Data Modeling for Information Professionals
Published on 2. September 1998
Book
Hardback
320 pages
978-0-13-080450-1 (ISBN)
Description
8045k-6 "A powerful, yet easy-to-use resource for training people in data modeling principles. I highly recommend it for anyone who needs to develop data modeling competence." Clive Finkelstein, Information Engineering Services, www.ies.aust.com/~ieinfo. "An outstanding vehicle for learning the mysteries of data/object modeling." David Hay, President, Essential Strategies, Inc., www.essentialstrategies.com, author of Data Model Patterns, The most fun you can have learning data modeling! No matter what role you play in managing information, you need an in-depth understanding of how to structure data. Data Modeling for Information Professionals gives you what you need - painlessly! Based on an interactive course that's been earning raves for years, it's the informal, friendly, real-world introduction to data modeling. *Discover what data models are, what makes them successful, and what makes them fail. *Walk through every component of an enterprise data model. *Understand domains, predicates, entities, classes, relationships, attributes, and more. *Learn from enterprise case studies and extensive nontrivial examples.*
Great for data administrators, analysts, SMEs, DBAs, and project managers! Comprehensive, insightful, and entertaining, Data Modeling for Information Professionals is the easy way to learn the data modeling techniques you can't afford not to know! REPOSITORY ON CD-ROM The many illustrations in this book expand and link when you launch the free data model using the included SILVERRUN CASE tool. Export this royalty-free model to jump start your own work and to practice using your own CASE tool. Or build your model u sing SILVERRUN, a leading tool for multiplatform, enterprise-capable business modeling.
Great for data administrators, analysts, SMEs, DBAs, and project managers! Comprehensive, insightful, and entertaining, Data Modeling for Information Professionals is the easy way to learn the data modeling techniques you can't afford not to know! REPOSITORY ON CD-ROM The many illustrations in this book expand and link when you launch the free data model using the included SILVERRUN CASE tool. Export this royalty-free model to jump start your own work and to practice using your own CASE tool. Or build your model u sing SILVERRUN, a leading tool for multiplatform, enterprise-capable business modeling.
More details
Language
English
Place of publication
Upper Saddle River
United States
Publishing group
Pearson Education (US)
Target group
College/higher education
Dimensions
Height: 242 mm
Width: 185 mm
Thickness: 29 mm
Weight
900 gr
ISBN-13
978-0-13-080450-1 (9780130804501)
Copyright in bibliographic data is held by Nielsen Book Services Limited or its licensors: all rights reserved.
Schweitzer Classification
Content
Should You Study This?
Does a Thing Called Data Modeling Exist?
About This Text.
Data Modeling Overview.
1. BASIC PARTS.
Concept 1: Domains.
Why Should I Study Domains? Of What Are Domains Composed? Domains Measure. Naked Domain. Non-numeric Domains. Sets of Domains. So Where Are We? Here's What You Should Have Learned.
Concept 2: Names.
Why Should I Care a Whit About a Nit Like Names? What Is a Name? Names and Domains. When Names Won't Do. Postscript. Here's What You Should Have Learned.
Concept 3: Predicates.
Why Should I Bother to Read About Predicates? Predicates Describe Something. Predicates Are What Can Be Known. Predicates Have Meaning. What Is a Predicate? How to Know What to Know. What Is It About? Predicates Can Describe Other Predicates.What Answers Do You Expect? Modifiable Predicates. Where Will You Get Your Answers? Here's What You Should Have Learned.
2. SETS OF PREDICATES.
Concept 4: Entities.
Why Should I Study Entities? What Is an Entity? Entities Are Describable. Entities Are Elemental. Entities Are Identifiable. Some Entities Are Not Unique. Some Entities Don't Start Out Unique. An Entity Is Something About Which We Keep Data. An Entity Is a Set of Predicates. Finding a Level of Relevance. Entities That Group. Here's What You Should Have Learned.
Concept 5: States.
Why Should I Care About States? State Analysis Can Lead to Business Process Improvement. What Is a State? States Integrate Perspectives. How States Relate to Each Other. Multiple Statehood. The Relation of States to Hidden Modifiers. Relevance of the Data From State to State. "Becomes a" vs. "Is a Part of". Here's What You Should Have Learned.
3. SETS OF ENTITIES.
Concept 6: Classes.
Why Should I Study Classes? What Is a Class? Groups of Similar Things. An Entity Is What It Is. Do Classes Have Predicates? A Place for Everything. What Really Happens on a Project. Here's What You Should Have Learned.
