1. Lecture 6: Design Constraints and Functional Dependencies January 21st, 2004

2. Administration Questions about project? Anything else?

3. Outline End of conceptual modeling Functional dependencies (3.4) Rules about FDs (3.5) Design of a Relational schema (3.6)

4. Modeling Subclasses The world is inherently hierarchical. Some entities are special cases of others We need a notion of subclass. This is supported naturally in object-oriented formalisms. Products Software products Educational products

5. Product namecategory price isa Educational ProductSoftware Product Age Groupplatforms Subclasses in E/R Diagrams

6. Understanding Subclasses Think in terms of records: –Product –SoftwareProduct –EducationalProduct field1 field2 field1 field2 field1 field2 field3 field4 field5

7. Subclasses to Relations Product namecategory price isa Educational ProductSoftware Product Age Groupplatforms NamePriceCategory Gizmo99gadget Camera49photo Toy39gadget Nameplatforms Gizmounix NameAge Group Gizmotodler Toyretired Product Sw.Product Ed.Product

8. Modeling UnionTypes With Subclasses FurniturePiece Person Company Say: each piece of furniture is owned either by a person, or by a company

9. Modeling Union Types with Subclasses Say: each piece of furniture is owned either by a person, or by a company Solution 1. Acceptable, imperfect (What’s wrong ?) FurniturePiecePerson Company ownedByPerson

10. Modeling Union Types with Subclasses Solution 2: better, more laborious isa FurniturePiece Person Company ownedBy Owner isa

11. Constraints in E/R Diagrams Finding constraints is part of the modeling process. Commonly used constraints: Keys: social security number uniquely identifies a person. Single-value constraints: a person can have only one father. Referential integrity constraints: if you work for a company, it must exist in the database. Other constraints: peoples’ ages are between 0 and 150.

12. Keys in E/R Diagrams address namessn Person Product namecategory price No formal way to specify multiple keys in E/R diagrams Underline:

13. Single Value Constraints makes v. s.

14. Referential Integrity Constraints CompanyProduct makes CompanyProduct makes

15. Other Constraints CompanyProduct makes <100 What does this mean ?

16. Weak Entity Sets Entity sets are weak when their key comes from other classes to which they are related. UniversityTeam affiliation numbersportname

17. Handling Weak Entity Sets UniversityTeam affiliation numbersportname Convert to a relational schema (in class)

18. Relational Schema Design Person buys Product name pricenamessn Conceptual Model: Relational Model: plus FD’s Normalization: Eliminates anomalies

19. Functional Dependencies Definition: A 1,..., A m  B 1,..., B n holds in R if:  t, t’  R, (t.A 1 =t’.A 1 ...  t.A m =t’.A m  t.B 1 =t’.B 1 ...  t.B m =t’.B m ) A1...AmB1...Bm if t, t’ agree here then t, t’ agree here t t’ R

20. Important Point! Functional dependencies are part of the schema! They constrain the possible legal data instances. At any point in time, the actual database may satisfy additional FD’s.

21. Examples EmpID Name, Phone, Position Position Phone but Phone Position EmpIDNamePhonePosition E0045Smith1234Clerk E1847John9876Salesrep E1111Smith9876Salesrep E9999Mary1234Lawyer

22. Formal definition of a key A key is a set of attributes A 1,..., A n s.t. for any other attribute B, A 1,..., A n  B A minimal key is a set of attributes which is a key and for which no subset is a key Note: book calls them superkey and key

23. Examples of Keys Product(name, price, category, color) name, category  price category  color Keys are: {name, category} and all supersets Enrollment(student, address, course, room, time) student  address room, time  course student, course  room, time Keys are: [in class]

24. Finding the Keys of a Relation Given a relation constructed from an E/R diagram, what is its key? Rules: 1. If the relation comes from an entity set, the key of the relation is the set of attributes which is the key of the entity set. address namessn Person Person(address, name, ssn)

