Normalisation

4.1 Functional Dependencies

A functional dependency $X \to Y$ holds on relation $R$ if for any two tuples $t_1, t_2$ in $R$ $t_1[X] = t_2[X]$ implies $t_1[Y] = t_2[Y]$.

Armstrong”s axioms:

1. Reflexivity: If $Y \subseteq X$Then $X \to Y$.
2. Augmentation: If $X \to Y$Then $XZ \to YZ$.
3. Transitivity: If $X \to Y$ and $Y \to Z$Then $X \to Z$.

Derived rules:

4. Union: If $X \to Y$ and $X \to Z$Then $X \to YZ$.
5. Decomposition: If $X \to YZ$Then $X \to Y$ and $X \to Z$.
6. Pseudotransitivity: If $X \to Y$ and $YW \to Z$Then $XW \to Z$.

Attribute closure. $X^+$ is the set of all attributes functionally determined by $X$. Computed By starting with $X$ and repeatedly applying Armstrong’s axioms until no new attributes can be added.

Theorem 4.1. $X \to Y$ holds if and only if $Y \subseteq X^+$.

4.2 Normal Forms

First Normal Form (1NF). All attribute values are atomic (indivisible). No repeating groups or Nested relations.

Second Normal Form (2NF). In 1NF, and no non-prime attribute is partially dependent on any Candidate key. Equivalently: every non-prime attribute is fully functionally dependent on every Candidate key.

A partial dependency is $A \to B$ where $A$ is a proper subset of a candidate key and $B$ is Non-prime.

Third Normal Form (3NF). In 2NF, and for every non-trivial FD $X \to A$ in $R$Either $X$ is a Superkey or $A$ is a prime attribute.

A prime attribute is an attribute that belongs to some candidate key.

Boyce-Codd Normal Form (BCNF). For every non-trivial FD $X \to A$ in $R$, $X$ is a superkey.

Theorem 4.2. Every relation in BCNF is in 3NF, but the converse does not hold.

Proof. BCNF requires $X$ to be a superkey for every non-trivial FD $X \to A$. 3NF allows $X$ to be A superkey or $A$ to be prime. Since the BCNF condition is stricter, every BCNF relation is in 3NF. For the converse, consider $R(A, B, C)$ with FDs $AB \to C$ and $C \to B$. Candidate keys: $\\{AB\\}$, $\\{AC\\}$. $C \to B$ does not violate 3NF ($B$ is prime) but violates BCNF ($C$ is not a Superkey). $\blacksquare$

4.3 Decomposition

A decomposition of $R$ into $R_1, R_2, \ldots, R_k$ must satisfy:

1. Lossless-join: $R = R_1 \bowtie R_2 \bowtie \cdots \bowtie R_k$. No information is lost.
2. Dependency preservation: Every FD in the original schema is implied by the FDs in the decomposed schemas.

Theorem 4.3 (Lossless-join test). A decomposition of $R$ into $R_1, R_2$ is lossless if and Only if $R_1 \cap R_2 \to R_1$ or $R_1 \cap R_2 \to R_2$.

Proof. Let $r$ be an instance of $R$ and let $r_1 = \pi_{R_1}(r)$, $r_2 = \pi_{R_2}(r)$. We must Show $r = r_1 \bowtie r_2$ under the given condition. Since $r_1$ and $r_2$ are projections of $r$ Every tuple in $r_1 \bowtie r_2$ agrees with some tuple of $r$ on every attribute. It suffices to show That no spurious tuple is produced. Suppose $(t_1, t_2) \in r_1 \bowtie r_2$ where $t_1 \in r_1$ and $t_2 \in r_2$. Since $t_1$ and $t_2$ agree on $R_1 \cap R_2$And by the condition $R_1 \cap R_2 \to R_1$ (WLOG), there exists $T' \in r$ with $T'[R_1 \cap R_2] = t_1[R_1 \cap R_2] = t_2[R_1 \cap R_2]$. Since $T'[R_1 \cap R_2] = t_1[R_1 \cap R_2]$ and $R_1 \cap R_2 \to R_1$, we have $t'[R_1] = t_1[R_1]$. Similarly $t'[R_2] = t_2[R_2]$. Thus $(t_1, t_2)$ corresponds to a real tuple $t' \in r$So no spurious tuple exists. $\blacksquare$

Dependency preservation. Let $F$ be the set of FDs for $R$. The decomposition $R_1, \ldots, R_k$ Preserves dependencies if the closure of $\bigcup_{i=1}^{k} F_i^+$ (where $F_i$ is the restriction Of $F$ to $R_i$) equals $F^+$. In practice, we check that every FD in a minimal cover of $F$ can be Tested within a single $R_i$.

Dependency preservation is important for efficient constraint checking: when updating $R_i$The DBMS can verify relevant FDs locally without joining all decomposed relations.

