1. Revision for Final Prof. Sin-Min Lee Department of Computer Science

3. Set Operators Relation is a set of tuples, so set operations should apply: , ,  (set difference) Result of combining two relations with a set operator is a relation; hence all its elements must be tuples having the same structure union compatible relationsHence, scope of set operations limited to union compatible relations

4. Union Compatible Relations union compatibleTwo relations are union compatible if –Both have same number of columns –Names of attributes are the same in both –Attributes with the same name in both relations have the same domain unionintersectionset differenceUnion compatible relations can be combined using union, intersection, and set difference

5. Example Tables: Person Person (SSN, Name, Address, Hobby) Professor Professor (Id, Name, Office, Phone) are not union compatible. But PersonProfessor  Name (Person) and  Name (Professor) are union compatible so PersonProfessor  Name (Person) -  Name (Professor) makes sense.

6. Cartesian Product RSRS R S (but see naming problem next)If R and S are two relations, R  S is the set of all concatenated tuples, where x is a tuple in R and y is a tuple in S (but see naming problem next) RSR  S is expensive to compute: –Factor of two in the size of each row –Quadratic in the number of rows A B C D A B C D x1 x2 y1 y2 x1 x2 y1 y2 x3 x4 y3 y4 x1 x2 y3 y4 x3 x4 y1 y2 RS R S x3 x4 y3 y4 RS R  S

7. Renaming in Cartesian Product Result of expression evaluation is a relation. Attributes of relation must have distinct names. So what do we do if they don’t? E.g., suppose R(A,B) and S(A,C) RS and we wish to compute R  S. One solution is to rename the attributes of the answer: RS( R.A, R.B, S.A, S.C) R  S( R.A, R.B, S.A, S.C) Although only A needs to be renamed, it is“cleaner” to rename them all.

8. Renaming Operator Previous solution is used whenever possible but it won’t work when R is the same as S. Renaming operator resolves this. It allows to assign any desired names, say A 1, A 2,… A n, to the attributes of the n column relation produced by expression expr with the syntax expr [A 1, A 2, … A n ]

9. Example This is a relation with 4 attributes: StudId, CrsCode1, ProfId, CrsCode2 Transcript Transcript (StudId, CrsCode, Semester, Grade) Teaching Teaching (ProfId, CrsCode, Semester) Transcript  StudId, CrsCode (Transcript)[StudId, CrsCode1] Teaching   ProfId, CrsCode (Teaching) [ProfId, CrsCode2]

10. Derived Operation: Join generalthetajoin A (general or theta) join of R and S is the expression R join-condition S where join-condition is a conjunction of terms: A i oper B i in which A i is an attribute of R; B i is an attribute of S; and oper is one of =,,  , . The meaning is:  join-condition ´ (R  S) where join-condition and join-condition ´ are the same, except for possible renamings of attributes caused by the Cartesian product.

11. Theta Join – Example Employee(Name,Id,MngrId,Salary Employee(Name,Id,MngrId,Salary) Manager(Name,Id,Salary Manager(Name,Id,Salary) Output the names of all employees that earn more than their managers. Employee EmployeeManager  Employee.Name ( Employee MngrId=Id AND Salary>Salary Manager ) The join yields a table with attributes: EmployeeEmployee Employee.Name, Employee.Id, Employee Employee.Salary, Employee.MngrId ManagerManagerManager Manager.Name, Manager.Id, Manager.Salary

12. Relational Algebra Relational algebra operations operate on relations and produce relations ( “ closure ” ) f: Relation -> Relationf: Relation x Relation -> Relation Six basic operations: –Projection   (R) –Selection   (R) –UnionR 1 [ R 2 –DifferenceR 1 – R 2 –ProductR 1 £ R 2 –(Rename)   (R)

13. Equijoin Join - Example Student  Name,CrsCode ( Student Transcript Id=StudId  Grade=‘A’ (Transcript)) Id Name Addr Status 111 John ….. ….. 222 Mary ….. ….. 333 Bill ….. ….. 444 Joe ….. ….. StudId CrsCode Sem Grade 111 CSE305 S00 B 222 CSE306 S99 A 333 CSE304 F99 A Mary CSE306 Bill CSE304 The equijoin is used very frequently since it combines related data in different relations. Student Transcript Equijoin Equijoin: Join condition is a conjunction of equalities.

