1. CSC 453 Database Systems Lecture
Tanu Malik
College of CDM
DePaul University

2. Today
Review-Normalization
Normalization-BCNF
Recursion
PL/SQL

3. Prime/Non-prime attributes
A  BC, B  C, D  B
AB  C, C  D, CE  A

4. Consider the following relations R(Sname, Semail, Course, Instructor)
Students(S , Sname)
Courses(Course, Instructor)
Taking(student , Course)
Gwen
CSC453
Malik
CSC443
Riely
Priya

5. R(Sname, Semail, Course, Instructor)
Gwen
CSC453
Malik
CSC443
Riely
Priya
Maya
Pitcher
Students(S , Sname)
Courses(Course, Instructor)
Taking(S , Course)

6. Motivation for Normal Forms
Identify a “good” schema
For some definition of good
Avoid anomalies, redundancy
Many normal forms
1st
2nd
3rd
BCNF

7. First Normal Form (1NF) No multi‐valued attributes allowed
Counter-example
Course(name, instructor, [student, ]*)
Redundancy in non‐list attributes

8. Removing Multi-Valued Attributes
Remove the multi-valued attribute from the relation
Create a new relation with the primary key of the original relation and the multi-valued attribute
For each tuple in original relation with k values, add k tuples to the new relation
Primary key of new relation contains all attributes; primary key of original relation becomes foreign key in new relation referencing original relation

9. Second Normal Form (2NF)
Second Normal Form: 1NF, plus every non-prime attribute in the relation is determined by the entire primary key (but not by any subset)
To get 2NF, eliminate partial dependencies on the primary key
XY is a partial dependency if ZY for some subset Z of X

10. Converting 1NF to 2NF
(EmpID, EmpLName, EmpFName, Dept, ProjCode, Hours)
Functional Dependencies
EmpID  EmpLName, EmpFName, Dept
EmpID, ProjCode  Hours

11. 1NF vs 2NF Data EmpID EmpLName EmpFName Dept ProjCode Hours 550 Smith
Winston
Accounting
101 20
252 10
601
Barney
Finance 5
390
Hammond
Evey
Personnel
995 25
001
Preston
Bill
Special Events
100
Logan
Ted
007
Bond
James
505
Lane
Lois
Media Relations

12. Converting 1NF to 2NF
Decompose (EmpID, EmpLName, EmpFName, Dept, ProjCode, Hours)
Remove the offending FD
EmpID EmpLName, EmpFName, Dept
Two tables
(EmpID, EmpLName, EmpFName, Dept)
(EmpID, ProjCode, Hours)

13. Towards 2NF EmpID EmpLName EmpFName Dept 550 Smith Winston Accounting
601
Barney
Finance
390
Hammond
Evey
Personnel
001
Preston
Bill
Special Events
100
Logan
Ted
007
Bond
James
505
Lane
Lois
Media Relations
EmpID
ProjCode
Hours
550
101 20
252 10
601 5
390
995 25
001
100
007
505

14. Towards 2NF EmpID  EmpLName, EmpFName, Dept EmpID, ProjCode  Hours
550
Smith
Winston
Accounting
601
Barney
Finance
390
Hammond
Evey
Personnel
001
Preston
Bill
Special Events
100
Logan
Ted
007
Bond
James
505
Lane
Lois
Media Relations
EmpID
ProjCode
Hours
550
101 20
252 10
601 5
390
995 25
001
100
007
505
EmpID 
EmpLName, EmpFName, Dept
EmpID, ProjCode 
Hours

15. Counterexample
Movies(title, year, star, studio, studioAddress, salary)
FD: title, year ‐> studio; studio ‐> studioAddress; star‐>salary

