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2	Chapter 10 Functional Dependencies and Normalization for Relational Databases
3	Chapter Outline 1 Informal Design Guidelines for Relational Databases 1.1Semantics of the Relation Attributes 1.2 Redundant Information in Tuples and Update Anomalies 1.3 Null Values in Tuples 1.4 Spurious Tuples 2 Functional Dependencies (FDs) 2.1 Definition of FD 2.2 Inference Rules for FDs 2.3 Equivalence of Sets of FDs 2.4 Minimal Sets of FDs
4	Chapter Outline(contd.) 3 Normal Forms Based on Primary Keys 3.1 Normalization of Relations 3.2 Practical Use of Normal Forms 3.3 Definitions of Keys and Attributes Participating in Keys 3.4 First Normal Form 3.5 Second Normal Form 3.6 Third Normal Form 4 General Normal Form Definitions (For Multiple Keys) 5 BCNF (Boyce-Codd Normal Form)
5	1 Informal Design Guidelines for Relational Databases (1) What is relational database design? The grouping of attributes to form "good" relation schemas Two levels of relation schemas The logical "user view" level The storage "base relation" level Design is concerned mainly with base relations What are the criteria for "good" base relations?
6	Informal Design Guidelines for Relational Databases (2) We first discuss informal guidelines for good relational design Then we discuss formal concepts of functional dependencies and normal forms - 1NF (First Normal Form) - 2NF (Second Normal Form) - 3NF (Third Normal Form) - BCNF (Boyce-Codd Normal Form) Additional types of dependencies, further normal forms, relational design algorithms by synthesis are discussed in Chapter 11
7	1.1 Semantics of the Relation Attributes GUIDELINE 1: Informally, each tuple in a relation should represent one entity or relationship instance. (Applies to individual relations and their attributes). Attributes of different entities (EMPLOYEEs, DEPARTMENTs, PROJECTs) should not be mixed in the same relation Only foreign keys should be used to refer to other entities Entity and relationship attributes should be kept apart as much as possible. Bottom Line: Design a schema that can be explained easily relation by relation. The semantics of attributes should be easy to interpret.
8	Figure 10.1 A simplified COMPANY relational database schema
9	1.2 Redundant Information in Tuples and Update Anomalies Mixing attributes of multiple entities may cause problems Information is stored redundantly wasting storage Problems with update anomalies Insertion anomalies Deletion anomalies Modification anomalies
10	EXAMPLE OF AN UPDATE ANOMALY (1) Consider the relation: EMP_PROJ ( Emp#, Proj#, Ename, Pname, No_hours) Update Anomaly: Changing the name of project number P1 from “Billing” to “Customer-Accounting” may cause this update to be made for all 100 employees working on project P1.
11	EXAMPLE OF AN UPDATE ANOMALY (2) Insert Anomaly: Cannot insert a project unless an employee is assigned to . Inversely - Cannot insert an employee unless an he/she is assigned to a project. Delete Anomaly: When a project is deleted, it will result in deleting all the employees who work on that project. Alternately, if an employee is the sole employee on a project, deleting that employee would result in deleting the corresponding project.
12	Figure 10.3 Two relation schemas suffering from update anomalies
13	Figure 10.4 Example States for EMP_DEPT and EMP_PROJ
14	Guideline to Redundant Information in Tuples and Update Anomalies GUIDELINE 2: Design a schema that does not suffer from the insertion, deletion and update anomalies. If there are any present, then note them so that applications can be made to take them into account
15	1.3 Null Values in Tuples GUIDELINE 3: Relations should be designed such that their tuples will have as few NULL values as possible Attributes that are NULL frequently could be placed in separate relations (with the primary key) Reasons for nulls: attribute not applicable or invalid attribute value unknown (may exist) value known to exist, but unavailable
16	1.4 Spurious Tuples Bad designs for a relational database may result in erroneous results for certain JOIN operations The "lossless join" property is used to guarantee meaningful results for join operations GUIDELINE 4: The relations should be designed to satisfy the lossless join condition. No spurious tuples should be generated by doing a natural-join of any relations.
17	Spurious Tuples (2) There are two important properties of decompositions: non-additive or losslessness of the corresponding join preservation of the functional dependencies. Note that property (a) is extremely important and cannot be sacrificed. Property (b) is less stringent and may be sacrificed. (See Chapter 11).
