Bài giảng Database System - 8 & 9. Functional Dependencies & Normalization for Relational DBs

Top-Down Database DesignMini-worldRequirementsConceptual schemaE1E2RRelation schemas?Functional Dependencies & Normalization for Relational DBsOutlineIntroductionFunctional dependencies (FDs)Definition of FDDirect, indirect, partial dependenciesInference Rules for FDsEquivalence of Sets of FDsMinimal Sets of FDsNormalization1NF and dependency problems2NF – solves partial dependency3NF – solves indirect dependencyBCNF – well-normalized relationsNotes and suggestionsSummaryReading suggestion: [1]: Chapters 10, 11; [2]: Chapters 13, 14 *IntroductionEach relation schema consists of a number of attributes and the relational database schema consists of a number of relation schemasAttributes are grouped to form a relation schemaNeed some formal measure of why one grouping of attributes into a relation schema may be better than another *Introduction“Goodness” measures:Redundant information in tuplesUpdate anomalies: modification, deletion, insertionReducing the NULL values in tuplesDisallowing the possibility of generating spurious tuples *IntroductionRedundant information in tuples: the attribute values pertaining to a particular department (DNUMBER, DNAME, DMGRSSN) are repeated for every employee who works for that department *IntroductionUpdate anomalies: modification, deletion, insertionModificationAs the manager of a dept. changes we have to update many values according to employees working for that dept.Easy to make the DB inconsistent *IntroductionDeletion: if Borg James E. leaves, we delete his tuple and lose the existing of dept. 1, the name of dept. 1, and who is the manager of dept. 1 *IntroductionInsertion:How can we create a department before any employees are assigned to it ?? *IntroductionReducing the NULL values in tuplesEmployees not assigned to any dept.: waste the storage spaceOther difficulties: aggregation operations (e.g., COUNT, SUM) and joins *IntroductionDisallowing the possibility of generating spurious tuplesEMP_PROJ(SSN, PNUMBER, HOURS, ENAME, PNAME, PLOCATION)EMP_LOCS(ENAME, PLOCATION)EMP_PROJ1(SSN, PNUMBER, HOURS, PNAME, PLOCATION)Generation of invalid and spurious data during JOINS: PLOCATION is the attribute that relates EMP_LOCS and EMP_PROJ1, and PLOCATION is neither a primary key nor a foreign key in either EMP_LOCS or EMP_PROJ1 (cf. chapter 10 [1] for more details) *IntroductionDisallowing the possibility of generating spurious tuples *IntroductionDisallowing the possibility of generating spurious tuples *IntroductionDisallowing the possibility of generating spurious tuples *Introduction“Goodness” measures:Redundant information in tuplesUpdate anomalies: modification, deletion, insertionReducing the NULL values in tuplesDisallowing the possibility of generating spurious tuples NormalizationIt helps DB designers determine the best relation schemasA formal framework for analyzing relation schemas based on their keys and on the functional dependencies among their attributesA series of normal form tests that can be carried out on individual relation schemas so that the relational database can be normalized to any desired degree It is based on the concept of normal form 1NF, 2NF, 3NF, BCNF, 4NF, 5 NFIt is a process which ensures that the data is structured in such a way that attributes are grouped with the PK. Attributes that do not directly depend on PK may be extracted to form a new relation *IntroductionThere are two important properties of decompositions: non-additive or losslessness of the corresponding joinpreservation of the functional dependenciesNote that property (a) is extremely important and cannot be sacrificed. Property (b) is less stringent and may be sacrificed (see chapter 11) *Functional Dependencies (FDs)Definition of FDDirect, indirect, partial dependenciesInference Rules for FDsEquivalence of Sets of FDsMinimal Sets of FDs *Functional Dependencies (FDs)Functional dependencies (FDs) are used to specify formal measures of the "goodness" of relational designsFDs and keys are used to define normal forms for relationsFDs are constraints that are derived from the meaning and interrelationships of the data attributesA set of attributes X functionally determines a set of attributes Y if the value of X determines a unique value for Y *Functional Dependencies (FDs)X -> Y holds if whenever two tuples have the same value for X, they must have the same value for YFor 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)Examples:social security number determines employee name: SSN -> ENAMEproject 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 *Functional Dependencies (FDs)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]) *Functional Dependencies (FDs)Definition of FDDirect, indirect, partial dependenciesInference Rules for FDsEquivalence of Sets of FDsMinimal Sets of FDs *Functional Dependencies (FDs)Direct dependency (fully functional dependency): All attributes in a R must be fully functionally dependent on the primary key (or the PK is a determinant of all attributes in R)Performer-idPerformer-namePerformer-typePerformer-location *Functional Dependencies (FDs)Indirect