25. Finding the Keys Person buys Product name pricenamessn buys(name, ssn, date) date Rules: 2. If the relation comes from a many-many relationship, the key of the relation is the set of all attribute keys in the relations corresponding to the entity sets

26. Finding the Keys Except: if there is an arrow from the relationship to E, then we don’t need the key of E as part of the relation key. Purchase Product Person Store CreditCard name card-no ssn sname Purchase(name, sname, ssn, card-no)

27. Expressing Dependencies Say: “the CreditCard determines the Person” Purchase Product Person Store CreditCard name card-no ssn sname Purchase(name, sname, ssn, card-no) Incomplete (what does it say ?) card-no  name

28. Finding the Keys More rules in the book – please read !

29. Inference Rules for FD’s A, A, … A 12n B, B, … B 12m A, A, … A 12n 1 Is equivalent to B A, A, … A 12n 2 B 12n m B … Splitting rule and Combing rule A1...AmB1...Bm

30. Inference Rules for FD’s (continued) A, A, … A 12n i A Trivial Rule Why ? A1...Am where i = 1, 2,..., n

31. Inference Rules for FD’s (continued) A, A, … A 12n Transitive Closure Rule B, B, … B 12m A, A, … A 12n 1 B, B …, B 2m1 C, C …, C 2p1 2p If and then Why ?

32. A1...AmB1...BmC1C1...CpCp

33. Enrollment(student, major, course, room, time) student  major major, course  room course  time What else can we infer ? [in class]

34. Closure of a set of Attributes Given a set of attributes {A1, …, An} and a set of dependencies S. Problem: find all attributes B such that: any relation which satisfies S also satisfies: A1, …, An B The closure of {A1, …, An}, denoted {A1, …, An}, is the set of all such attributes B +

35. Closure Algorithm Start with X={A1, …, An}. Repeat until X doesn’t change do: if is in S, and C is not in X then add C to X. B, B, … B 12n C 12 n are all in X, and

36. Example A B C A D E B D A F B Closure of {A,B}: X = {A, B, } Closure of {A, F}: X = {A, F, } R(A,B,C,D,E,F)

37. Why Is the Algorithm Correct ? Show the following by induction: –For every B in X: A1, …, An B Initially X = {A1, …, An} -- holds Induction step: B1, …, Bm in X –Implies A1, …, An B1, …, Bm –We also have B1, …, Bm C –By transitivity we have A1, …, An C This shows that the algorithm is sound; need to show it is complete

38. Relational Schema Design (or Logical Design) Main idea: Start with some relational schema Find out its FD’s Use them to design a better relational schema

39. Relational Schema Design Anomalies: Redundancy = repeat data Update anomalies = Fred moves to “Bellvue” Deletion anomalies = Fred drops all phone numbers: what is his city ? Recall set attributes (persons with several phones): SSN  Name, City, but not SSN  PhoneNumber NameSSNPhoneNumberCity Fred123-45-6789206-555-1234Seattle Fred123-45-6789206-555-6543Seattle Joe987-65-4321908-555-2121Westfield Joe987-65-4321908-555-1234Westfield

40. Relation Decomposition Break the relation into two: NameSSNCity Fred123-45-6789Seattle Joe987-65-4321Westfield SSNPhoneNumber 123-45-6789206-555-1234 123-45-6789206-555-6543 987-65-4321908-555-2121 987-65-4321908-555-1234

41. Relational Schema Design Person buys Product name pricenamessn Conceptual Model: Relational Model: plus FD’s Normalization: Eliminates anomalies

42. Decompositions in General R(A 1,..., A n ) Create two relations R1(B1,..., Bm) and R2(C1,..., Cp) such that: B1,..., Bm  C1,..., Cp = A1,..., An and: R 1 = projection of R on B 1,..., B m R 2 = projection of R on C 1,..., C p