18	2.1 Functional Dependencies (1) Functional dependencies (FDs) are used to specify formal measures of the "goodness" of relational designs FDs and keys are used to define normal forms for relations FDs are constraints that are derived from the meaning and interrelationships of the data attributes A set of attributes X functionally determines a set of attributes Y if the value of X determines a unique value for Y
19	Functional Dependencies (2) X -> Y holds if whenever two tuples have the same value for X, they must have the same value for Y For any two tuples t1 and t2 in any relation instance r(R): If t1[X]=t2[X], then t1[Y]=t2[Y] X -> Y in R specifies a constraint on all relation instances r(R) Written as X -> Y; can be displayed graphically on a relation schema as in Figures. ( denoted by the arrow: ). FDs are derived from the real-world constraints on the attributes
20	Examples of FD constraints (1) social security number determines employee name SSN -> ENAME project number determines project name and location PNUMBER -> {PNAME, PLOCATION} employee ssn and project number determines the hours per week that the employee works on the project {SSN, PNUMBER} -> HOURS
21	Examples of FD constraints (2) An FD is a property of the attributes in the schema R The constraint must hold on every relation instance r(R) If K is a key of R, then K functionally determines all attributes in R (since we never have two distinct tuples with t1[K]=t2[K])
22	2.2 Inference Rules for FDs (1) Given a set of FDs F, we can infer additional FDs that hold whenever the FDs in F hold Armstrong's inference rules: IR1. (Reflexive) If Y subset-of X, then X -> Y IR2. (Augmentation) If X -> Y, then XZ -> YZ (Notation: XZ stands for X U Z) IR3. (Transitive) If X -> Y and Y -> Z, then X -> Z IR1, IR2, IR3 form a sound and complete set of inference rules
23	Inference Rules for FDs (2) Some additional inference rules that are useful: (Decomposition) If X -> YZ, then X -> Y and X -> Z (Union) If X -> Y and X -> Z, then X -> YZ (Psuedotransitivity) If X -> Y and WY -> Z, then WX -> Z The last three inference rules, as well as any other inference rules, can be deduced from IR1, IR2, and IR3 (completeness property)
24	Inference Rules for FDs (3) Closure of a set F of FDs is the set F⁺ of all FDs that can be inferred from F Closure of a set of attributes X with respect to F is the set X ⁺ of all attributes that are functionally determined by X X ⁺ can be calculated by repeatedly applying IR1, IR2, IR3 using the FDs in F
25	2.3 Equivalence of Sets of FDs Two sets of FDs F and G are equivalent if: - every FD in F can be inferred from G, and - every FD in G can be inferred from F Hence, F and G are equivalent if F ⁺ =G ⁺ Definition: F covers G if every FD in G can be inferred from F (i.e., if G ⁺ subset-of F ⁺) F and G are equivalent if F covers G and G covers F There is an algorithm for checking equivalence of sets of FDs
26	2.4 Minimal Sets of FDs (1) A set of FDs is minimal if it satisfies the following conditions: Every dependency in F has a single attribute for its RHS. We cannot remove any dependency from F and have a set of dependencies that is equivalent to F. We cannot replace any dependency X -> A in F with a dependency Y -> A, where Y proper-subset-of X ( Y subset-of X) and still have a set of dependencies that is equivalent to F.
27	Minimal Sets of FDs (2) Every set of FDs has an equivalent minimal set There can be several equivalent minimal sets There is no simple algorithm for computing a minimal set of FDs that is equivalent to a set F of FDs To synthesize a set of relations, we assume that we start with a set of dependencies that is a minimal set (e.g., see algorithms 11.2 and 11.4)
28	3 Normal Forms Based on Primary Keys 3.1 Normalization of Relations 3.2 Practical Use of Normal Forms 3.3 Definitions of Keys and Attributes Participating in Keys 3.4 First Normal Form 3.5 Second Normal Form 3.6 Third Normal Form
29	3.1 Normalization of Relations (1) Normalization: The process of decomposing unsatisfactory "bad" relations by breaking up their attributes into smaller relations Normal form: Condition using keys and FDs of a relation to certify whether a relation schema is in a particular normal form
30	Normalization of Relations (2) 2NF, 3NF, BCNF based on keys and FDs of a relation schema 4NF based on keys, multi-valued dependencies : MVDs; 5NF based on keys, join dependencies : JDs (Chapter 11) Additional properties may be needed to ensure a good relational design (lossless join, dependency preservation; Chapter 11)
31	3.2 Practical Use of Normal Forms Normalization is carried out in practice so that the resulting designs are of high quality and meet the desirable properties The practical utility of these normal forms becomes questionable when the constraints on which they are based are hard to understand or to detect The database designers need not normalize to the highest possible normal form. (usually up to 3NF, BCNF or 4NF) Denormalization: the process of storing the join of higher normal form relations as a base relation—which is in a lower normal form
32	3.3 Definitions of Keys and Attributes Participating in Keys (1) A superkey of a relation schema R = {A₁, A₂, ...., A_n} is a set of attributes S subset-of R with the property that no two tuples t₁ and t₂ in any legal relation state r of R will have t₁[S] = t₂[S] A key K is a superkey with the additional property that removal of any attribute from K will cause K not to be a superkey any more.