dependency (transitive dependency): Value of an attribute is not determined directly by the primary keyPerformer-idPerformer-namePerformer-typePerformer-locationFee *Functional Dependencies (FDs)Partial dependencyComposite determinant - more than one value is required to determine the value of another attribute, the combination of values is called a composite determinantEMP_PROJ(SSN, PNUMBER, HOURS, ENAME, PNAME, PLOCATION){SSN, PNUMBER} -> HOURS Partial dependency - if the value of an attribute does not depend on an entire composite determinant, but only part of it, the relationship is known as the partial dependencySSN -> ENAME PNUMBER -> {PNAME, PLOCATION} *Functional Dependencies (FDs)Partial dependencyPerformer-idPerformer-namePerformer-typePerformer-locationFeeAgent-idAgent-nameAgent-location *Functional Dependencies (FDs)Definition of FDDirect, indirect, partial dependenciesInference Rules for FDsEquivalence of Sets of FDsMinimal Sets of FDs *Functional Dependencies (FDs)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 X, then X -> YIR2. (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 *Functional Dependencies (FDs)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) *Functional Dependencies (FDs)Closure of a set F of FDs is the set F+ of all FDs that can be inferred from FClosure of a set of attributes X with respect to F is the set X + of all attributes that are functionally determined by XX + can be calculated by repeatedly applying IR1, IR2, IR3 using the FDs in F *Functional Dependencies (FDs)Definition of FDDirect, indirect, partial dependenciesInference Rules for FDsEquivalence of Sets of FDsMinimal Sets of FDs *Functional Dependencies (FDs)Two sets of FDs F and G are equivalent if F+ = G+Definition: F covers G if G+ F+. F and G are equivalent if F covers G and G covers FThere is an algorithm for checking equivalence of sets of FDs (see chapter 10 [1]) *Functional Dependencies (FDs)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 *Functional Dependencies (FDs)Every set of FDs has an equivalent minimal setThere can be several equivalent minimal setsThere is no simple algorithm for computing a minimal set of FDs that is equivalent to a set F of FDsTo 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) *OutlineIntroductionFunctional dependencies (FDs)Definition of FDDirect, indirect, partial dependenciesInference Rules for FDsEquivalence of Sets of FDsMinimal Sets of FDsNormalization1NF and dependency problems2NF – solves partial dependency3NF – solves indirect dependencyBCNF – well-normalized relationsNotes and suggestionsSummaryReading suggestion: [1]: Chapters 10, 11; [2]: Chapters 13, 14 *NormalizationNormalization: The process of decomposing unsatisfactory "bad" relations by breaking up their attributes into smaller relationsNormal form: Using keys and FDs of a relation to certify whether a relation schema is in a particular normal formNormalization is carried out in practice so that the resulting designs are of high quality and meet the desirable properties The database designers need not normalize to the highest possible normal form (3NF, BCNF or 4NF) *NormalizationTwo new concepts:A Prime attribute must be a member of some candidate keyA Nonprime attribute is not a prime attribute: it is not a member of any candidate key *Normalization1NF and dependency problems2NF – solves partial dependency3NF – solves indirect dependencyBCNF – well-normalized relations *NormalizationFirst normal form (1NF): there is only one value at the intersection of each row and column of a relation - no set valued attributes in 1 NF  Disallows composite attributes, multivalued attributes, and nested relationsTo be part of the formal definition of a relation in the basic (flat) relational model *1NF *1NF *1NFAgent-idVenue-idEvent-idBooking-datePerformer-locationAgent-nameAgent-locationVenue-nameVenue-locationEvent-nameEvent-typePerformer-idPerformer-namePerformer-typeFee1NF determinancy diagram *1NF *Normalization1NF and dependency problems2NF – solves partial dependency3NF – solves indirect dependencyBCNF – well-normalized relations *NormalizationSecond normal form (2NF) - all attributes must be fully functionally dependent on the primary key2NF solves partial dependency problem in 1NF Method: identify primary keys and group attributes that relate to the key together to form separate new relations *2NFPerformer-idPerformer-namePerformer-typePerformer-locationFeeRelation in 2NFP-idP-nameP- typeFeeP-location101BaronSinger75York105SteedDancer60Berlin108JonesActor85Bombay112EaglesActor85Leeds118MarkovDancer60Moscow126StokesComedian90Athens129ChongActor85Beijing134BrassSinger75London138NgSinger75Penang140StrongMagician72Rome141GomezMusician92Lisbon143TanSinger75Chicago147QureshiActor85London149TanActor85Taipei150PointerMagician72Paris152PeelDancer60London2NF determinancy diagram *2NFAgent-idAgent-nameAgent-locationVenue-idVenue-nameVenue-location2NF determinancy diagramRelation in 2NFA-idA-nameA-location1295BurtonLonton1435NunnBoston1504LeeTaipei1682TsangBeijing1460StritchRome1522EllisMadrid1509PatelYork1478BurnsLeeds1377WebbSydney1478BurnsLeeds1190PatelHue1802ChapelBristol1076EcclesOxford1409ArkleyYork1428VemonCairoV-idV-nameV-location59AtlasTokyo35PolisAthens54NationLisbon17SilburyTunis46RoyaleCairo75VostokKiev79FestiveRome28GrattonBoston84StateKiev82TowerLima92PalaceMilan62ShawOxford *2NF2NF determinancy diagramRelation in 2NFEvent-idEvent-typeEvent-nameE-idE-nameE-type959Show TimeMusical907Elgar 1Concert921Silver ShoeBallet942White LaceBallet901The DarkDrama913What NowDrama926Next YearDrama952Gold DaysDrama934AngelsOpera945Trick-TreatVariety show938New DawnDrama981BirdsongMusical957QuicktimeMusical963VanishMagic show941Mahler 1Concert964The FriendsDrama927ChansonOpera971Card TrickMagic show988Secret TapeDrama978Swift StepDance *2NF2NF determinancy diagramRelation in 2NFPerformer-idAgent-idVenue-idEvent-idBooking-dateP-idA-idV-idE-idBooking-date10112955995925-Nov-9910514353592107-Jan-0210515045494210-Feb-0210816827990129-Jul-0311214601792613-Aug-0011215224695205-May-9911215047595216-Mar-9912615095994502-Sept-0112914787992622-Jun-0013415042898118-Sept-0113815098495718-Aug-9914014781796318-Aug-9914114788494121-Jul-0014315047992721-Nov-0214710761795230-Apr-0014714097998817-Apr-0015214285997801-Oct-01 *2NFPerformer-idPerformer-namePerformer-locationPerformer-typeFeeRelation in 2NFP-idP-nameP- typeFeeP-loc’n101BaronSinger75York105SteedDancer60Berlin108JonesActor85Bombay112EaglesActor85Leeds118MarkovDancer60Moscow126StokesComedian90Athens129ChongActor85Beijing134BrassSinger75London138NgSinger75Penang140StrongMagician72Rome141GomezMusician92Lisbon143TanSinger75Chicago147QureshiActor85London149TanActor85Taipei150PointerMagician72Paris152PeelDancer60London2NF determinancy diagramProblem with 2NF:- Insertion- Modification- Deletion *2NF *Employee(SSN, Ename)Project(Pnumber, Pname, Plocation)Emp_Poj(SSN, Pnumber)Normalization1NF and dependency problems2NF – solves partial dependency3NF – solves indirect dependencyBCNF – well-normalized relations *NormalizationA 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 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 *Normalization3NF solves indirect (transitive) dependencies problem in 1NF and 2NFMethod: identify all transitive dependencies and each transitive dependency will form a new relation, with non-prime attributes participating in the transitive dependency and the attribute which determines others as the attributes for the new relation *3NF3NF determinancy diagramRelation in 3NFPerformer-typeFeePerformer-namePerformer-typePerformer-locationPerformer-idP-idP-nameP- typeP-loc’n101BaronSingerYork105SteedDancerBerlin108JonesActorBombay112EaglesActorLeeds118MarkovDancerMoscow126StokesComedianAthens129ChongActorBeijing134BrassSingerLondon138NgSingerPenang140StrongMagicianRome141GomezMusicianLisbon143TanSingerChicago147QureshiActorLondon149TanActorTaipei150PointerMagicianParis152PeelDancerLondonP- typeFeeSinger75Dancer60Actor85Comedian90Magician72Musician92 *3NF3NF determinancy diagramAgent-locationAgent-idAgent-nameAgent-locationEvent-idEvent-typeEvent-nameVenue-idVenue-nameVenue-locationPerformer-idAgent-idVenue-idEvent-idBooking-date *Normalization1NF and dependency problems2NF – solves partial dependency3NF – solves indirect dependencyBCNF – well-normalized relations *SUMMARY OF NORMAL FORMS based on Primary Keys *General Normal Form DefinitionsThe above definitions consider the primary key onlyThe following more general definitions take into account relations with multiple candidate keys *General Normal Form DefinitionsA 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 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 *Normalization1NF and dependency problems2NF – solves partial dependency3NF – solves indirect dependencyBCNF – well-normalized relations *NormalizationA 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 RMore details: [1] -> chapters 10, 11The goal is to have each relation in BCNF (or 3NF) *BCNFBoyce-Codd normal form. (a) BCNF normalization of LOTS1A with the functional dependency FD2 being lost in the decomposition. (b) A schematic relation with FDs; it is in 3NF, but not in BCNF. *OutlineIntroductionFunctional dependencies (FDs)Definition of FDDirect, indirect, partial dependenciesInference Rules for FDsEquivalence of Sets of FDsMinimal Sets of FDsNormalization1NF and dependency problems2NF – solves partial dependency3NF – solves indirect dependencyBCNF – well-normalized relationsNotes and suggestionsSummaryReading suggestion: [1]: Chapters 10, 11; [2]: Chapters 13, 14 *Notes & Suggestions[1]: chapter 114NF: based on multivalued dependency (MVD)5NF: based on join dependencySuch a dependency is very difficult to detect in practice and therefore, normalization into 5NF is considered very rarely in practiceOther normal forms & algorithmsER modeling: top-down database designBottom-up database design ?? *SummaryIntroduction: “goodness” measures for relationsFunctional dependencies (FDs)Definition of FDDirect, indirect, partial dependenciesInference Rules for FDsEquivalence of Sets of FDsMinimal Sets of FDsNormalization1NF and dependency problems2NF – solves partial dependency3NF – solves indirect dependencyBCNF – well-normalized relationsNotes and suggestions: 4NF, 5NF, other normal forms, and bottom-up DB design *Q&AQuestion ?