43. Incorrect Decomposition Sometimes it is incorrect: NamePriceCategory Gizmo19.99Gadget OneClick24.99Camera DoubleClick29.99Camera Decompose on : Name, Category and Price, Category

44. Incorrect Decomposition NameCategory GizmoGadget OneClickCamera DoubleClickCamera PriceCategory 19.99Gadget 24.99Camera 29.99Camera NamePriceCategory Gizmo19.99Gadget OneClick24.99Camera OneClick29.99Camera DoubleClick24.99Camera DoubleClick29.99Camera When we put it back: Cannot recover information

45. Normal Forms First Normal Form = all attributes are atomic Second Normal Form (2NF) = old and obsolete Third Normal Form (3NF) = this lecture Boyce Codd Normal Form (BCNF) = this lecture Others...

46. Boyce-Codd Normal Form A simple condition for removing anomalies from relations: In English (though a bit vague): Whenever a set of attributes of R is determining another attribute, should determine all the attributes of R. A relation R is in BCNF if: Whenever there is a nontrivial dependency A 1,..., A n  B in R, {A 1,..., A n } is a key for R A relation R is in BCNF if: Whenever there is a nontrivial dependency A 1,..., A n  B in R, {A 1,..., A n } is a key for R

47. Example What are the dependencies? SSN  Name, City What are the keys? {SSN, PhoneNumber} Is it in BCNF? NameSSNPhoneNumberCity Fred123-45-6789206-555-1234Seattle Fred123-45-6789206-555-6543Seattle Joe987-65-4321908-555-2121Westfield Joe987-65-4321908-555-1234Westfield

48. Decompose it into BCNF NameSSNCity Fred123-45-6789Seattle Joe987-65-4321Westfield SSNPhoneNumber 123-45-6789206-555-1234 123-45-6789206-555-6543 987-65-4321908-555-2121 987-65-4321908-555-1234 SSN  Name, City

49. Summary of BCNF Decomposition Find a dependency that violates the BCNF condition: A, A, … A 12n B, B, … B 12m A’s Others B’s R1R2 Heuristics: choose B, B, … B “as large as possible” 12m Decompose: Is there a 2-attribute relation that is not in BCNF ? Continue until there are no BCNF violations left.

50. Example Decomposition Person(name, SSN, age, hairColor, phoneNumber) SSN  name, age age  hairColor Decompose in BCNF (in class): Step 1: find all keys Step 2: now decompose

51. Other Example R(A,B,C,D) A B, B C Key: A, D Violations of BCNF: A B, A C, A BC Pick A BC: split into R1(A,BC) R2(A,D) What happens if we pick A B first ?

52. Correct Decompositions A decomposition is lossless if we can recover: R(A,B,C) R1(A,B) R2(A,C) R’(A,B,C) should be the same as R(A,B,C) R’ is in general larger than R. Must ensure R’ = R Decompose Recover

53. Correct Decompositions Given R(A,B,C) s.t. A  B, the decomposition into R1(A,B), R2(A,C) is lossless

54. 3NF: A Problem with BCNF Unit Company Product Unit Company Unit Product FD’s: Unit  Company; Company, Product  Unit So, there is a BCNF violation, and we decompose. Unit  Company No FDs

55. So What’s the Problem? Unit Company Product Unit CompanyUnit Product Galaga99 UW Galaga99 databases Bingo UW Bingo databases No problem so far. All local FD’s are satisfied. Let’s put all the data back into a single table again: Galaga99 UW databases Bingo UW databases Violates the dependency: company, product -> unit!

56. Solution: 3rd Normal Form (3NF) A simple condition for removing anomalies from relations: A relation R is in 3rd normal form if : Whenever there is a nontrivial dependency A 1, A 2,..., A n  B for R, then {A 1, A 2,..., A n } a super-key for R, or B is part of a key. A relation R is in 3rd normal form if : Whenever there is a nontrivial dependency A 1, A 2,..., A n  B for R, then {A 1, A 2,..., A n } a super-key for R, or B is part of a key.