33	Definitions of Keys and Attributes Participating in Keys (2) If a relation schema has more than one key, each is called a candidate key. One of the candidate keys is arbitrarily designated to be the primary key, and the others are called secondary keys. A Prime attribute must be a member of some candidate key A Nonprime attribute is not a prime attribute—that is, it is not a member of any candidate key.
34	3.2 First Normal Form Disallows composite attributes, multivalued attributes, and nested relations; attributes whose values for an individual tuple are non-atomic Considered to be part of the definition of relation
35	Figure 10.8 Normalization into 1NF
36	Figure 10.9 Normalization nested relations into 1NF
37	3.3 Second Normal Form (1) Uses the concepts of FDs, primary key Definitions: Prime attribute - attribute that is member of the primary key K Full functional dependency - a FD Y -> Z where removal of any attribute from Y means the FD does not hold any more Examples: - {SSN, PNUMBER} -> HOURS is a full FD since neither SSN -> HOURS nor PNUMBER -> HOURS hold - {SSN, PNUMBER} -> ENAME is not a full FD (it is called a partial dependency ) since SSN -> ENAME also holds
38	Second Normal Form (2) A relation schema R is in second normal form (2NF) if every non-prime attribute A in R is fully functionally dependent on the primary key R can be decomposed into 2NF relations via the process of 2NF normalization
39	Figure 10.10 Normalizing into 2NF and 3NF
40	Figure 10.11 Normalization into 2NF and 3NF
41	3.4 Third Normal Form (1) Definition: Transitive functional dependency - a FD X -> Z that can be derived from two FDs X -> Y and Y -> Z Examples: - SSN -> DMGRSSN is a transitive FD since SSN -> DNUMBER and DNUMBER -> DMGRSSN hold - SSN -> ENAME is non-transitive since there is no set of attributes X where SSN -> X and X -> ENAME
42	Third Normal Form (2) A relation schema R is in third normal form (3NF) if it is in 2NF and no non-prime attribute A in R is transitively dependent on the primary key R can be decomposed into 3NF relations via the process of 3NF normalization NOTE: In X -> Y and Y -> Z, with X as the primary key, we consider this a problem only if Y is not a candidate key. When Y is a candidate key, there is no problem with the transitive dependency . E.g., Consider EMP (SSN, Emp#, Salary ). Here, SSN -> Emp# -> Salary and Emp# is a candidate key.
43	4 General Normal Form Definitions (For Multiple Keys) (1) The above definitions consider the primary key only The following more general definitions take into account relations with multiple candidate keys A relation schema R is in second normal form (2NF) if every non-prime attribute A in R is fully functionally dependent on every key of R
44	General Normal Form Definitions (2) Definition: Superkey of relation schema R - a set of attributes S of R that contains a key of R A relation schema R is in third normal form (3NF) if whenever a FD X -> A holds in R, then either: (a) X is a superkey of R, or (b) A is a prime attribute of R NOTE: Boyce-Codd normal form disallows condition (b) above
45	5 BCNF (Boyce-Codd Normal Form) A relation schema R is in Boyce-Codd Normal Form (BCNF) if whenever an FD X -> A holds in R, then X is a superkey of R Each normal form is strictly stronger than the previous one Every 2NF relation is in 1NF Every 3NF relation is in 2NF Every BCNF relation is in 3NF There exist relations that are in 3NF but not in BCNF The goal is to have each relation in BCNF (or 3NF)
46	Figure 10.12 Boyce-Codd normal form
47	Figure 10.13 a relation TEACH that is in 3NF but not in BCNF
48	Achieving the BCNF by Decomposition (1) Two FDs exist in the relation TEACH: fd1: { student, course} -> instructor fd2: instructor ->course {student, course} is a candidate key for this relation and that the dependencies shown follow the pattern in Figure 10.12 (b). So this relation is in 3NF but not in BCNF A relation NOT in BCNF should be decomposed so as to meet this property, while possibly forgoing the preservation of all functional dependencies in the decomposed relations. (See Algorithm 11.3)
49	Achieving the BCNF by Decomposition (2) Three possible decompositions for relation TEACH {student, instructor} and {student, course} {course, instructor } and {course, student} {instructor, course } and {instructor, student} All three decompositions will lose fd1. We have to settle for sacrificing the functional dependency preservation. But we cannot sacrifice the non-additivity property after decomposition. Out of the above three, only the 3^rd decomposition will not generate spurious tuples after join.(and hence has the non-additivity property). A test to determine whether a binary decomposition (decomposition into two relations) is nonadditive (lossless) is discussed in section 11.1.4 under Property LJ1. Verify that the third decomposition above meets the property.