14. Natural Join Special case of equijoin + a special projection –join condition equates all and only those attributes with the same name (condition doesn’t have to be explicitly stated) –duplicate columns eliminated (projected out) from the result Transcript Transcript (StudId, CrsCode, Sem, Grade) Teaching ( Teaching (ProfId, CrsCode, Sem) Transcript Teaching Teaching =  StudId, Transcript.CrsCode, Transcript.Sem, Grade, ProfId Transcript ( Transcript Sem Teaching CrsCode=CrsCode AND Sem=Sem Teaching ) [ StudId, CrsCode, Sem, Grade, ProfId ]

15. Natural Join (cont’d) More generally: R SRS S =  attr-list (  join-cond (R × S) ) where RS attr-list = attributes (R)  attributes (S) (duplicates are eliminated) and join-cond has the form: A 1 = A 1 AND … AND A n = A n where RS {A 1 … A n } = attributes(R)  attributes(S)

16. Natural Join Example List all Ids of students who took at least two different courses:  StudId (  CrsCode  CrsCode2 ( Transcript Transcript Transcript [ StudId, CrsCode2, Sem2, Grade2 ] )) We don’t want to join on CrsCode, Sem, and Grade attributes, hence renaming!

17. Example Data Instance sidname 1Jill 2Qun 3Nitin 4Marty fidname 1Ives 2Saul 8Roth sidexp-gradecid 1A550-0103 1A700-1003 3A 3C500-0103 4C cidsubjsem 550-0103DBF03 700-1003AIS03 501-0103ArchF03 fidcid 1550-0103 2700-1003 8501-0103 STUDENT Takes COURSE PROFESSOR Teaches

18. Natural Join and Intersection Natural join: special case of join where  is implicit – attributes with same name must be equal: STUDENT ⋈ Takes ´ STUDENT ⋈ STUDENT.sid = Takes.sid Takes Intersection: as with set operations, derivable from difference A-B B-A A B A  B

19. Division A somewhat messy operation that can be expressed in terms of the operations we have already defined Used to express queries such as “ The fid's of faculty who have taught all subjects ” Paraphrased: “ The fid ’ s of professors for which there does not exist a subject that they haven ’ t taught ”

20. Division Using Our Existing Operators All possible teaching assignments: Allpairs: NotTaught, all (fid,subj) pairs for which professor fid has not taught subj: Answer is all faculty not in NotTaught:  fid,subj (PROFESSOR £  subj (COURSE)) Allpairs -  fid,subj (Teaches ⋈ COURSE)  fid (PROFESSOR) -  fid (NotTaught) ´  fid (PROFESSOR) -  fid (  fid,subj (PROFESSOR £  subj (COURSE)) -  fid,subj (Teaches ⋈ COURSE))

21. Division: R 1  R 2 Requirement: schema(R 1 ) ¾ schema(R 2 ) Result schema: schema(R 1 ) – schema(R 2 ) “ Professors who have taught all courses ” : What about “ Courses that have been taught by all faculty ” ?  fid (  fid,subj ( Teaches ⋈ COURSE)   subj (COURSE))

22. Division Goal: Produce the tuples in one relation, r, that match all tuples in another relation, s –r –r (A 1, …A n, B 1, …B m ) –s –s (B 1 …B m ) –rs s r –r/s, with attributes A 1, …A n, is the set of all tuples such that for every tuple in s, is in r Can be expressed in terms of projection, set difference, and cross-product

23. Division (cont’d)

24. Division - Example List the Ids of students who have passed all courses that were taught in spring 2000 Numerator: –StudId and CrsCode for every course passed by every student: Transcript  StudId, CrsCode (  Grade  ‘F’ (Transcript) ) Denominator: – CrsCode of all courses taught in spring 2000 Teaching  CrsCode (  Semester=‘S2000’ (Teaching) ) Result is numerator/denominator