4.4 Normalisation Examples

Example 1. Enrolment(StudentID, CourseID, StudentName, Dept, Grade) with FDs: $\mathrm{StudentID} \to \mathrm{StudentName}$, $\mathrm{StudentID} \to \mathrm{Dept}$ $\\{\mathrm{StudentID}, \mathrm{CourseID}\\} \to \mathrm{Grade}$.

• Candidate key: $\\{\mathrm{StudentID}, \mathrm{CourseID}\\}$.
• 1NF: satisfied (atomic values).
• 2NF violation: $\mathrm{StudentID} \to \mathrm{StudentName}$ is a partial dependency (StudentID is a proper subset of the key).
• Decompose: Student(StudentID, StudentName, Dept) and Enrolment(StudentID, CourseID, Grade). Both are in 3NF and BCNF.

Example 2 (3NF but not BCNF). CourseOffering(Course, Instructor, Room, Time) with FDs: $\\{\mathrm{Course}, \mathrm{Time}\\} \to \\{\mathrm{Instructor}, \mathrm{Room}\\}$ $\mathrm{Instructor} \to \mathrm{Room}$.

• Candidate key: $\\{\mathrm{Course}, \mathrm{Time}\\}$.
• 3NF: $\mathrm{Instructor} \to \mathrm{Room}$ — Instructor is not a superkey, but Room is not prime. Wait — Room is not prime (not in any candidate key). So this actually violates 3NF too.

Let us correct: CourseOffering(Course, Instructor, Textbook) with FDs: $\mathrm{Course} \to \mathrm{Instructor}$ $\mathrm{Instructor} \to \mathrm{Textbook}$.

• Candidate key: $\\{\mathrm{Course}\\}$ (since Course determines everything transitively).
• 3NF: $\mathrm{Instructor} \to \mathrm{Textbook}$. Instructor is not a superkey. Textbook is not prime. Violates 3NF.

Better example. Class(Course, Instructor, Student) with FDs: $\\{\mathrm{Course}, \mathrm{Student}\\} \to \mathrm{Instructor}$ $\mathrm{Instructor} \to \mathrm{Course}$.

• Candidate keys: $\\{\mathrm{Course}, \mathrm{Student}\\}$, $\\{\mathrm{Instructor}, \mathrm{Student}\\}$.
• 3NF check for $\mathrm{Instructor} \to \mathrm{Course}$: Instructor is not a superkey. But Course is prime (in candidate key $\\{\mathrm{Course}, \mathrm{Student}\\}$). So 3NF is satisfied.
• BCNF check: $\mathrm{Instructor} \to \mathrm{Course}$ violates BCNF (Instructor is not a superkey).

Decompose: Teaches(Instructor, Course) and Attends(Instructor, Student). This is lossless ($\mathrm{Instructor}$ is common and $\mathrm{Instructor} \to \mathrm{Course}$ holds in Teaches). But the dependency $\\{\mathrm{Course}, \mathrm{Student}\\} \to \mathrm{Instructor}$ is Not preserved.

Theorem 4.4. Not every relation can be decomposed into BCNF while preserving dependencies. 3NF Is the strongest normal form guaranteeing dependency-preserving, lossless-join decomposition.

:::caution Common Pitfall Do not confuse partial dependency (2NF violation) with transitive dependency (3NF violation). A Partial dependency involves a proper subset of a candidate key determining a non-prime attribute. A transitive dependency involves a non-key attribute determining another non-prime attribute.

4.5 Multivalued Dependencies and 4NF

A multivalued dependency (MVD) $X \twoheadrightarrow Y$ holds on relation $R$ if for any two Tuples $t_1, t_2 \in R$ with $t_1[X] = t_2[X]$There exists a tuple $t_3 \in R$ such that:

• $t_3[X] = t_1[X]$
• $t_3[Y] = t_1[Y]$
• $t_3[R - X - Y] = t_2[R - X - Y]$

: $X$ determines $Y$ independently of $R - X - Y$.

Trivial MVDs: $X \twoheadrightarrow Y$ where $Y \subseteq X$ or $X \cup Y = R$.

Fourth Normal Form (4NF). $R$ is in 4NF if for every non-trivial MVD $X \twoheadrightarrow Y$ That holds on $R$, $X$ is a superkey.

Theorem 4.5. Every relation in 4NF is in BCNF.

Proof. Every FD $X \to Y$ implies the MVD $X \twoheadrightarrow Y$. In 4NF, every non-trivial MVD $X \twoheadrightarrow Y$ requires $X$ to be a superkey. Therefore, every non-trivial FD $X \to Y$ Also requires $X$ to be a superkey, which is the BCNF condition. $\blacksquare$

4NF decomposition. Given $R$ with MVD $X \twoheadrightarrow Y$ where $X$ is not a superkey, Decompose into $R_1 = X \cup Y$ and $R_2 = R - Y$. The decomposition is lossless-join.

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