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The Data Model Resource Book: Volume 3: Universal Patterns for Data Modeling (Englisch) Taschenbuch – 2. Januar 2009

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  • The Data Model Resource Book: Volume 3: Universal Patterns for Data Modeling
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  • The Data Model Resource Book: A Library of Universal Data Models for All Enterprises, Volume 1
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  • The Data Model Resource Book: A Library of Universal Data Models by Industry Types, Volume 2
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Learn how to customize existing data models and create new data models with the information in "The Data Model Resource Book: Universal Patterns for Data Modeling, Volume 3", a guide to universal data patterns that are applicable across a wide variety of organizations. Included are many examples of specific data implementations, with patterns for modeling and using roles, categorizing data, organizing data, tracking the status of data, handling events and transactions, and modeling business rules. Customize existing models and convert models into physical database designs using the guidelines in this book.


"Universal Patterns for Data Modeling is essential reading for anyone undertaking commercial data modeling. The Data Model Resource Book series represents the most important contribution to the data modeling discipline in the last decade."
-Dr. Graeme Simsion, author of Data Modeling Essentials and Data Modeling Theory and Practice
This third volume of the bestselling Data Model Resource Book series revolutionizes the data modeling discipline by answering the question "How can you save significant time while improving the quality of any type of data modeling effort?" In contrast to the first two volumes, this new volume focuses on the fundamental, underlying patterns that affect over 50 percent of most data modeling efforts. These patterns can be used to considerably reduce modeling time and cost, to jump-start data modeling efforts, as standards and guidelines to increase data model consistency and quality, and as an objective source against which an enterprise can evaluate data models.
For each pattern, numerous alternatives are provided, ranging from very specific to very generalized ways of modeling. Len Silverston and Paul Agnew point out the pros and cons of these alternatives and provide guidelines to help you make appropriate decisions depending on the set of circumstances faced. In developing and documenting these patterns, the authors share an invaluable set of foundational tools for anyone involved in data modeling, from the novice to the expert. The authors show you how to:
* Model the most prevalent data modeling constructs such as ways to model roles, hierarchies, classifications, statuses, contact information, 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 generalized models
* Apply the patterns in many types of data modeling efforts, such as prototypes, applications, enterprise data models, data warehouses, and master data management efforts
* Gain buy-in regarding the use of patterns and/or standardizing on these patterns

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Format: Taschenbuch
This is by far the most useful book I have seen till now on the topic of data modeling "patterns" or "templates". The data models are well abstracted and contain the necessary detail for use in actual implementation projects.
The models also serve as good examples/illustrations of common modeling principles, making the book a good "application primer" after a book on database modeling methods.
See Martin Fowler's "Analysis Patterns" for some approaches on how to turn these models (intended for use with databases) into useful conceptual resources for object oriented implementations.
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Das Buch bringt mir in der Praxis nicht soviel, weil ich hier über viele Seiten nach der Essenz suchen muss. Letztlich ist das aber auch sehr subjektiv. Jemand anderem gefällt der umfangreiche Inhalt vielleicht.
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Die hilfreichsten Kundenrezensionen auf Amazon.com (beta)

Amazon.com: 4.5 von 5 Sternen 58 Rezensionen
30 von 31 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen An important contribution to our field 2. April 2009
Von Steve Hoberman - Veröffentlicht auf Amazon.com
Format: Taschenbuch
As an analyst for a large manufacturing company's ERP implementation, I was responsible for a very complex and critical area called Classifications. Classifications was the place where all products, vendors, or customers were grouped into buckets based on similar behavior. For example, if this company manufactured vehicles, there could be classifications for hybrids, sports cars, SUVs, minivans, etc. To better understand classifications, I dived into screens, help files, and actual database tables and after several weeks, completed a classifications data model. The model I produced was very similar to the data model that appears on page 224 of "The Data Model Resource Book Volume 3: Universal Patterns for Data Modeling" by Len Silverston and Paul Agnew.