16. Removing Partial Dependencies
Find all dependencies where a subset of the primary key determines some non-prime attribute(s)
Starting with the smallest subset, do the following:
1. Remove all attributes on the right-hand side from the relation and put them in a new relation
2. Add the attributes in the determinant(l.h.s) to the new relation; make them the primary key, and make them a foreign key in the original relation referencing the new table
3. Remove from any remaining partial dependencies any attributes removed from the original relation

17. Third Normal Form (3NF)
Third Normal Form: 2NF, plus every non-prime attribute in the relation is determined only by the primary key of the relation.
To get 3NF, eliminate transitive dependencies on the primary key
XY is a transitive dependency if XZ and ZY for some Z that is disjoint from X

18. Counterexample
Movies(title, year, star, studio, studioAddress, salary)
FD: title, year ‐> studio; studio ‐> studioAddress; star‐>salary

19. Converting 2NF to 3NF
Schema: (First , Last, Address, City, State, Zip)
First, Last  Address, City, State, Zip
Transitive functional dependency:
Zip  City, State

20. 2NF Data Table First Last Address City State ZipCode Henry Bienen
2145 Sheridan Rd
Evanston IL
60202
Helmut
Epp
55 E. Jackson St
Chicago
60604
Denis
Stein
55 E. Jackson St.
David
Miller
243 S. Wabash Av.
Gary
5000 Forbes Av.
Pittsburgh PA
15213
Leighton
77 Beacon St
Cambridge MA
02139

21. Decompose the Tables City State ZipCode Evanston IL 60202 Chicago
First
Last
Address
ZipCode
Henry
Bienen
2145 Sheridan Rd
60202
Helmut
Epp
55 E. Jackson St
60604
Denis
Stein
55 E. Jackson St.
David
Miller
243 S. Wabash Av.
Gary
5000 Forbes Av.
15213
Leighton
77 Beacon St
02139
City
State
ZipCode
Evanston IL
60202
Chicago
60604
Pittsburgh PA
15213
Cambridge MA
02139

22. Decompose the Tables ZipCode  City, State
First
Last
Address
ZipCode
Henry
Bienen
2145 Sheridan Rd
60202
Helmut
Epp
55 E. Jackson St
60604
Denis
Stein
55 E. Jackson St.
David
Miller
243 S. Wabash Av.
Gary
5000 Forbes Av.
15213
Leighton
77 Beacon St
02139
City
State
ZipCode
Evanston IL
60202
Chicago
60604
Pittsburgh PA
15213
Cambridge MA
02139
ZipCode  City, State
First, Last  First, Last, Address, ZipCode

23. 3NF Decomposition
Input: A universal relation R and a set of functional dependencies F on R
Output: A decomposition D of R into 3NF schemas with dependency preservation and nonadditive join

24. 3NF-Normalization Algorithm (3NF Normalization):
Input: Relation R with FDs F c
Output: 3NF decomposition D of R
D = {}
For every XY in F add sub-relation Q =(XY) to D, unless
some sub-relation in D already contains all of XY: don’t add Q
some sub-relation(S) in D is contained in XY: replace S with Q(XY)
If no relation in D contains a key of R, then add
new relation Q(X) on some key X of R

25. Example R = (A,B,C,D) A→ C D BA→ C The candiate key is ?
3NF decomposition is ?
R1 = (C, A, D)
R2 = (B, A, C)

26. 3NF Decomposition R(A, B, C, D) R(A,B,C,D,E) R(A, B, C, D, E, F)
FDs: A B, C D
R(A,B,C,D,E)
FDs: CE, BC
R(A, B, C, D, E, F)
F = { AB  CD, D  A, C  EF}
(AB)
(AC)
(AB) (DB)

27. 3NF Decomposition R={A, B, C, D, E, F} F = { AB  CD, C  EF, D  A }
Non-prime determines a prime attribute 

28. 3NF-Checking Order Is F in minimal cover? What is the candidate key?
Which FDs violate 3NF?
Decompose to 3NF.
Is the decomposition lossless?
Is it dependency preserving?

