Data Modeling For Quality
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Developing High Quality Data Models
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
Data Modeling for Quality
This book is for all data modelers, data architects, and database designers―be they novices who want to learn what's involved in data modeling, or experienced modelers who want to brush up their skills. A novice will not only gain an overview of data modeling, they will also learn how to follow the data modeling process, including the activities required for each step. The experienced practitioner will discover (or rediscover) techniques to ensure that data models accurately reflect business requirements. This book describes rigorous yet easily implemented approaches to: modeling of business information requirements for review by business stakeholders before development of the logical data model normalizing data, based on simple questions rather than the formal definitions which many modelers find intimidating naming and defining concepts and attributes modeling of time-variant data documenting business rules governing both the real world and data data modeling in an Agile project managing data model change in any type of project transforming a business information model to a logical data model against which developers can code implementing the logical data model in a traditional relational DBMS, an SQL:2003-compliant DBMS, an object-relational DBMS, or in XML. Part 1 describes business information models in-depth, including: the importance of modeling business information requirements before embarking on a logical data model business concepts (entity classes) attributes of business concepts attribute classes as an alternative to DBMS data types relationships between business concepts time-variant data generalization and specialization of business concepts naming and defining the components of the business information model business rules governing data, including a distinction between real-world rules and data rules. Part 2 journeys from requirements to a working data resource, covering: sourcing data requirements developing the business information model communicating it to business stakeholders for review, both as diagrams and verbally managing data model change transforming the business information model into a logical data model of stored data for implementation in a relational or object-relational DBMS attribute value representation and data constraints (important but often overlooked) modeling data vault, dimensional and XML data.
The Data Modeling Handbook
Author: Michael C. Reingruber
language: en
Publisher: John Wiley & Sons
Release Date: 1994-12-17
This practical, field-tested reference doesn't just explain the characteristics of finished, high-quality data models--it shows readers exactly how to build one. It presents rules and best practices in several notations, including IDEFIX, Martin, Chen, and Finkelstein. The book offers dozens of real-world examples and go beyond basic theory to provide users with practical guidance.