25. Relational Calculus Important features: –Declarative formal query languages for relational model –Based on the branch mathematical logic known as predicate calculus –Two types of RC: 1) tuple relational calculus 2) domain relational calculus –A single statement can be used to perform a query

26. Tuple Relational Calculus based on specifying a number of tuple variables a tuple variable refers to any tuple

27. Generic Form {t | COND (t)} –where – t is a tuple variable and –COND(t) is Boolean expression involving t

28. Simple example 1 To find all employees whose salary is greater than $50,000 –{t| EMPLOYEE(t) and t.Salary>5000} where EMPLOYEE(t) specifies the range of tuple variable t –The above operation selects all the attributes

29. Simple example 2 To find only the names of employees whose salary is greater than $50,000 –{t.FNAME, t.NAME| EMPLOYEE(t) and t.Salary>5000} The above is equivalent to SELECT T.FNAME, T.LNAME FROM EMPLOYEE T WHERE T.SALARY > 5000

30. Elements of a tuple calculus In general, we need to specify the following in a tuple calculus expression: –Range Relation (I.e, R(t)) = FROM –Selected combination= WHERE –Requested attributes= SELECT

31. More Example:Q0 Retrieve the birthrate and address of the employee(s) whose name is ‘John B. Smith’ {t.BDATE, t.ADDRESS| EMPLOYEE(t) AND t.FNAME=‘John’ AND t.MINIT=‘B” AND t.LNAME=‘Smith}

32. Formal Specification of tuple Relational Calculus A general format: {t 1.A 1, t 2.A 2,…,t n.A n |COND ( t 1,t 2,…, t n, t n+1, t n+2,…,t n+m )} –where –t 1,…,t n+m are tuple var –A i : attribute  R(t i ) –COND (formula) Where COND corresponds to statement about the world, which can be True or False

33. Elements of formula A formula is made of Predicate Calculus atoms: – an atom of the from R(ti) –t i.A op t j.B op  {=,,..} –F1 And F2 where F1 and F2 are formulas –F1 OR F2 –Not (F1) –F’=(  t) (F) or F’= (  t) (F)  Y friends (Y, John)  X likes(X, ICE_CREAM)

34. Example Queries Using the Existential Quantifier Retrieve the name and address of all employees who work for the ‘ Research ’ department {t.FNAME, t.LNAME, t.ADDRESS| EMPLOYEE(t) AND (  d) (DEPARTMENT (d) AND d.DNAME=‘Research’ AND d.DNUMBER=t.DNO)}

35. More Example For every project located in ‘Stafford’, retrieve the project number, the controlling department number, and the last name, birthrate, and address of the manger of that department.

36. Cont. {p.PNUMBER,p.DNUM,m.LNAME,m.BD ATE, m.ADDRESS|PROJECT(p) and EMPLOYEE(M) and P.PLOCATION=‘Stafford’ and (  d) (DEPARTMENT(D) AND P.DNUM=d.DNUMBER and d.MGRSSN=m.SSN))}

37. Logical Equivalences There are two logical equivalences that will be heavily used: –p  q   p  q (Whenever p is true, q must also be true.) –  x. p(x)   x.  p(x) (p is true for all x) The second can be a lot easier to check!

38. Normalization  Review on Keys superkey: a set of attributes which will uniquely identify each tuple in a relation candidate key: a minimal superkey primary key: a chosen candidate key secondary key: all the rest of candiate keys prime attribute: an attribute that is a part of a candidate key (key column) nonprime attribute: a nonkey column

39. Normalization  Functional Dependency Type by Keys ‘ whole (candidate) key  nonprime attribute ’ : full FD (no violation) ‘ partial key  nonprime attribute ’ : partial FD (violation of 2NF) ‘ nonprime attribute  nonprime attribute ’ : transitive FD (violation of 3NF) ‘ not a whole key  prime attribute ’ : violation of BCNF