29. The End Result A collection of relations, each in 3NF
Each relation has a primary key
(We are assuming that there is only one candidate key…)
Every non-prime attribute in a relation is determined by its entire primary key
No non-prime attribute in a relation is determined by any attributes other than its entire primary key
Information can reconstructed using joins, and stored in views if desired

30. Boyce-Codd Normal Form
Boyce-Codd Normal Form (BCNF): For every non-trivial functional dependency XA, it must be the case that X is a superkey
“Every determinant must contain a candidate key”
X must be a superkey even if A is a prime attribute

31. BCNF example
Pizza |Topping Type |Topping | | | cheese | mozzarella 1 | meat | pepperoni 1 | vegetable| olives 2 | meat | sausage 2 | cheese | mozzarella 2 | vegetable| peppers
Each pizza must have exactly
one of each topping type
One type of cheese
One type of meat
One type of vegetable

32. BCNF example
Pizza |Topping Type |Topping | | | cheese | mozzarella 1 | meat | pepperoni 1 | vegetable| olives 2 | meat | mozzarella 2 | cheese | sausage 2 | vegetable| peppers
Pizza can have
exactly 3 types
of topping
One type of cheese
One type of meat
One type of vegetable

33. Decompose
Pizza |Topping | | | 1 |mozzarella| 1 |pepperoni | 1 |olives | 2 |mozzarella | 2 |sausage | 2 |peppers |
Topping |Topping Type |
mozzarella|cheese
pepperoni |meat
olives |vegetable
mozzarella | cheese
sausage |meat
peppers |vegetable

34. BCNF Decomposition
Input: A universal relation R and a set of functional dependencies F on R
Output: A decomposition D of R into BCNF schemas with nonadditive join
Algorithm on next page
Algorithm does not guarantee dependency preservation

35. BCNF Decomposition Algorithm ALGORITHM BCNF (R: Relation, F: FD set)
BEGIN
1. D  {R}
3. While some X → Y holds in some Ri(A1,…,An) in D and
(X → Y) is not trivial, X is not a superkey of Ri
Ri1  X+ ∩({A1,…,An})
Ri2  X  ({A1,…,An} - X+ )
Result  Result – {Ri}  {Ri1,Ri2}
4. Return result
END

36. BCNF Example: R = (A, B, C) F = {A → B, B → C} Is R in BCNF?
A: Consider the nontrivial dependencies in F:
1. A → B, A → R (A is a key)
2. B → C, B → A (B is not a key)
Therefore, R not in BCNF

37. BCNF Example: Q: Is the decomposition lossless?
R = R1  R2 R1 = (A, B); R2 = (B, C)
F = {A → B, B → C}
Are R1, R2 in BCNF?
A: 1. Test R1:
A → B covered, A → R1 (all other FD’s covered trivial)
2. Test R2:
B → C covered, B → R2 (all other FD’s covered trivial)
 R1, R2 in BCNF
Q: Is the decomposition lossless?

38. BCNF Decompose R into BCNF: R = (A, B, C, D, E, H)
F = {A → BC, E → HA}
Decompose R into BCNF:

39. BCNF Decomposition Decomposition #1: R = R1  R3  R4 Q: Is this DP?
R = (A, B, C, D, E, H)
F = {A → BC, E → HA}
(Note: Fc = F)
Decomposition #1: R = R1  R3  R4
R = (A, B, C, D, E, H)
Decompose on A → BC
R1 = (A, B, C)
R2 = (A, D, E, H)
Decompose on E → HA
R3 = (A, E, H)
R4 = (D, E)
Q: Is this DP?
A: Yes. All Fc covered by R1, R3, R4. Therefore F+ covered

40. BCNF Decomposition (cont.)
R = (A, B, C, D, E, H)
F = {A → B, E → HA, A → C}
(Note: Fc = F)
Decomposition #2: R = R1  R3  R5  R6
R = (A, B, C, D, E, H)
Decompose on A → B
R1 = (A, B)
R2 = (A, C, D, E, H)
Decompose on E → HA
R3 = (A, E, H)
R4 = (C, D, E)
Decompose on E → C
R5 = (C, E)
R6 = (E, D)
Q: Not DP. Why?
A: A → C not covered by R1, R3, R5 , R6.

