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Data Modeling Theory and Practice by [Simsion, Graeme]
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Length: 416 pages Language: English

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Product Description

DATA MODELING THEORY AND PRACTICE is for practitioners and academics who have learned the conventions and rules of data modeling and are looking for a deeper understanding of the discipline. The coverage of
theory includes a detailed review of the extensive literature on data modeling and logical database design, referencing nearly 500 publications, with a strong focus on their relevance to practice. The
practice component incorporates the largest-ever study of data modeling practitioners, involving over 450 participants in interviews, surveys and data modeling tasks. The results challenge many longstanding held assumptions about data modeling and will be of interest to academics and practitioners alike.


Graeme Simsion brings to the book the practical perspective and intellectual clarity that have made his
Data Modeling Essentials a classic in the field. He begins with a question about the nature of data modeling (design or description), and uses it to illuminate such issues as the definition of data modeling, its philosophical underpinnings, inputs and deliverables, the necessary behaviors and skills, the role of creativity, product diversity, quality measures, personal styles, and the differences between experts and novices.


Data Modeling Theory and Practice is essential reading for anyone involved in data modeling practice, research, or teaching.

Product details

  • Format: Kindle Edition
  • File Size: 3101 KB
  • Print Length: 416 pages
  • Publisher: Technics Publications; First edition (20 September 2014)
  • Sold by: Amazon Australia Services, Inc.
  • Language: English
  • ASIN: B00NRXL6BE
  • Text-to-Speech: Enabled
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  • Word Wise: Not Enabled
  • Enhanced Typesetting: Not Enabled
  • Average Customer Review: Be the first to review this item
  • Amazon Bestsellers Rank: #199,761 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
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Amazon.com: 3.6 out of 5 stars 8 reviews
4.0 out of 5 stars Excellent summary and deatiled explanation of what about the realtities of data modleing - a "must read" for data modelers 8 September 2015
By R. Ordowich - Published on Amazon.com
Verified Purchase
The author provides a comprehensive summary of the academic and practitioner views of data modeling in an easy to understand way. His own points of view are occasionally referenced but do not dominate the book. He substantiates his observations with numerous references. This is a "must read" book for all data modelers, DBA's and programmers as well as CIO's! The contents of this book should also be introduced to non-technical staff for them to gain an appreciation for the data modeling practices and challenges.

My only critique of this book is that it represents the technologist's point of view, not that of the business or non-technical participants in the design of a data model. As such, it provides little insight as to how to improve the involvement and accountability of the business staff in the design process. Data is a human construct and has been around for much longer than the database technology but we continue to rely on data modelers as the "experts" for data. The book misses the critical factor that data modelers and business users are Data Illiterate and the result are database models that are semantically handicapped resulting in data silos, data duplication and limited usable data.
2 of 2 people found the following review helpful
3.0 out of 5 stars Heavy on practice, light on theory 31 August 2013
By Rainer Schoenrank - Published on Amazon.com
Verified Purchase
The book investigates the current state of data modeling practice very well. It explores the differences in data modeling as executed by novices and experts. It explores and organizes the current data modeling literature carefully but it never investigates the mathematical theory behind data modeling, relational algebra and The Relational Data Model.

It replaces mathematical theory and the restrictions on the data model required for a database with fuzzy feel-good philosophical preferences of the 'thought leaders' in data modeling. It does not advance the theory of data modeling one iota.

The index leaves a lot to be desired.
5.0 out of 5 stars Not for beginners 17 June 2013
By P. R. Kennicott - Published on Amazon.com
Verified Purchase
I loved this book, but I have some experience in data modeling. This book is an expansion of the author's PhD thesis on some details of data modeling. It explores a controversy about whether modeling describes an existing situation, or whether it is a design for a database and thus depends on who is doing the modeling. This is not a book for beginners in data modeling.
8 of 8 people found the following review helpful
5.0 out of 5 stars A valuable contribution to the field of data modeling revealing many useful insights 15 August 2007
By L. Silverston - Published on Amazon.com
After spending 25 years practicing, lecturing, writing about, studying, and teaching data modeling, I was thrilled to discover new insights and gain more clarity in this field due to Dr. Simsion's diligent work that is shared in his latest book, "Data Modeling Theory and Practice". Like many data modelers, I have strong views on this subject and it was interesting to be able to see the broad range of opinions, experiences, and even to be able to see some of the diverse models produced in the research contained with this book. One of my views is that the more perspectives you can see, the greater the clarity. So, regardless of your views on data modeling, if you are interested in furthering your understanding of the discipline of data modeling, then I would highly recommend reading this material as I give my "two thumbs way up" to this revolutionary book.
2 of 2 people found the following review helpful
5.0 out of 5 stars A highly recommended reading 10 February 2009
By Frank Kalis - Published on Amazon.com
Data modeling is a discipline that many times has lead to heated discussions. The front between the theorists on the one side and the practitioners on the other side sometimes seems to be immovable.

Where should sticking to the mere theory end, where should the pragmatic, for the specific case customized creativity begin? How much theory does a "good" model need? How much "common sense" can a model tolerate and still stand a theorists test?

Graeme Simsion has written a remarkable book that succeeds in examining both theory and practice in a critical fashion without snubbing any side. However, set up as dissertation on the adequate scientific level this is no beginners book.

A highly recommended reading for all, who want to look beyond their own nose in data modeling questions and learn from theory and practice alike.

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