Introduction to
Database Systems,
CS420
Schema Refinement Normalization
1
Agenda
Problem Design
 Functional Dependencies
 Reasoning about Functional Dependencies
 Refinement by Decomposition

2
Review: Relationship  Relation
name
husband
addr
Drinkers
1
Buddies
name
Likes
3
Beers
2
Favorite
wife
Married
manf
Likes(drinker, beer)
Favorite(drinker, beer)
Buddies(name1, name2)
Married(husband, wife)
Review: ER Design - Combining
Relations

OK to combine into one relation:
The relation for an entity-set E
2. The relations for many-one relationships of
which E is the “many.”
1.

4
Example: Drinkers(name, addr) and
Favorite(drinker, beer) combine to make
Drinker1(name, addr, favBeer).
Problem Design
Drinkers(name, addr, beersLiked, manf, favBeer)
name
Janeway
Janeway
Spock
addr
Voyager
Voyager
Enterprise
beersLiked
Bud
WickedAle
Bud
manf
A.B.
Pete’s
A.B.
Data is redundant
- Addr and favBeer duplicated for each name
- Manf duplicated for each beersLiked
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favBeer
WickedAle
WickedAle
Bud
Redundancy
Redundancy = saying the same thing in
two (or more) different ways.
 Wastes space and (more importantly)
encourages inconsistency.


6
Two representations of the same fact become
inconsistent if we change one and forget to
change the other.
Example: inconsistency
name
Janeway
Janeway
Spock
addr
Voyager
Voyager
Enterprise
beersLiked
Bud
WickedAle
Bud
manf
A.B.
Pete’s
A.B.
favBeer
WickedAle
WickedAle
Bud
• Update anomaly: if Janeway is transferred to Intrepid,
will we remember to change each of her tuples?
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Functional Dependencies
8
Motivation
Functional dependencies, can be used to
identify schemas with previous problems
and to suggest refinements
 Main refinement technique: decomposition
(replacing ABCD with, say, AB and BCD,
or ACD and ABD)

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Functional Dependencies

X Y is an assertion about a relation R that
whenever two tuples of R agree on all the
attributes of X, then they must also agree on all
attributes in set Y.
 Say
“X Y holds in R.”
 Convention: …, X, Y, Z represent sets of attributes;
A, B, C,… represent single attributes.
 Convention: no set formers in sets of attributes, just
ABC, rather than {A,B,C }.
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Splitting Right Sides of FD’s
XA1A2…An holds for R exactly when
each of XA1, XA2,…, XAn hold
for R.
 Example: ABC is equivalent to AB
and AC.
 There is no splitting rule for left sides.
 We’ll generally express FD’s with
singleton right sides.

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Example: FD’s
Drinkers(name, addr, beersLiked, manf,
favBeer)
 Reasonable FD’s to assert:
1.
name  addr favBeer
this FD is the same as name  addr and
name  favBeer.
 Note
2.
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beersLiked  manf
Example: Possible Data
name
Janeway
Janeway
Spock
addr
Voyager
Voyager
Enterprise
Because name  addr
beersLiked manf
Bud
A.B.
WickedAle Pete’s
Bud
A.B.
Because name  favBeer
Because beersLiked  manf
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favBeer
WickedAle
WickedAle
Bud
Keys of Relations


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K is a superkey for relation R if K
functionally determines all of R.
K is a key for R if K is a superkey, but
no proper subset of K is a superkey.
Example: Superkey
Drinkers(name, addr, beersLiked, manf,
favBeer)
 {name, beersLiked} is a superkey
because together these attributes
determine all the other attributes.
name  addr favBeer
 beersLiked  manf

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Example: Key

{name, beersLiked} is a key because
neither {name} nor {beersLiked} is a
superkey.


There are no other keys, but lots of
superkeys.

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name doesn’t  manf; beersLiked doesn’t 
addr.
Any superset of {name, beersLiked}.
More FD’s From “Physics”

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Example: “no two courses can meet in the
same room at the same time” tells us:
hour room  course.
Reasoning about Functional
Dependencies
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Inferring FD’s

We are given FD’s X1  A1, X2  A2,…,
Xn  An , and we want to know whether
an FD Y  B must hold in any relation
that satisfies the given FD’s.


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Example: If A  B and B  C hold, surely
A  C holds, even if we don’t say so.
Important for design of good relation
schemas.
Inference Test

To test if Y  B, start by assuming two
tuples agree in all attributes of Y.
Y
0000000. . . 0
00000?? . . . ?
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Inference Test – (2)

Use the given FD’s to infer that these
tuples must also agree in certain other
attributes.
If B is one of these attributes, then Y  B
is true.
 Otherwise, the two tuples, with any forced
equalities, form a two-tuple relation that
proves Y  B does not follow from the
given FD’s.