41. BCNF Decomposition R(A, B, C, D, E, F) F = { AB  CD, C  EF, D  A }
(AC)
(AB) (DB) (CB)

42. BCNF Decomposition R (S, P, Q, X, Y, N, C)
F = { S  NC, P  XY, SP  Q , QP }
Decompose to BCNF
Is it dependency preserving?

43. Properties of Decompositions
When we work with BCNF, we must look at properties involving multiple relations:
Nonadditive (Lossless) Join: No tuples that are not in the original relation (spurious tuples) are generated when decomposed relations are joined
Dependency Preservation: Every functional dependency in the original relation is represented somewhere in the decomposition

44. BCNF vs. 3NF Every relation in BCNF is in 3NF
Not every relation in 3NF is in BCNF
3NF relations that are not in BCNF fail because some prime attribute is determined by something that is not a superkey – this is allowed by 3NF but not by BCNF
Decomposing tables into BCNF can be tricky – functional dependencies can be lost!

45. Remarks on Algorithms
Different runs may yield different results, depending on the order in which attributes and functional dependencies are considered
We must know all functional dependencies
We can’t always guarantee dependency preservation for BCNF, but we can generate a 3NF decomposition and then consider the individual relations in the result

46. PL/SQL
A general-purpose procedural programming that includes SQL commands
PL/SQL can
create and issue SQL statements
store and process the results of queries
define procedures to respond to database events

47. Basic Structure of Code
Simplest form is an anonymous block:
declare -- variable and subprogram declarations
-- every statement must end with a ; begin -- PL/SQL statements to execute
--every statement must end with a ;
--statements can be nested with another B/E exception -- exception handling (optional) end;

48. Output To display output: RAISE NOTICE ‘string %’, arguments;
Output buffer displayed in DBMS Output tab
Use View Dbms Output and ‘+’ to open tab
Single line comments with –
Multi-line with /* */

49. Data Types Numeric Character Boolean Datetime
Data types are not case sensitive
DECLARE
num1 INTEGER;
num2 REAL;
num3 DOUBLE PRECISION;
BEGIN null;
END; /

50. Declaring Variables All variables must be declared:
varName [CONSTANT] dataType [NOT NULL] [:= initialValue];
Assignments use :=, and PL/SQL has typical arithmetic operations

51. Scoping DECLARE BEGIN / -- Global variables num1 number := 95;
dbms_output.put_line('Outer Variable num1: ' || num1);
dbms_output.put_line('Outer Variable num2: ' || num2);
-- Local variables
num1 number := 195;
num2 number := 185;
dbms_output.put_line('Inner Variable num1: ' || num1);
dbms_output.put_line('Inner Variable num2: ' || num2);
END; /

52. Declaring Variables
Only one variable can be declared per line, but variable types can be given in terms of the domain of another variable or attribute:
varName otherVar%type;
varName TABLE.Attribute%type;

53. Operators Arithmetic operators Relational operators
Comparison operators
LIKE, BETWEEN, IN, IS NULL
Logical operators
String operators

54. Branching if-then: if condition then
…’true’ statements… end if;
if-else: if condition then …’true’ statements… else …’false’ statements… end if;

55. Branching
if-elsif: if condition1 then … ‘true’ statements… elsif condition2 then … ‘false-true’ statements… elsif condition3 then … ‘false-false-true’ statements… (… as many times as needed…) else … ‘all false’ statements… end if;

56. Case Statement CASE [ expression ] WHEN condition_1 THEN result_1
...
WHEN condition_n THEN result_n
ELSE result
END

57. Case Statement expression condition_1, condition_2, ... condition_n
Optional. It is the value that you are comparing to the list of conditions. (ie: condition_1, condition_2, ... condition_n)
condition_1, condition_2, ... condition_n
The conditions that must all be the same datatype. The conditions are evaluated in the order listed. Once a condition is found to be true, the CASE statement will return the result and not evaluate the conditions any further.
result_1, result_2, ... result_n
Results that must all be the same datatype. This is the value returned once a condition is found to be true.

