Developing High Quality Data Models

Matthew West

ISBN 10: 0123751063 / ISBN 13: 9780123751065

Published by Elsevier Science, 2011

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - Developing High Quality Data Models provides an introduction to the key principles of data modeling. It explains the purpose of data models in both developing an Enterprise Architecture and in supporting Information Quality; common problems in data model development; and how to develop high quality data models, in particular conceptual, integration, and enterprise data models. The book is organized into four parts. Part 1 provides an overview of data models and data modeling including the basics of data model notation; types and uses of data models; and the place of data models in enterprise architecture. Part 2 introduces some general principles for data models, including principles for developing ontologically based data models; and applications of the principles for attributes, relationship types, and entity types. Part 3 presents an ontological framework for developing consistent data models. Part 4 provides the full data model that has been in development throughout the book. The model was created using Jotne EPM Technologys EDMVisualExpress data modeling tool. This book was designed for all types of modelers: from those who understand data modeling basics but are just starting to learn about data modeling in practice, through to experienced data modelers seeking to expand their knowledge and skills and solve some of the more challenging problems of data modeling.

Seller Inventory # 9780123751065

Bibliographic Details

• Title: Developing High Quality Data Models
• Publisher: Elsevier Science
• Publication Date: 2011
• Language: English
• ISBN 10: 0123751063
• ISBN 13: 9780123751065
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 236W x 192H x 24D millimeters 236 millimeters width by 192 millimeters height by 24 millimeters depth
• Weight: 720 grams

About this title

Synopsis

Developing High Quality Data Models provides an introduction to the key principles of data modeling. It explains the purpose of data models in both developing an Enterprise Architecture and in supporting Information Quality; common problems in data model development; and how to develop high quality data models, in particular conceptual, integration, and enterprise data models.

The book is organized into four parts. Part 1 provides an overview of data models and data modeling including the basics of data model notation; types and uses of data models; and the place of data models in enterprise architecture. Part 2 introduces some general principles for data models, including principles for developing ontologically based data models; and applications of the principles for attributes, relationship types, and entity types. Part 3 presents an ontological framework for developing consistent data models. Part 4 provides the full data model that has been in development throughout the book. The model was created using Jotne EPM Technologys EDMVisualExpress data modeling tool.

This book was designed for all types of modelers: from those who understand data modeling basics but are just starting to learn about data modeling in practice, through to experienced data modelers seeking to expand their knowledge and skills and solve some of the more challenging problems of data modeling.

• Uses a number of common data model patterns to explain how to develop data models over a wide scope in a way that is consistent and of high quality
• Offers generic data model templates that are reusable in many applications and are fundamental for developing more specific templates
• Develops ideas for creating consistent approaches to high quality data models

About the Author

Matthew West spent over 20 years as a leading data modeler for Shell where he was a key technical contributor to data modeling and data management standards and their application. Matthew was responsible for Shell's Downstream Data Model. He currently serves as the Director of Information Junction, a data architecture and analysis consultancy in the UK. He is also a key contributor to ISO 15926 (Lifecycle integration of process data) and ISO 8000 (Data and Information Quality). Matthew is a Visiting Professor at the University of Leeds