Sam S. Lightstone

Toby J. Teorey

Patrick O'Neil

Jan L. Harrington

Jim Melton

Stephen Buxton

Graham Witt

Ralf Hartmut Güting

Markus Schneider

Graeme Simsion

Thomas P. Nadeau

Tony Morgan

W.H. Inmon

Bonnie O'Neil

Lowell Fryman

Terry Halpin

Elizabeth O'Neil

This book brings all of the elements of database design together in a single volume, saving the reader the time and expense of making multiple purchases. It consolidates both introductory and advanced topics, thereby covering the gamut of database design methodology ? from ER and UML techniques, to conceptual data modeling and table transformation, to storing XML and querying moving objects databases. The proposed book expertly combines the finest database design material from the Morgan Kaufmann portfolio. Individual chapters are derived from a select group of MK books authored by the best and brightest in the field. These chapters are combined into one comprehensive volume in a way that allows it to be used as a reference work for those interested in new and developing aspects of database design. This book represents a quick and efficient way to unite valuable content from leading database design experts, thereby creating a definitive, one-stop-shopping opportunity for customers to receive the information they would otherwise need to round up from separate sources. Chapters contributed by various recognized experts in the field let the reader remain up to date and fully informed from multiple viewpoints. Details multiple relational models and modeling languages, enhancing the reader’s technical expertise and familiarity with design-related requirements specification. Coverage of both theory and practice brings all of the elements of database design together in a single volume, saving the reader the time and expense of making multiple purchases.

Database Design: Know It All

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.

Data Modeling for Quality

The world of rules -- How rules work -- A brief history of rules -- Types of rules -- The building blocks of natural language rule statements -- Fact Models -- How to write quality natural language rule statements -- An end-to-end rule management methodology -- Rule statement templates and subtemplates.

Writing Effective Business Rules

Data Modeling Essentials, Third Edition, covers the basics of data modeling while focusing on developing a facility in techniques, rather than a simple familiarization with "the rules". In order to enable students to apply the basics of data modeling to real models, the book addresses the realities of developing systems in real-world situations by assessing the merits of a variety of possible solutions as well as using language and diagramming methods that represent industry practice. This revised edition has been given significantly expanded coverage and reorganized for greater reader comprehension even as it retains its distinctive hallmarks of readability and usefulness. Beginning with the basics, the book provides a thorough grounding in theory before guiding the reader through the various stages of applied data modeling and database design. Later chapters address advanced subjects, including business rules, data warehousing, enterprise-wide modeling and data management. It includes an entirely new section discussing the development of logical and physical modeling, along with new material describing a powerful technique for model verification. It also provides an excellent resource for additional lectures and exercises. This text is the ideal reference for data modelers, data architects, database designers, DBAs, and systems analysts, as well as undergraduate and graduate-level students looking for a real-world perspective. Thorough coverage of the fundamentals and relevant theory. Recognition and support for the creative side of the process. Expanded coverage of applied data modeling includes new chapters on logical and physical database design. New material describing a powerful technique for model verification. Unique coverage of the practical and human aspects of modeling, such as working with business specialists, managing change, and resolving conflict.

Data Modeling Essentials

Graham Witt

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