58. Loops General loop: loop …loop body… end loop;
Repeats until exit; is executed in loop body
While loop: while condition loop …loop body… end loop;
Repeats until condition is false

59. Loops
For loop: for variable in [reverse] lower..upper loop …loop body… end loop;
Can only increment/decrement by one
lower always appears before upper in header

60. Incorporating SQL Queries
Result of a query can be stored in a set of variables by adding INTO clause to query: SELECT list of attributes INTO list of variables FROM list of tables …
Variable types must match attribute types

61. Procedures (In Oracle)
CREATE [OR REPLACE] PROCEDURE name (paramName IN [OUT] paramType …) AS …declarations… BEGIN …body of procedure… END; /
‘IN’ parameters are passed by value, for input only, read-only parameters
‘OUT’ parameters are passed by reference
‘IN OUT’ parameters are passed by reference, to return results to the calling sub-program

62. Functions
CREATE [OR REPLACE] FUNCTION mode {IN|OUT|INOUT} name …) RETURNS returnType AS
$$ …declarations… BEGIN …body of function… return returnValue; END; $$ language plpgsql;
‘IN’ parameters are default
Specify return type and return value instead

63. Executing Procedures and Functions
A standalone procedure
Using the EXECUTE keyword
Calling the name of the procedure from a PL/SQL block
A standalone function
Calling the name of the function from a PL/SQL block
Calling the name of the function in a SQL query

64. Cursors
A cursor represents a pointer into a set of records returned by a query declare name cursor for query;
cursor name can be used to iterate through the records returned by query

65. Cursor Commands/Expressions
open name; -- initializes to beginning of set
fetch name into variableList; -- reads the next record into the variables
close name; -- closes the cursor

66. Parameterized Cursors
Can supply a parameter in cursor declaration and query declare name (parameter in type) cursor for query;
Each time cursor is opened, value of parameter is specified in parentheses to complete the query

67. Implicit Cursors for DML statements
Whenever a DML statement (INSERT, UPDATE and DELETE) is issued, an implicit cursor is associated with this statement.
INSERT operations: the cursor holds the data that needs to be inserted.
UPDATE and DELETE operations: the cursor identifies the rows that would be affected.

68. Records Data structure to hold data items of different kinds
Table-based Records:
Can create a record with same structure as the row of a table (fields are table attributes): recordName TABLE%rowtype;
Can select a row of a table directly into a record, and access individual fields with recordName.Attribute

69. Records Cursor-based records: Assign rowtype from a query in cursor
User-defined Records:
Declare a new data type and a table of records: create type newType (
attr1 datatype,
attr2 datatype )

70. Exceptions
DECLARE <declarations section> BEGIN <executable command(s)> EXCEPTION <exception handling goes here > WHEN exception1 THEN exception1-handling-statements WHEN exception2 THEN exception2-handling-statements WHEN exception3 THEN exception3-handling-statements WHEN others THEN exception3-handling-statements END;

71. Case Statement SELECT LastName, FirstName,
(CASE Career WHEN 'UGRD' THEN 'Undergraduate'
WHEN 'GRD' THEN 'Graduate'
WHEN ' SAL' THEN 'Student At Large' END) AS Career
FROM student;
UPDATE employee
SET salary = (CASE WHEN salary < THEN 50000
WHEN salary < THEN salary * 1.05
ELSE salary * 1.1 END);