Data Modeling for Information Professionals

Data Modeling for Information Professionals

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Understand what data models are, what makes them successful, and what makes them fail. Walk through every element of a data model, including domains, predicates, entities, classes, relationships and attributes, learning through extensive examples, case studies and games. The ideas in this book have been earning raves since they were first introduced as part of a computer-based education program in 1992. Honed through six years of user and student feedback, they're even more valuable now.
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Product details

  • Hardback | 320 pages
  • 182.88 x 236.22 x 40.64mm | 748.42g
  • Prentice Hall
  • Upper Saddle River, United States
  • English
  • 0130804509
  • 9780130804501

Table of contents

Should You Study This?

Does a Thing Called Data Modeling Exist?

About This Text.

Data Modeling Overview.

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.


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.


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.


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.


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.

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