40. Functional Dependencies Let R be a relation schema   R and   R The functional dependency    holds on R iff for any legal relations r(R), whenever two tuples t 1 and t 2 of r have same values for , they have same values for . t 1 [  ] = t 2 [  ]  t 1 [  ] = t 2 [  ] On this instance, A  B does NOT hold, but B  A does hold. 14 1 5 37 A B

41. 1. Closure Given a set of functional dependencies, F, its closure, F +, is all FDs that are implied by FDs in F. e.g. If A  B, and B  C, then clearly A  C

42. Armstrong’s Axioms We can find F+ by applying Armstrong’s Axioms: –if   , then    (reflexivity) –if   , then      (augmentation) –if   , and   , then    (transitivity) These rules are –sound (generate only functional dependencies that actually hold) and –complete (generate all functional dependencies that hold).

43. Additional rules If    and   , then     (union) If    , then    and    (decomposition) If    and    , then     (pseudotransitivity) The above rules can be inferred from Armstrong’s axioms.

44. Example R = (A, B, C, G, H, I) F = { A  B A  C CG  H CG  I B  H} Some members of F + –A  H by transitivity from A  B and B  H –AG  I by augmenting A  C with G, to get AG  CG and then transitivity with CG  I –CG  HI by augmenting CG  I to infer CG  CGI, and augmenting of CG  H to infer CGI  HI, and then transitivity

45. 2. Closure of an attribute set Given a set of attributes A and a set of FDs F, closure of A under F is the set of all attributes implied by A In other words, the largest B such that: A  B Redefining super keys: The closure of a super key is the entire relation schema Redefining candidate keys: 1. It is a super key 2. No subset of it is a super key

46. Computing the closure for A Simple algorithm 1. Start with B = A. 2. Go over all functional dependencies,   , in F + 3. If   B, then Add  to B 4. Repeat till B changes

47. Example R = (A, B, C, G, H, I) F = { A  B A  C CG  H CG  I B  H} (AG) + ? 1. result = AG 2.result = ABCG(A  C and A  B) 3.result = ABCGH(CG  H and CG  AGBC) 4.result = ABCGHI(CG  I and CG  AGBCH Is (AG) a candidate key ? 1. It is a super key. 2. (A+) = BC, (G+) = G. YES.

48. Uses of attribute set closures Determining superkeys and candidate keys Determining if A  B is a valid FD Check if A+ contains B Can be used to compute F+

49. Database Normalization Functional dependency (FD) means that if there is only one possible value of Y for every value of X, then Y is Functionally dependent on X. Is the following FDs hold? XYZ 10B1C1 10B2C2 11B4C1 12B3C4 13B1C1 14B3C4

50. Functional Dependency is “good”. With functional dependency the primary key (Attribute A) determines the value of all the other non-key attributes (Attributes B,C,D,etc.) Transitive dependency is “bad”. Transitive dependency exists if the primary/candidate key (Attribute A) determines non-key Attribute B, and Attribute B determines non-key Attribute C. If a relation schema has more than one key, each is called a candidate key An attribute in a relation schema R is called prim if it is a member of some candidate key of R Database Normalization

51. First Normal Form (1NF) Each attribute must be atomic (single value) No repeating columns within a row (composite attributes) No multi-valued columns. 1NF simplifies attributes Queries become easier.

52. 1NF DeptnoDnameLocation 10ITLeeds, Bradford, Kent 20ResearchHundredfold 30MarketingLeeds DeptnoDname 10IT 20Research 30Marketing DeptnoLocation 10Leeds 10Bradfprd 10Kent 20Hundredfold 30Leeds

53. Second Normal Form (2NF) Each attribute must be functionally dependent on the primary key. If the primary key is a single attribute, then the relation is in 2NF The test for 2NF involves testing for FDs whose left-hand-side attribute are part of the primary key Disallow partial dependency, where non-keys attributes depend on part of a composite primary key In short, remove partial dependencies 2NF improves data integrity. Prevents update, insert, and delete anomalies.

