©Silberschatz, Korth and Sudarshan7.1Database System Concepts Chapter 7: Relational Database Design First Normal Form Pitfalls in Relational Database Design.

Slides:



Advertisements
Similar presentations
Chapter 7: Relational Database Design. ©Silberschatz, Korth and Sudarshan7.2Database System Concepts Chapter 7: Relational Database Design First Normal.
Advertisements

Dr. Kalpakis CMSC 461, Database Management Systems URL: Relational Database Design.
C.1 Appendix C: Advanced Relational Database Design Reasoning with MVDs Higher normal forms Join dependencies and PJNF DKNF.
Database System Concepts, 6 th Ed. ©Silberschatz, Korth and Sudarshan See for conditions on re-usewww.db-book.com Appendix B: Advanced.
©Silberschatz, Korth and Sudarshan Relational Database Design First Normal Form Pitfalls in Relational Database Design Functional Dependencies Decomposition.
7.1 Chapter 7: Relational Database Design. 7.2 Chapter 7: Relational Database Design Features of Good Relational Design Atomic Domains and First Normal.
Chapter 7: Relational Database Design. ©Silberschatz, Korth and Sudarshan7.2Database System Concepts Chapter 7: Relational Database Design First Normal.
Chapter 7: Relational Database Design. ©Silberschatz, Korth and Sudarshan7.2Database System Concepts Chapter 7: Relational Database Design First Normal.
Chapter 7: Relational Database Design. ©Silberschatz, Korth and Sudarshan7.2Database System Concepts Chapter 7: Relational Database Design First Normal.
Database System Concepts, 5th Ed. ©Silberschatz, Korth and Sudarshan See for conditions on re-usewww.db-book.com Chapter 7: Relational.
Chapter 7: Relational Database Design. ©Silberschatz, Korth and Sudarshan7.2Database System Concepts Chapter 7: Relational Database Design First Normal.
Slides adapted from A. Silberschatz et al. Database System Concepts, 5th Ed. Relational Database Design - part 2 - Database Management Systems I Alex Coman,
CMSC424: Database Design Instructor: Amol Deshpande
Normal Form Design addendum by C. Zaniolo. ©Silberschatz, Korth and Sudarshan7.2Database System Concepts Normal Form Design Compute the canonical cover.
Functional Dependencies (Part 3) Presented by Nash Raghavan All page numbers are in reference to Database System Concepts (5 th Edition)
1 CMSC424, Spring 2005 CMSC424: Database Design Lecture 9.
1 Functional Dependency and Normalization Informal design guidelines for relation schemas. Functional dependencies. Normal forms. Normalization.
1 Schema Refinement and Normal Forms Chapter 19 Raghu Ramakrishnan and J. Gehrke (second text book) In Course Pick-up box tomorrow.
Relational Database Design
Department of Computer Science and Engineering, HKUST Slide 1 7. Relational Database Design.
Chapter 8: Relational Database Design First Normal Form First Normal Form Functional Dependencies Functional Dependencies Decomposition Decomposition Boyce-Codd.
Relational Database Design
Database System Concepts, 6 th Ed. ©Silberschatz, Korth and Sudarshan See for conditions on re-usewww.db-book.com Chapter 8: Relational.
Database System Concepts, 5th Ed. ©Silberschatz, Korth and Sudarshan Chapter 7: Relational Database Design.
Chapter 7: Relational Database Design. ©Silberschatz, Korth and Sudarshan7.2 Chapter 7: Relational Database Design First Normal Form Goals of Relational.
Asst.Prof.Dr.İlker Kocabaş UBİ502 at
Computing & Information Sciences Kansas State University Wednesday, 01 Oct 2008CIS 560: Database System Concepts Lecture 15 of 42 Wednesday, 01 October.
Chapter 7: Relational Database Design. 7.2Unite International CollegeDatabase Management Systems Chapter 7: Relational Database Design Features of Good.
 Features of Good Relational Design  Atomic Domains and First Normal Form  Decomposition Using Functional Dependencies  Functional Dependency Theory.
Chapter 10 Functional Dependencies and Normalization for Relational Databases.