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Closure Test
An easier way to test is to compute the
closure of Y, denoted Y +.
 Basis: Y + = Y.
 Induction: Look for an FD’s left side X that
is a subset of the current Y +. If the FD is
X  A, add A to Y +.

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X
Y+
23
A
new Y+
Example: Finding {A,B}+


1.
2.
3.
4.
5.
6.
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R (A, B, C, D, E, F)
FD’s: ABC, BCAD, DE, and CFB
Splitting BCAD as BCA, BCD
Start with X = {A, B}
By ABC, we get X = {A, B, C}
By BCD, we get X = {A, B, C, D}
By DE, we get X= {A, B, C, D, E}
Done. Note: CFB can never be used.
Test If ABD

Suppose we have FD’s as in last example
Want to know if ABD follows from these
FD’s.
1.
Compute {A, B}+

{A, B}+ = {A, B, C, D, E}
2.
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Since D is in {A, B}+, so ABD follows.
Schema refinement Normalization
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Relational Schema Design

Goal of relational schema design is to
avoid anomalies and redundancy.

Update anomaly: one occurrence of a fact is
changed, but not all occurrences.
 Deletion anomaly: valid fact is lost when a
tuple is deleted.
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Example of Bad Design
Drinkers(name, addr, beersLiked, manf, favBeer)
name
Janeway
Janeway
Spock
addr
Voyager
???
Enterprise
beersLiked
Bud
WickedAle
Bud
manf
A.B.
Pete’s
???
favBeer
WickedAle
???
Bud
Data is redundant, because each of the ???’s can be figured
out by using the FD’s name  addr favBeer and
beersLiked  manf.
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This Bad Design Also Exhibits Anomalies
name
Janeway
Janeway
Spock
addr
Voyager
Voyager
Enterprise
beersLiked
Bud
WickedAle
Bud
manf
A.B.
Pete’s
A.B.
favBeer
WickedAle
WickedAle
Bud
• Update anomaly: if Janeway is transferred to Intrepid,
will we remember to change each of her tuples?
• Deletion anomaly: If nobody likes Bud, we lose track
of the fact that Anheuser-Busch manufactures Bud.
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Boyce-Codd Normal Form

We say a relation R is in BCNF if
whenever X Y is a nontrivial FD that
holds in R, X is a superkey.

Nontrivial means Y is not contained in X.
 Remember, a superkey is any superset of a
key.
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Example
Drinkers(name, addr, beersLiked, manf, favBeer)
FD’s: nameaddr favBeer, beersLiked->manf
Only key is {name, beersLiked}.
 In each FD, the left side is not a superkey.
 Any one of these FD’s shows Drinkers is
not in BCNF

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Another Example
Beers(name, manf, manfAddr)
FD’s: namemanf, manfmanfAddr
 Only key is {name} .
 namemanf does not violate BCNF, but
manfmanfAddr does.
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Decomposition into BCNF
Given: relation R with FD’s F.
 Look among the given FD’s for a BCNF
violation X Y.
 Compute X +.


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Not all attributes, or else X is a superkey.
Decompose R Using X  Y

Replace R by relations with schemas:
R1 = X +.
2. R2 = R – (X + – X ).
1.

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Project given FD’s F onto the two new
relations.
Decomposition Picture
R1
R-X +
X
R2
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X +- X
R
Example: BCNF Decomposition
Drinkers(name, addr, beersLiked, manf, favBeer)
F = nameaddr,
name  favBeer,
beersLikedmanf

Pick BCNF violation nameaddr.

Get closure of the left side: {name}+ = {name,
addr, favBeer}.

Decomposed relations:
1. Drinkers1(name, addr, favBeer)
2. Drinkers2(name, beersLiked, manf)
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Example -- Continued
We are not done; we need to check
Drinkers1 and Drinkers2 for BCNF.
 For Drinkers1(name, addr, favBeer),
relevant FD’s are nameaddr and
namefavBeer.


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Thus, {name} is the only key and Drinkers1
is in BCNF.
Example -- Continued

For Drinkers2(name, beersLiked, manf),
the only FD is beersLikedmanf, and
the only key is {name, beersLiked}.


Violation of BCNF.
beersLiked+ = {beersLiked, manf}, so we
decompose Drinkers2 into:
Drinkers3(beersLiked, manf)
2. Drinkers4(name, beersLiked)
1.
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Example -- Concluded

The resulting decomposition of Drinkers:
1.
2.
3.

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Drinkers1(name, addr, favBeer)
Drinkers3(beersLiked, manf)
Drinkers4(name, beersLiked)
Notice: Drinkers1 tells us about drinkers,
Drinkers3 tells us about beers, and Drinkers4
tells us the relationship between drinkers and
the beers they like.
Summary
Redundancies can be detected by FD’s
 Schema refinement can be guided by FD’s

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END
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