1. Database Design Dr. M.E. Fayad, Professor
6/7/2019
Database Design
Dr. M.E. Fayad, Professor
Computer Engineering Department, Room #283I
College of Engineering
San José State University
One Washington Square
San José, CA
© M.E. Fayad
SJSU -- CmpE

2. Lesson 05: Normalization
6/7/2019
Lesson 05:
Normalization 2
© M.E. Fayad
SJSU – CmpE M.E. Fayad

3. 3 Lesson Objectives Understand the goals of normalization
6/7/2019
Lesson Objectives
Understand the goals of normalization
Explore the problems of DB bad design
Understand normalization
Learn how to deal with normalization 3
© M.E. Fayad
SJSU – CmpE M.E. Fayad

4. Relational Database Design
6/7/2019
Relational Database Design
Goals:
Reduce data redundancy (undesirable replication of data values)
Minimize anomaly problems (data model is structured in an improper manner)
Maintain (correct) information
Enforce semantic & integrity constraints, e.g., using dependency & domain constraints 4
© M.E. Fayad
SJSU – CmpE M.E. Fayad

5. Problems of Bad DB Design
6/7/2019
Problems of Bad DB Design
Data replication: extra storage, update anomalies
Anomaly: costly & data inconsistency
Loss of information: poor decomposition
Un-enforced dependency constraints: dependencies are lost 5
© M.E. Fayad
SJSU – CmpE M.E. Fayad

6. 6 Normalization A db scheme design tool
6/7/2019
Normalization
A db scheme design tool
A process of replacing associations among attributes in a relation scheme
An approximation of the relation schemes that should be created
Objectives: accomplish the goals of relational database design
Two approaches: decomposition & synthesis 6
© M.E. Fayad
SJSU – CmpE M.E. Fayad

7. 6/7/2019
Decomposition
A process to split or decompose a relation until the resultant relations no longer exhibit the undesirable problems, e.g., data redundancy, data inconsistency, anomaly, etc.
Decomposing a relation scheme R means breaking R into a pair of schemes, possibly intersecting
+ this process is repeated until all the decomposed relation schemes are in the desired (normal) form. 7
© M.E. Fayad
SJSU – CmpE M.E. Fayad

8. 8 Normal Forms (NFs) 1NF 2NF 3NF BCNF 4NF PJNF 6/7/2019
© M.E. Fayad
SJSU – CmpE M.E. Fayad

9. 6/7/2019
Normal Forms (NFs)
restrictions on the db scheme that preclude certain undesirable properties (data redundancy, update anomaly, loss of information, etc.) from the DB.
A relation scheme R is in PJNF if
R is in 4NF if
R is in BCNF if
R is in 3NF if
R is in 2NF if
R is in 1NF 9
© M.E. Fayad
SJSU – CmpE M.E. Fayad

10. 6/7/2019
Normal Forms (NFs)
Definition. A data value v is atomic if v is not a (i) set of values or (ii) composite value; otherwise, v is non-atomic.
First Normal Form (1NF). A relation scheme R is in 1NF if for every attribute A in R, the values in the domain of A, i.e., dom(A), are atomic. 10
© M.E. Fayad
SJSU – CmpE M.E. Fayad

11. 6/7/2019
Normal Forms (NFs)
Boyce-Codd Normal Form (BCNF). A relation scheme R is in BCNF if for every non-trivial FD X  Y applied to R, X is a superkey for R.
Definition. Let A be an attribute in a relation scheme R, and let F be a set of FDs over R. A is a prime attribute in R if A is contained in some candidate key of R; otherwise, A is a non-prime attribute in R. 11
© M.E. Fayad
SJSU – CmpE M.E. Fayad

12. 6/7/2019
Normal Forms (NFs)
Third Normal Form (3NF). A relation scheme R is in 3NF if
R is in 1NF, and
For every non-trivial FD X  Y applied to R, either
X is a superkey of R, or
every attribute in Y is an attribute of some candidate key of R, i.e., prime. 12
© M.E. Fayad
SJSU – CmpE M.E. Fayad

13. 6/7/2019
Normal Forms (NFs)
Lossy decomposition: a decomposition is lossy if the natural join of all the decomposed relations contain additional tuples and the original relation is lost.
Lossless decomposition: a decomposition is lossless if the natural join of all the decomposed relations always yields the original relation without any extra tuples, i.e.,
a decomposition {R1, R2, …, Rn} of R is lossless if r(R),
r(R) = R1(r)  R2(r)  …  Rn(r) 13
© M.E. Fayad
SJSU – CmpE M.E. Fayad

14. 2-Relational lossless-join decomposition
6/7/2019
2-Relational lossless-join decomposition
2-Relational lossless-join decomposition:
Let R1 and R2 be decomposed schemes of R. Let F be a set of FDs on R. The decomposition is lossless if
R1  R2  R1  F+ or R1  R2  R2  F+ 14
© M.E. Fayad
SJSU – CmpE M.E. Fayad

15. Dependency preserving
6/7/2019
Dependency preserving
a decomposition is dependency preserving if no dependency is lost in the process.
Let F be the set of FDs on R.
Let R1, … , Rn be a decomposition of R.
Let Fi, 1  i  n, be the set of FDs in F+ which applies to Ri.
Let F’ = i=1..n Fi.
If F’+ = F+, then the decomposition is dependency preserving. 15
© M.E. Fayad
SJSU – CmpE M.E. Fayad