Class Agenda (03/29 through 04/10)

1. Class Agenda (03/29 through 04/10) • Review HW #7 (do this on 4/3/2012) • Present normalization process • Enhance conceptual knowledge of database design. • Improve practical skills of database design. • Approach • Review goals of database design. • Identify and define vocabulary for normalization. • Do an “intuitive” database design for a refresher. • Discuss the characteristics of the three normal forms and the characteristics of a data model in third normal form. • Use the normalization process to do the same database design. • Compare results.

2. Review of database design goals • Protect the integrity of the data. • Reduce data redundancy. • Prevent data anomalies. • Provide for change. • Prevent inflexible data structures. • Anticipate changes. • Provide access to complete data for decision making.

3. What are data anomalies? • An anomaly is a potential error or inconsistency in the data. • Data anomalies are most frequently caused by the implementation of M:N relationships. • A M:N relationship can be implemented through a “false” 1:M relationship.

4. Example of a “False” 1:m Relationship Order 12100 5613 02-27-2012 03-10-2012 200 net30 4.99 11 12100 7816 02-27-2012 03-10-2012 200 net30 45.89 15 12100 5613 03-12-2012 03-26-2012 200 net30 4.78 50 12250 4512 02-30-2012 03-23-2012 231 cod 9.99 87 12250 5622 03-12-2012 03-18-2012 231 cod 5.70 25 Product 5613 tumbler 12 ozea inventory 50 452 Sacramento South 9162234551 5613 tumbler 12 ozea inventory 50 455 Sacramento North 9167817273 7816 food processor ea inventory 15 455 Sacramento North 9167817273 5622 paper rm supplies 25 452 Sacramento South 9162244551 4512 glass pitcher ea inventory 75 488 Reno 7753314551

5. Redundant Data • What data is redundant in the previous slide? • Is redundant data really a problem? Why or why not? • Do the primary keys for both entities yield unique values for each row in the table?

6. Potential anomalies in the example • Insertion anomaly: Can’t add “some” of a row; must have all the key attributes. Example - Suppose we need to add a new warehouse? • Deletion anomaly: Lose some relevant data when deleting other data. Example - What happens to the Reno warehouse information (name and phone#) when we delete item #4512? • Update anomaly: Must update more than one row when one piece of data changes. Examples - What happens if the telephone number at the Sacramento North warehouse changes? What happens if the date the purchase order was placed is entered incorrectly and must be updated?

7. Other problems with “false” 1:m relationships • What happens when the database design grows or changes? • How do you add new data attributes? • What about keeping track of the buyer for an order? • Or the manager for a warehouse?

8. What is normalization? • Normalization is a formal, process-oriented approach to data modeling. • Normalization is the process of: • examining groups of data attributes; • splitting them into appropriate entities; • identifying the relationships between the entities; and • identifying appropriate primary and foreign keys.

9. Database Normalization • What will you know about database normalization? • Define normalization. • Know the vocabulary of normalization. • Understand the process of normalization. • Better understand the characteristics of an effective database design. • What will you be able to do? • Be able to identify the characteristics of each normal form. • Be able to tell whether or not a data model is in third normal form. • Potentially be able to use normalization to assist you in the design of a database.

10. Same old/same old • Normalization should sound like what you have already done during database design. • The ultimate goals of design have not changed; we are just going to go about it in a slightly different way. • Let’s start with an application and doing it through “intuitive” database design. • Student information grade screen design exercise

11. Normalization process • Some refer to this as the “bottom-up” form of database design. • Contrast with the more intuitive “top-down” approach we have been using. • The results from the normalization process are stable, flexible entities. The results from the intuitive approach should be the same.

12. Two methods of applying normalization • Use it to help in designing a database. • Normalization starts with a single entity. • Normalization breaks that entity into a series of additional entities. • More entities are discovered and named during the process. • Entities are linked during the process. • Use it to validate the design of a database. • Identify entities from the meaning of the data. • Create conceptual and logical data models. • Apply the rules of normalization to ensure a stable, non-redundant design.

13. Vocabulary for normalization • A “functional dependency" is a relationship between attributes in which one attribute or group of attributes determines the value of another. • A “determinant” is an attribute or group of attributes that, once known, can determine the value of another attribute.

14. Examples of functional dependencies and determinants • A social security number determines your name and address. SSN  name, address. • A vehicle id number determines the make and model of a car. VIN  make, model. • Name and address are “functionally dependent” on SSN. • SSN “determines” name and address. • Functional dependency diagram format: • CourseID CourseName, CourseDescription, CourseCredits • ZipCode City, State • PatientID, TreatmentDateTime TestResults

15. Normalization process • Normalization is accomplished in stages. A “normal form” is a state (level of completeness) of a data model. • Unnormalized data: A data model that has not been normalized. It contains repeating groups and is not a stable model. • Unnormalized data is essentially one entity. The system under analysis is categorized as a single entity.

16. Steps/forms/phases in Normalization • First normal form: Remove repeating groups. • Second normal form: Remove partial functional dependencies. • Third normal form: Remove transitive dependences

17. Unnormalized data for grade report exercise • Semester • Year • Student Name • Student Address • Student City • Student State • Student Zip Code • Student ID • Student College • Student Major • Student Minor • Student Year • Course ID • Course Title • Course Instructor • Course Credits • Grade What attributes might be needed that aren’t visible on the grade report? Group all attributes in one “big” entity. Identify a primary key for the entity. Maybe studentID for this one.

18. First Normal Form • First normal form: Remove repeating groups. • A repeating group is an attribute or group of attributes that can have more than one value for an instance of an entity. If it is a single attribute, we have been calling it a “multi-valued” attribute. • To get a data model into first normal form: • Identify repeating groups and place them as separate entities in the model. • Identify a primary key for the repeating group. The key may be concatenated. • Create the relationships between entities. • Divide m:n relationships with appropriate intersection entities.

19. Second Normal Form • Second normal form: Remove partial functional dependencies. • A partial functional dependency is a situation in which one or more non-key attributes are functionally dependent on part, but not all, of the primary key. • Partial functional dependencies occur only with concatenated keys. • Examples of partial functional dependencies: • PatientID, TreatmentDateTime PatName, TstResults, TrtID, LocID • CourseID, StudentID  CourseTitle, Grade • Which entities developed during the transition to first normal form for the grade report have concatenated keys?

20. Third normal form • Third normal form: Remove transitive dependencies. • A transitive dependency occurs when a non-key attribute is functionally dependent on one or more non-key attributes. • Third normal form examines entities with single primary keys and removes the “floating” or transitive dependencies. • It may be possible to have attributes that are determined by other attributes, rather than by the primary key. They must be removed into entities with appropriate primary keys. • Example of partial functional dependency: • PatID, TrtDateTime  TstResults, TrtType, TrtDescription, LocName, TrtID, LocID

21. Summary of normalization process • Examine and evaluate the logical data model for effectiveness. • Find the repeating groups and put the model into first normal form. Identify primary key fields for any new entities. Relate entities with foreign keys. • Find the functional dependencies. Identify the partial functional dependencies and put the model into second normal form. Identify primary key fields for any new entities. Relate entities with foreign keys. • Find the transitive dependencies and put the model into third normal form. Identify primary key fields for any new entities. Relate entities with foreign keys.

22. Goal of normalization A set of entities where each attribute in each entity is dependent on the primary key, the whole primary key, and nothing but the primary key.