This book contains a collection of patterns, which are general building blocks that could be used as the basis for just about any type of data modeling within any industry. Classifications is one example, and there are a collection of others such as roles, statuses, and contact mechanisms. Whereas Volumes 1 and 2 in The Data Model Resource Book series contained models for common business processes or industries, this volume contains patterns that cross through all processes and industries. Consistent with the series however, the purpose of this text is to save the modeler time so instead of starting from scratch, the modeler can start from a reliable and proven foundation. Realizing these patterns exist and making them work for your particular modeling assignment can result in a higher quality data model and a greater level of consistency within your organization.

A majority of the book is dedicated to chapters which describe how to model a pattern at different levels of generalization. Level 1 is the most concrete and this is where terms and rules a business analyst are familiar with are shown, such as email address and telephone number. Level 2 through 4 go through increasingly more generized levels with Level 4 being the most generalized. The Classifications example I encountered in the ERP package for example was a Level 3 model, very generalized so that it can be leveraged by any industry. The book makes an important point that there are situations where one level is more appropriate than another, and sometimes the modeler must trade the familiarity and business rule enforcement of a Level 1 with the flexibility available in a Level 2, 3, or 4. For example, a phone number and email address from a Level 1 model would be generalized into contact mechanism data in a Level 2 model. This extra flexibility allows for accommodating other ways of contacting someone that may not have been specified (for example, via a person's "voice over IP" or Skype number). The book also makes the point that sometimes on a single model you can combine different levels for the same requirement (i.e. a hybrid approach).

Chapter 1 introduces the concept of a universal pattern as well as the terms and symbols used throughout the book. The goals for the book are also clearly articulated, in addition to the intended audience and a summary of each chapter. There is a wonderful furniture analogy used to distinguish a universal data model from a universal pattern. Universal data models (the subject of the first two volumes of The Data Model Resource Book), are similar to already constructed standard tables and chairs. The consumer can obtain this furniture instead of build the tables and chairs from scratch. Similarly, the modeler can reuse an inventory or claims universal data model instead of building it from scratch. Universal patterns are similar to the dovetail joints of the furniture, common pieces that exist in already built tables and chairs as well as custom furniture. Universal patterns are the building blocks such as the roles and statuses behind any modeling project.

Chapters 2 through 8 each focus on a particular pattern. Chapters 2 and 3 focus on parties and roles; Chapter 2 on declaration roles and Chapter 3 on contextual roles. A party is a person or organization of importance to the business, and declaration roles are those roles that are independent of any business event while contextual roles are dependent on a particular business event. For example Bob the person can have a declarative role of `Doctor', yet when an insurance claim is filed, they can also have the contextual role of `Primary Care Physician'. Chapter 4 focuses on similar structures for relating data including hierarchies, aggregations, and peer-to-peer relationships. Chapter 5 focuses on taxonomies and classifications, and Chapter 6 on patterns for states that business concepts go through. Chapter 7 contains patterns for getting in touch with parties, such as those patterns for modeling telephone number and email address. Chapter 8 focuses on how to model business rules including the rule itself, the factors involved in the rule, and the outcomes of the rule.

I was impressed with the consistency and comprehensiveness of each of these chapters. These chapters follow a similar format of demonstrating each of the four levels of detail. Each chapter begins with an explanation of the pattern and a discussion of its importance. Then for each of the four levels, there is a section on the reason for the level, how the pattern works (with lots of examples), when the pattern should be used, and the weaknesses of the pattern. I found the charts and tables to be extremely useful in the text, especially the Summary of Patterns table at the end of each chapter.

Chapter 9 focuses on how to apply these patterns in many types of efforts including both relational and dimensional modeling efforts and both application and enterprise areas of scope. As with the other chapters, there is a great summary at the end on the strengths and weaknesses of patterns with each type of effort. Chapter 10 adds the human dynamics side to incorporating patterns, as success or failure is heavily connected with people's perception or trust. Four principles are discussed, that will help acceptance and usage of the patterns: Understand motivations and work toward meeting them, Develop a clear, common, compelling vision, Develop trust, and Manage conflict effectively.

To summarize, under every data model is a set of common building blocks, clearly explained in "Universal Patterns for Data Modeling". I would recommend this book for every analyst, modeler, or architect who is striving for a level of information consistency within their organization. Whether you are just starting your modeling adventure or have been in the modeling for decades, you will find these patterns invaluable tools for every modeling effort.
15 von 16 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen A Comprehensive Study of Key Data Model Patterns 19. Februar 2009
Von Robert P. Hoeting - Veröffentlicht auf Amazon.com
Format: Taschenbuch Verifizierter Kauf
Unlike the world of Object Oriented programming, there are very few pattern books devoted to data modeling. This is unfortunate because most business applications suffer from over-specialization & inflexibility in the data model, where change is very expensive. The few data model patterns books that do exist do not explore the depths of subject to the extent that this book does.

