This book introduces Logical Data Structures (LDS), a powerful new approach to database design that can dramatically improve the cost-effectiveness and business value of any enterprise database system or database-driven application. The authors introduce a new notation, new diagramming approach, and new user-centered, high-ROI techniques for modeling even the most complex, high-volume database applications. KEY TOPICS: This book starts from first principles, asking and answering crucial questions like: "To best serve users, how should the process of data modeling work? To create good, economical software systems, what kind of information should be on a data model? To become an effective data modeler, what skills should you master before talking with users?" Next, it teaches data modeling using a powerful new notation, Logical Data Structures (LDS), designed to encourage a user-centered, requirements approach that leads directly to more effective applications. The authors introduce "good habits" every data modeler should exhibit; provide formal definitions of an effective LDS-based data model; show how to explain LDS models to domain experts and others without data modeling expertise; and walk you through the entire process of creating and enhancing a data model. MARKET: For all database administrators, analysts, designers, and architects, and for all IT managers responsible for enterprise database system management or deployment.
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John Carlis is on the faculty in the Department of Computer Science at the University of Minnesota. For the past twenty years he has taught, consulted, and conducted research on database systems, particularly in data modeling and database language extensions. Visit his homepage at www.cs.umn.edu/~carlis.
Joseph Maguire is an independent consultant and the creator of the forthcoming Web site www.logicaldatastructures.com. For the past 18 years he has been an employee or consultant for many companies, including Bachman Information Systems, Digital, Lotus, Microsoft, and US WEST.
Data modeling is one of the most critical phases in the database application development process, but also the phase most likely to fail. A master data modeler must come into any organization, understand its data requirements, and skillfully model the data for applications that most effectively serve organizational needs.
Mastering Data Modeling is a complete guide to becoming a successful data modeler. Featuring a requirements-driven approach, this book clearly explains fundamental concepts, introduces a user-oriented data modeling notation, and describes a rigorous, step-by-step process for collecting, modeling, and documenting the kinds of data that users need.
Assuming no prior knowledge, Mastering Data Modeling sets forth several fundamental problems of data modeling, such as reconciling the software developer's demand for rigor with the users' equally valid need to speak their own (sometimes vague) natural language. In addition, it describes the good habits that help you respond to these fundamental problems. With these good habits in mind, the book describes the Logical Data Structure (LDS) notation and the process of controlled evolution by which you can create low-cost, user-approved data models that resist premature obsolescence. Also included is an encyclopedic analysis of all data shapes that you will encounter. Most notably, the book describes The Flow, a loosely scripted process by which you and the users gradually but continuously improve an LDS until it faithfully represents the information needs. Essential implementation and technology issues are also covered.
You will learn about such vital topics as:
"Story interludes" appear throughout the book, illustrating real-world successes of the LDS notation and controlled evolution process. Numerous exercises help you master critical skills. In addition, two detailed, annotated sample conversations with users show you the process of controlled evolution in action.
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