion from imperial to metric units caused the $125 million dollar craft to be destroyed. 'Data itself is not enough,' Halpin cautions, 'what we really need is information.' And so begins the introduction of the most accurate way to model data: Object-Role Modeling (ORM). For those of you not familiar with the technique, ORM is a fact-based approach to modeling that not only captures the semantics of data ¿ in the native language of the subject matter expert ¿ but it also captures many rules, offers an embedded process to ensure the model is correct, and completely maps to any fully normalized logical notation (e.g. ER, UML). Let me re-phrase the above, because it is extremely important. With ORM, you can: a) Talk to subject matter experts in their language and in terms they can understand ¿ you don¿t have to define tuples, entities, foreign keys, attributes, and all that other nonsense; b) Verify that the model is correct by using a robust method (ORM is more than just a notation) filled with quality checks; c) Document more rules ¿ intrinsic in ORM¿s rich constraint language ¿ to ensure the resulting system captures all of the rules crucial to the data being modeled; d) And finally map the conceptual schema into a fully normalized database structure. If you are new to data modeling, this is the first book you should read. This book will detail the concepts you need to know in order to analyze and create correct data schemas ¿ regards less of which notation or tool you end up using (although both Halpin and myself have an opinion on which to choose). In other words, use this book to learn how to think about the problem. In so doing, you can easily map the concepts into the more trendy notations and methodologies, if you must. If you are a modeling veteran, you should also read this book. In so doing, I¿ll wager that you will discover you have been making correct models the hard way all these years. You¿ll see, in exquisitely clear detail, the inherent problems in the other techniques (such as ER and UML). Further, if you are open minded enough to temporarily forget what you have learned so far, you too can learn how to think correctly about data modeling problems ¿ and their solutions. Now that I (hopefully) have convinced you to give this book a try, I¿ll detail the contents. The first two chapters are introductory material intended to give the reader a sneak peek at what is coming up. In them, Halpin provides a brief overview of three techniques (ORM, ER, and UML) and discusses the pros and cons of each. With Halpin¿s witty, clear, concise writing style, and the clear evidence of problems with the other techniques, I expect the reader to be fully motivated to read on and delve into the more rigid explanation of the technique. Don¿t let the academic nature of the topics intimidate you; Halpin uses easy-to-follow examples and well-tuned prose to inform academics and industry professionals alike. Just because the method is academically sound (it¿s firmly rooted in predicate calculus and set theory) doesn¿t mean that the material has to be boring. In fact, the tone of the text and the sample data provided in the examples will imply to the reader Halpin¿s distinct sense of humor that actually makWas this review helpful? Yes NoThank you for your feedback. Report this reviewThank you, this review has been flagged.
Posted February 28, 2001
By now, most people recognize how critical a properly designed database is to the success of most business applications. Developers love good database designs because they are much easier to code against, and they make it much easier to accommodate the business requirements of the user, which is after all the purpose of the application. Everyone recognizes the need for good data design, but few people know how fill that need. A good database design requires a good data model, where does one learn how to create a good data model? If you are looking for one book that will really make a difference the next time you design a database, look no further than Information Modeling and Relational Databases by Dr. Terry Halpin. Halpin¿s writing style is clear and interesting, and the numerous examples he uses make the concepts easier to digest. Besides examples within the text, each subsection of the book has a complete set of exercises. Comparing your answers with the supplied answers is a great way to make sure you¿ve absorbed the material. This book is very comprehensive; it starts with simple concepts, and ends with discussions of relational algebra, UML and ER modeling, in addition to Halpin¿s preferred method, Object Role Modeling (ORM). Halpin¿s presentation and explanation of ORM sets this book apart from other data modeling books. As Halpin explains it, the focus in ORM is on business facts, not abstract data structures. As a professional database designer, one of the most common (and often valid) criticisms I encounter is that data modelers often seem too far removed from the business or too ¿theoretical¿. Genuinely good theories should have practical benefits, which is certainly the case with ORM. Object Role Modeling has a very solid theoretical foundation (indeed it is grounded in logic and philosophy), but the application of ORM is very practical. Throughout the book, one is struck by how often Halpin emphasizes the importance of getting real examples from the users. Of course, many books will tell you how important it is to get requirements from the users, but they don¿t outline a simple, usable method for actually doing it. Halpin outlines such a method in the ¿Conceptual Schema Design Procedure¿ (CSDP). The CSDP is a step-by-step guide to using ORM for producing a first class data model based on business requirements. The CSDP walks one through the entire process, from familiarization with the business to the final quality checks on the model. ORM and the CSDP provide a simple way to organize, manipulate and validate the business knowledge that you glean from the users. Halpin calls ORM a conceptual modeling method. So what does an ORM conceptual model look like? At its core an ORM conceptual model is a set of simple assertions about the data for a particular business and how those data relate. Examples are ¿Employee drives Car¿ and ¿Car is made by Manufacturer¿ etc. Such assertions are known as sentence types. Each of these sentence types alone deals with only a small part of the business data, but taken as a collection, the sentence types form a complete picture of the data that must be stored and manipulated in the business environment. Every one of these sentence types is populated (i.e. turned from a general statement into specific examples) with sample data. The sample data can either be supplied directly by the users, or created by the users and database designer as part of the design sessions. Once the sentence types are populated, you apply constraints that regulate the allowable populations. ORM¿s constraint language is very expressive. Using ORM, you can directly model such constraints as ¿No person can review a book which s/he has written¿, ¿No employee can have insurance unless s/he is full time¿, and ¿An ambassador can be assigned to a country only if s/he is fluent in one of the languages spoken in that country¿. Other modeling methods have trouble with thesWas this review helpful? Yes NoThank you for your feedback. Report this reviewThank you, this review has been flagged.