Concept 7: Superclasses.
Why Do I Need to Bother With Superclasses?What Is a Superclass? Generalizing. Specializing. Entities of Many Classes. Superclasses Serve Some Purpose. Do Not Simply Put All Instances Into the Same Class. Do Not Create Superclasses Just to Hold Common Predicates. Too Many Classes? Here's What You Should Have Learned.
4. BACK TO PREDICATES.
Concept 8: Relationships.
What Is a Relationship? Optionality or Minimum Cardinality. Degree. Ten Possible Relationships. 1 to M Relationships. M to M Relationships. 1 to 1 Relationships. Here's What You Should Have Learned.
Concept 9: Attributes.
What Is an Attribute? Attributes and Entities. Attributes and Domains. Description Is Not an Attribute. Consistent Precision. Multiple Choices. Three Types of Attributes. More on Elemental Attributes. Here's What You Should Have Learned.
5, CONCLUSION.
You Could Do Everything Right...but Still Make a Mess.
Don't Pick a Fight With Your Friends. Too Much of a Good Thing. Knowing Too Much. Looking for Trouble. Hard to Say. Wet Noodle Syndrome. Lost in the House of Mirrors. Here's What You Should Have Learned. If I Had More Time...I'd Have Written Less.
Appendix.
Index.
Does a Thing Called Data Modeling Exist?
About This Text.
Data Modeling Overview.
1. BASIC PARTS.
Concept 1: Domains.
Why Should I Study Domains? Of What Are Domains Composed? Domains Measure. Naked Domain. Non-numeric Domains. Sets of Domains. So Where Are We? Here's What You Should Have Learned.
Concept 2: Names.
Why Should I Care a Whit About a Nit Like Names? What Is a Name? Names and Domains. When Names Won't Do. Postscript. Here's What You Should Have Learned.
Concept 3: Predicates.
Why Should I Bother to Read About Predicates? Predicates Describe Something. Predicates Are What Can Be Known. Predicates Have Meaning. What Is a Predicate? How to Know What to Know. What Is It About? Predicates Can Describe Other Predicates.What Answers Do You Expect? Modifiable Predicates. Where Will You Get Your Answers? Here's What You Should Have Learned.
2. SETS OF PREDICATES.
Concept 4: Entities.
Why Should I Study Entities? What Is an Entity? Entities Are Describable. Entities Are Elemental. Entities Are Identifiable. Some Entities Are Not Unique. Some Entities Don't Start Out Unique. An Entity Is Something About Which We Keep Data. An Entity Is a Set of Predicates. Finding a Level of Relevance. Entities That Group. Here's What You Should Have Learned.
Concept 5: States.
Why Should I Care About States? State Analysis Can Lead to Business Process Improvement. What Is a State? States Integrate Perspectives. How States Relate to Each Other. Multiple Statehood. The Relation of States to Hidden Modifiers. Relevance of the Data From State to State. "Becomes a" vs. "Is a Part of". Here's What You Should Have Learned.
3. SETS OF ENTITIES.
Concept 6: Classes.
Why Should I Study Classes? What Is a Class? Groups of Similar Things. An Entity Is What It Is. Do Classes Have Predicates? A Place for Everything. What Really Happens on a Project. Here's What You Should Have Learned.
Concept 7: Superclasses.
Why Do I Need to Bother With Superclasses?What Is a Superclass? Generalizing. Specializing. Entities of Many Classes. Superclasses Serve Some Purpose. Do Not Simply Put All Instances Into the Same Class. Do Not Create Superclasses Just to Hold Common Predicates. Too Many Classes? Here's What You Should Have Learned.
4. BACK TO PREDICATES.
Concept 8: Relationships.
What Is a Relationship? Optionality or Minimum Cardinality. Degree. Ten Possible Relationships. 1 to M Relationships. M to M Relationships. 1 to 1 Relationships. Here's What You Should Have Learned.
Concept 9: Attributes.
What Is an Attribute? Attributes and Entities. Attributes and Domains. Description Is Not an Attribute. Consistent Precision. Multiple Choices. Three Types of Attributes. More on Elemental Attributes. Here's What You Should Have Learned.
5, CONCLUSION.
You Could Do Everything Right...but Still Make a Mess.
Don't Pick a Fight With Your Friends. Too Much of a Good Thing. Knowing Too Much. Looking for Trouble. Hard to Say. Wet Noodle Syndrome. Lost in the House of Mirrors. Here's What You Should Have Learned. If I Had More Time...I'd Have Written Less.
Appendix.
Index.