Data Modeling by Graeme Simsion

Data Modeling by Graeme Simsion

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Data Modeling by Graeme Simsion

This book is for practitioners and academics who have learned the conventions and rules of data modelling and are looking for a deeper understanding of the discipline. The coverage of theory includes a detailed review of the extensive literature on data modelling 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 modelling practitioners, involving over 450 participants in interviews, surveys and data modelling tasks. The results challenge many long-standing held assumptions about data modelling 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 Modelling Essentials" a classic in the field. He begins with a question about the nature of data modelling (design or description), and uses it to illuminate such issues as the definition of data modelling, its philosophical underpinnings, inputs and deliverables, the necessary behaviours 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 modelling practice, research, or teaching.

Graeme Simsion is the author of The Rosie Project, The Rosie Effect, and The Rosie Outcome, all of which feature Professor Don Tillman, as well as The Best of Adam Sharp and Two Steps Forward, which he co-wrote with Anne Buist. Graeme is an Australian citizen.

SKU Unavailable
ISBN 13 9780977140015
ISBN 10 0977140016
Title Data Modeling
Author Graeme Simsion
Condition Unavailable
Binding Type Paperback
Publisher Technics Publications LLC
Year published 2007-03-27
Number of pages 400
Cover note Book picture is for illustrative purposes only, actual binding, cover or edition may vary.