54. 2NF PNoPNamePLocEmpNoENameSalaryAddressHoursNo Given the following FDs: Assuming all attributes are atomic, is the above relation in the 1NF, 2NF ? Relation X1Relation X3 Relation X2 PNoPNamePLoc EmpNoENameSalaryAddress PNoEmpNoHoursNo

55. Third Normal Form (3NF) Remove transitive dependencies. Transitive dependency n A non-prime attribute is dependent on another, non-prime attribute or attributes n Attribute is the result of a calculation Examples:  Area code attribute based on City attribute of a customer  Total price attribute of order entry based on quantity attribute and unit price attribute (calculated value) Solution: Any transitive dependencies are moved into a smaller table.

56. Transitive Dependence Give a relation R, Assume the following FD hold: Note : Both Ename and Address attributes are non-key attributes in R, and since Address depends on a non-Prime attribute Name, which depends on the primary key(EmpNo), a transitive dependency exists EmpNoENameSalaryAddress EmpNoENameSalary EnameAddress R1 R2 Note : If address is a prime attribute Then R is in 3NF

57. Boyce-Codd Normal Form (BCNF) –A relation is in Boyce-Codd normal form (BCNF) if every determinant in the table is a candidate key. (A determinant is any attribute whose value determines other values with a row.) –If a table contains only one candidate key, the 3NF and the BCNF are equivalent. –BCNF is a special case of 3NF. Database Normalization

58. A Table That Is In 3NF But Not In BCNF Figure 5.7

59. The Decomposition of a Table Structure to Meet BCNF Requirements Figure 5.8

60. Sample Data for a BCNF Conversion Table 5.2

61. Decomposition into BCNF

64. Perform lossless-join decompositions of each of the following scheme into BCNF schemes: R(A, B, C, D, E) with dependency set {AB  CDE, C  D, D  E} A B C D C D D E A B C E A B C D C D D E A B C

65. Given the FDs {B  D, AB  C, D  B} and the relation {A, B, C, D}, give a two distinct lossless join decomposition to BNCF indicating the keys of each of the resulting relations. A B C D B D A B C A B C D B D A C D

66. Definition of MVD A multivalued dependency (MVD) X ->->Y is an assertion that if two tuples of a relation agree on all the attributes of X, then their components in the set of attributes Y may be swapped, and the result will be two tuples that are also in the relation.

67. Example The name-addr-phones-beersLiked example illustrated the MVD name->->phones and the MVD name ->-> beersLiked.

68. Picture of MVD X ->->Y XY others equal exchange

69. MVD Rules Every FD is an MVD. –If X ->Y, then swapping Y ’s between two tuples that agree on X doesn’t change the tuples. –Therefore, the “new” tuples are surely in the relation, and we know X ->->Y. Complementation : If X ->->Y, and Z is all the other attributes, then X ->->Z.

73. Fourth Normal Form The redundancy that comes from MVD’s is not removable by putting the database schema in BCNF. There is a stronger normal form, called 4NF, that (intuitively) treats MVD’s as FD’s when it comes to decomposition, but not when determining keys of the relation.

74. 4NF Definition A relation R is in 4NF if whenever X ->->Y is a nontrivial MVD, then X is a superkey. –“Nontrivial means that: 1.Y is not a subset of X, and 2.X and Y are not, together, all the attributes. –Note that the definition of “superkey” still depends on FD’s only.

75. BCNF Versus 4NF Remember that every FD X ->Y is also an MVD, X ->->Y. Thus, if R is in 4NF, it is certainly in BCNF. –Because any BCNF violation is a 4NF violation. But R could be in BCNF and not 4NF, because MVD’s are “invisible” to BCNF.

76. Normalization  Good Decomposition dependency preserving decomposition - it is undesirable to lose functional dependencies during decomposition lossless join decomposition - join of decomposed relations should be able to create the original relation (no spurious tuples)

78. Decomposition and 4NF If X ->->Y is a 4NF violation for relation R, we can decompose R using the same technique as for BCNF. 1.XY is one of the decomposed relations. 2.All but Y – X is the other.

