1. Ihr Logo Fundamentals of Database Systems Fourth Edition El Masri & Navathe Chapter 10 Functional Dependencies and Normalization for Relational Databases

2. Your Logo 2 Functional Dependencies Functional dependencies (FDs) are used to specify formal measures of the "goodness" of relational designs. FDs and keys are used to define normal forms for relations FDs are constraints that are derived from the meaning and interrelationships of the data attributes.

3. Your Logo 3 Functional Dependencies A set of attributes X functionally determines a set of attributes Y if the value of X determines a unique value for Y X  Y X  Y holds if whenever two tuples have the same value for X, they must have the same value for Y. X  Y in R specifies a constraint on all relation instances r(R).

4. Your Logo 4 Examples of FD constraints social security number determines employee name SSN  ENAME project number determines project name and location PNUMBER  {PNAME, PLOCATION} City name determines ZIP code City  ZIP Employee’s ssn and project number determines the hours per week that the employee works on the project {Level, Experience_Years}  Salary

5. Your Logo 5 Examples of FD constraints If K is a key of R, then K functionally determines all attributes in R (since we never have two distinct tuples with t1[K]=t2[K]) So: Keys are Special Cases of FDs

6. Your Logo 6 Inference Rules for FDs Given a set of FDs F, we can infer additional FDs that hold whenever the FDs in F hold. Armstrong's inference rules: IR1. (Reflexive) If Y is subset of X, then X  Y IR2. (Augmentation) If X  Y, then XZ  YZ (Notation: XZ stands for X U Z) IR3. (Transitive) If X  Y and Y  Z, then X -> Z

7. Your Logo 7 Inference Rules for FDs (Cont.) IR1, IR2, IR3 form a sound and complete set of inference rules Some additional inference rules that are useful: (Decomposition) If X  YZ, then X  Y and X  Z (Union) If X  Y and X  Z, then X  YZ (Pseudo transitivity) If X  Y and WY  Z, then WX  Z The last three inference rules, as well as any other inference rules, can be deduced from IR1, IR2, and IR3 (completeness property)

8. Your Logo 8 F Closure Closure of a set F of FDs is the set F + of all FDs that can be inferred from F. F + =F +{all FDs that can be logically imply from F using the inference rules} So F ≡ F +

9. Your Logo 9 F Closure Example: R(A,B,C,D,E,G,H) F= { B  CD, A  C, E  GA, C  EH } Find F + ? F + = { B  CD, A  C, E  GA, C  EH, B  C, B  D, E  G, E  A, C  E, C  H, B  E, B  H, …. ….}

10. Your Logo 10 Attributes Closure Closure of a set of attributes X with respect to F is the set X + of all attributes that are functionally determined by X Given a relation schema R and a set of FDs F that hold on R, Let X belongs to R ( X is a set of attributes that found in R) X + = X + { all attributes that can be derived from X using F}

11. Your Logo 11 Attributes Closure Example: R(A,B,C,D,E,G,H) F= { B  CD, A  C, E  GA, C  EH } A + = ACEHG (CE) + = CEHGA B + = BCDEHGA Note: Here B Give me all other attributes, So B is a key for R. So We can Use X + to find Keys for any relation

12. Your Logo 12 Minimal Cover of F Also called Canonical Cover of F and is denoted by F c Every set of FDs has an equivalent F c. There can be several equivalent minimal sets.

13. Your Logo 13 Minimal Cover of F Compute F c from F 1.Let Fc  F. 2.Repeat the following steps until no more reduction can be made on Fc A.Remove all redundant attributes from X in X  Y Ex. A  B, AC  B (C is redundant) B.Remove all redundant attributes from Y in X  Y Ex. A  BC, B  C (C is redundant) C.Replace each pair of FDs of the form X  Y 1, X  Y 2 By X  Y 1 Y 2 Ex. A  B, A  C Then A  BC

14. Your Logo 14 Minimal Cover of F Example F= { A  C, AC  D, E  AD, E  H } 1)Remove C from AC  D 2)Then Convert A  C, A  D to A  CD 3)Then Convert E  AD, E  H to E  ADH 4)Remove D from E  ADH Then Fc is { A  DC, E  AH} Find Fc?

15. Your Logo 15 Problems caused by Bad Database Design  Wastes storage  Causes problems with update anomalies Insertion anomalies Deletion anomalies Modification anomalies

16. Your Logo 16 EXAMPLE OF AN UPDATE ANOMALY  Consider the relation: EMP_PROJ(Emp#, Proj#, Ename, Pname, No_hours)  Update Anomaly: Changing the name of project number P1 from “Billing” to “Customer-Accounting” may cause this update to be made for all 100 employees working on project P1.

17. Your Logo 17 EXAMPLE OF AN INSERT ANOMALY  Consider the relation: EMP_PROJ(Emp#, Proj#, Ename, Pname, No_hours)  Insert Anomaly: Cannot insert a project unless an employee is assigned to it.  Conversely Cannot insert an employee unless a he/she is assigned to a project.

18. Your Logo 18 EXAMPLE OF AN DELETE ANOMALY  Consider the relation: EMP_PROJ(Emp#, Proj#, Ename, Pname, No_hours)  Delete Anomaly: When a project is deleted, it will result in deleting all the employees who work on that project. Alternately, if an employee is the sole employee on a project, deleting that employee would result in deleting the corresponding project.