Based on their many years of experience, Silverston & Agnew chose to focus on the most common areas of interest (Parties, Roles, Relationships, Statuses, Classifications, Contact Mechanisms, & Rules) and offer a set of data model pattens for each. Each area has a set of "pattern levels" ranging from specific to general, listing benefits, drawbacks, & usage guidelines for each. The modeler can choose which pattern level best suites the enterprise based on perceived needs.

This book can be thought of as the "gang of four" book for data modeling. I would recommend this book for anyone engaging in any form of data modeling. You may not use the exact patterns, but it will at least serve as a catalyst for future thought.
25 von 29 Kunden fanden die folgende Rezension hilfreich
2.0 von 5 Sternen Two surprises I didn't like 25. Oktober 2005
Von G. Pond - Veröffentlicht auf Amazon.com
Format: Taschenbuch Verifizierter Kauf
The text of this book was generally good - although it seemed padded out with a massive listing of model metadata. The book comes with a CD. The first surprise was, the CD does not include the Data Definition Language (DDL) for its sample models... there's an extra cost for that. My question to the publisher would be, what good is the CD you provide for free?

The second surprise is the illustrations. There are plenty of them, but they look like they were done in a primitive graphics package - not in an enterprise modeling tool. They author seems to have invented his own wierd set of conventions, including "foreign keys do not appear in the entities... that is duplicate information". Before you buy this book, take a look at the illustrations of the models. If you can live with the notation, maybe consider buying it.
8 von 8 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen Encapsulates lots of practitioner experience to jumpstart modeling 17. August 2005
Von Derick Jose - Veröffentlicht auf Amazon.com
Format: Taschenbuch
I got both the volumes 2 years back. I found the concepts really useful and have applied the modeling constructs to actual engagements in supply chain, corporate banking and travel industry. Especially the "party-role-transaction" construct . It is truly a liberating data modeling construct to apply across industry. The models are also at the right level of abstraction. It elegantly positioned in between 2 modeling extremes ... It is neither too abstract/conceptual nor too specific and detailed to one implementation ... a balance which is typically very difficult to maintain . One word of caution on expectation ... the objective is to treat these models as a starting point for your specific projects. It need not be the only way to model a business scenario. But the book opens the modelers mind upto possibilities which one typically tends to ignore and that is the key! Because often times these "outlier scenarios" tend to come and haunt the architecture once realized physically on a database and is often a painful process to modify. Lens varied experience highlights some specific "land mines" to watch for in modeling these scenarios which has helped me in my projects. In a nut shell I have found the book to express in a concise manner the essential elements of modeling to watch out for
11 von 12 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen A terrific tool for the I.T. developer 24. Juni 2001
Von Ted Kowalski - Veröffentlicht auf Amazon.com
Format: Taschenbuch
The Data Model Resource Book is as important to a data modeler or application developer as a dictionary is to a writer: you're not at your best without it. Where I've worked, this material has helped me create the architecture for an enterprise data model of our company. Aided by the Resource Book, we build a framework of major tables containing subject areas and the result is a business model to which we can map all of our application databases and data warehouses.
This book is basically rather simple to use; you find the data or subject of interest and then check to see if there are any attributes or relationships in the book that are relevant to your specific application database. This type of a check helps add quality and completeness to your logical and physical model.
But using the book just scratches the surface of its value; it's the author who's responsible for its quality and completeness. I've personally worked with Mr. Silverston who participated in consulting engagements at our firm and I`ve also seen him perform in the classroom setting. He seems to have an uncanny ability to analyze a given business situation-no matter how seemingly bizarre-and to create a model structure that will accommodate any situation.
I highly recommend the Resource Book to business analysts, application developers, programmers, and data warehouse designers.
Ted Kowalski Data Architect, Equilon Enterprises, Houston and author of "Opening Doors--A Facilitator's Handbook."
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