81. Example Drinkers(name, addr, phones, beersLiked) FD: name -> addr MVD’s: name ->-> phones name ->-> beersLiked Key is {name, phones, beersLiked}. All dependencies violate 4NF.

82. Example, Continued Decompose using name -> addr: 1.Drinkers1(name, addr) uIn 4NF, only dependency is name -> addr. 2.Drinkers2(name, phones, beersLiked) uNot in 4NF. MVD’s name ->-> phones and name ->-> beersLiked apply. No FD’s, so all three attributes form the key.

83. Example: Decompose Drinkers2 Either MVD name ->-> phones or name ->-> beersLiked tells us to decompose to: –Drinkers3(name, phones) –Drinkers4(name, beersLiked)

84. BCNF Given a relation schema R, and a set of functional dependencies F, if every FD, A  B, is either: 1. Trivial 2. A is a superkey of R Then, R is in BCNF (Boyce-Codd Normal Form) Why is BCNF good ?

85. BCNF What if the schema is not in BCNF ? Decompose (split) the schema into two pieces. Careful: you want the decomposition to be lossless

86. Achieving BCNF Schemas For all dependencies A  B in F+, check if A is a superkey By using attribute closure If not, then Choose a dependency in F+ that breaks the BCNF rules, say A  B Create R1 = A B Create R2 = A (R – B – A) Note that: R1 ∩ R2 = A and A  AB (= R1), so this is lossless decomposition Repeat for R1, and R2 By defining F1+ to be all dependencies in F that contain only attributes in R1 Similarly F2+

87. Example 1 B  C R = (A, B, C) F = {A  B, B  C} Candidate keys = {A} BCNF = No. B  C violates. R1 = (B, C) F1 = {B  C} Candidate keys = {B} BCNF = true R2 = (A, B) F2 = {A  B} Candidate keys = {A} BCNF = true

88. Example 2-1 A  B R = (A, B, C, D, E) F = {A  B, BC  D} Candidate keys = {ACE} BCNF = Violated by {A  B, BC  D} etc… R1 = (A, B) F1 = {A  B} Candidate keys = {A} BCNF = true R2 = (A, C, D, E) F2 = {AC  D} Candidate keys = {ACE} BCNF = false (AC  D) From A  B and BC  D by pseudo-transitivity AC  D R3 = (A, C, D) F3 = {AC  D} Candidate keys = {AC} BCNF = true R4 = (A, C, E) F4 = {} [[ only trivial ]] Candidate keys = {ACE} BCNF = true Dependency preservation ??? We can check: A  B (R1), AC  D (R3), but we lost BC  D So this is not a dependency -preserving decomposition

89. Example 2-2 BC  D R = (A, B, C, D, E) F = {A  B, BC  D} Candidate keys = {ACE} BCNF = Violated by {A  B, BC  D} etc… R1 = (B, C, D) F1 = {BC  D} Candidate keys = {BC} BCNF = true R2 = (B, C, A, E) F2 = {A  B} Candidate keys = {ACE} BCNF = false (A  B) A  B R3 = (A, B) F3 = {A  B} Candidate keys = {A} BCNF = true R4 = (A, C, E) F4 = {} [[ only trivial ]] Candidate keys = {ACE} BCNF = true Dependency preservation ??? We can check: BC  D (R1), A  B (R3), Dependency-preserving decomposition

90. Example 3 A  BC R = (A, B, C, D, E, H) F = {A  BC, E  HA} Candidate keys = {DE} BCNF = Violated by {A  BC} etc… R1 = (A, B, C) F1 = {A  BC} Candidate keys = {A} BCNF = true R2 = (A, D, E, H) F2 = {E  HA} Candidate keys = {DE} BCNF = false (E  HA) E  HA R3 = (E, H, A) F3 = {E  HA} Candidate keys = {E} BCNF = true R4 = (ED) F4 = {} [[ only trivial ]] Candidate keys = {DE} BCNF = true Dependency preservation ??? We can check: A  BC (R1), E  HA (R3), Dependency-preserving decomposition