FUNCTIONAL DEPENDENCIES. Chapter Outline 1 Informal Design Guidelines for Relational Databases 1.1Semantics of the Relation Attributes 1.2 Redundant Information.
Computing & Information Sciences Kansas State University Monday, 13 Oct 2008CIS 560: Database System Concepts Lecture 18 of 42 Monday, 13 October 2008.
Database System Concepts, 5th Ed. ©Silberschatz, Korth and Sudarshan See for conditions on re-usewww.db-book.com Chapter 7: Relational.
Chapter 7: Relational Database Design. 7.2Unite International CollegeDatabase Management Systems Chapter 7: Relational Database Design Features of Good.
Chapter 7: Relational Database Design. ©Silberschatz, Korth and Sudarshan7.2Database System Concepts Chapter 7: Relational Database Design Pitfalls in.
Relational Database Design by Relational Database Design by Dr.S.Sridhar, Ph.D.(JNUD), RACI(Paris, NICE), RMR(USA), RZFM(Germany) DIRECTOR ARUNAI ENGINEERING.
Chapter 8: Relational Database Design First Normal Form First Normal Form Functional Dependencies Functional Dependencies Decomposition Decomposition Boyce-Codd.
Database System Concepts, 5th Ed. ©Silberschatz, Korth and Sudarshan See for conditions on re-usewww.db-book.com Chapter 7: Relational.
Database System Concepts, 5th Ed. ©Silberschatz, Korth and Sudarshan See for conditions on re-usewww.db-book.com Chapter 7: Relational.
Computing & Information Sciences Kansas State University Tuesday, 27 Feb 2007CIS 560: Database System Concepts Lecture 18 of 42 Tuesday, 27 February 2007.
Computing & Information Sciences Kansas State University Wednesday, 04 Oct 2006CIS 560: Database System Concepts Lecture 17 of 42 Wednesday, 04 October.
Database System Concepts, 5th Ed. ©Silberschatz, Korth and Sudarshan See for conditions on re-usewww.db-book.com Chapter 7: Relational.
Chapter 7: Relational Database Design. ©Silberschatz, Korth and Sudarshan7.2Database System Concepts Chapter 7: Relational Database Design First Normal.
Database System Concepts ©Silberschatz, Korth and Sudarshan See for conditions on re-usewww.db-book.com Chapter 7: Relational Database.
Chapter 7: Relational Database Design. ©Silberschatz, Korth and Sudarshan7.2Database System Concepts Chapter 7: Relational Database Design First Normal.
Database System Concepts, 6 th Ed. ©Silberschatz, Korth and Sudarshan See for conditions on re-usewww.db-book.com Appendix B: Advanced.
Database System Concepts, 5th Ed. ©Silberschatz, Korth and Sudarshan See for conditions on re-usewww.db-book.com Chapter 7: Relational.
Chapter 7: Relational Database Design
Chapter 7: Relational Database Design. ©Silberschatz, Korth and Sudarshan7.2Database System Concepts Chapter 7: Relational Database Design First Normal.
Chapter 8 Relational Database Design. 2 Relational Database Design: Goals n Reduce data redundancy (undesirable replication of data values) n Minimize.
Computing & Information Sciences Kansas State University Friday, 03 Oct 2007CIS 560: Database System Concepts Lecture 16 of 42 Wednesday, 03 October 2007.
Database System Concepts, 5th Ed. Bin Mu at Tongji University Chapter 7: Relational Database Design.
©Silberschatz, Korth and Sudarshan6.1Database System Concepts Chapter 6: Integrity Constraints Domain Constraints Referential Integrity Assertions Triggers.
Module 5: Overview of Database Design -- Normalization
Relational Database Design by Dr. S. Sridhar, Ph. D
Chapter 7: Relational Database Design
Relational Database Design
Relational Database Design
Chapter 8: Relational Database Design
Chapter 7: Relational Database Design
Module 5: Overview of Normalization
Chapter 7: Relational Database Design
Normal Form: 4 & 5.
Chapter 7: Relational Database Design
Instructor: Mohamed Eltabakh
Relational Database Design
Chapter 7: Relational Database Design
Normalization.
Chapter 7a: Overview of Database Design -- Normalization
Presentation transcript:

©Silberschatz, Korth and Sudarshan7.1Database System Concepts Chapter 7: Relational Database Design First Normal Form Pitfalls in Relational Database Design Functional Dependencies Decomposition Boyce-Codd Normal Form Third Normal Form (Multivalued Dependencies and Fourth Normal Form) (Overall Database Design Process)

©Silberschatz, Korth and Sudarshan7.2Database System Concepts First Normal Form Domain is atomic if its elements are considered to be indivisible units  Examples of non-atomic domains:  Set of names, composite attributes  Identification numbers like CS101 that can be broken up into parts A relational schema R is in first normal form if the domains of all attributes of R are atomic Non-atomic values complicate storage and encourage redundant (repeated) storage of data  E.g. Instead of a separate relation depositor  set of accounts stored with each customer, and set of owners stored with each account  In this chapter, we assume all relations are in first normal form

©Silberschatz, Korth and Sudarshan7.3Database System Concepts First Normal Form (Cont.) Atomicity is actually a property of how the elements of the domain are used.  Strings would normally be considered indivisible  But  Suppose that students are given roll numbers which are strings of the form CS0012 or EE1127  If the first two characters are extracted to find the department, the domain of roll numbers is not atomic.  Doing so is a bad idea: leads to encoding of information in application program rather than in the database.

©Silberschatz, Korth and Sudarshan7.4Database System Concepts Pitfalls in Relational Database Design Relational database design requires that we find a “good” collection of relation schemas. A bad design may lead to  Repetition of Information.  Inability to represent certain information.

©Silberschatz, Korth and Sudarshan7.5Database System ConceptsExample Consider the relation schema: Lending-schema = (branch-name, branch-city, assets, customer-name, loan-number, amount) Redundancy  Data for branch-name, branch-city, assets are repeated for each loan that a branch makes  Wastes space  Complicates updating, introducing possibility of inconsistency of assets value Null values  Cannot store information about a branch if no loans exist  Can use null values  but, difficult to handle.

©Silberschatz, Korth and Sudarshan7.6Database System Concepts Goal — Devise a Theory for the Following Decide whether a particular relation R is in “good” form. In the case that a relation R is not in “good” form, decompose it into a set of relations {R 1, R 2,..., R n } such that  each relation is in good form  the decomposition is a lossless-join decomposition Our theory is based on:  functional dependencies  multivalued dependencies

©Silberschatz, Korth and Sudarshan7.7Database System Concepts Functional Dependencies A key role in differentiating good database designs from bad database designs. A functional dependency is a generalization of the notion of a key. FDs are constraints on the set of legal relations. Require that the value for a certain set of attributes determines uniquely the value for another set of attributes.

©Silberschatz, Korth and Sudarshan7.8Database System Concepts Functional Dependencies (Cont.) Let R be a relation schema   R and   R The functional dependency    holds on R if and only if for any legal relations r(R), whenever any two tuples t 1 and t 2 of r agree on the attributes , they also agree on the attributes . That is, t 1 [  ] = t 2 [  ]  t 1 [  ] = t 2 [  ] Example: Consider r(A,B) with the following instance of r.  On this instance, A  B does NOT hold, but B  A does hold

©Silberschatz, Korth and Sudarshan7.9Database System Concepts Functional Dependencies (Cont.) K is a superkey for relation schema R if and only if K  R K is a candidate key for R if and only if  K  R, and  for no   K,   R FDs allow us to express constraints that cannot be expressed using superkeys. Consider the schema: Loan-info-schema = (customer-name, loan-number, branch-name, amount). The set of functional dependencies that we expect to hold: loan-number  amount loan-number  branch-name but would not expect the following to hold: loan-number  customer-name (e.g. a loan - husband-wife pair)

©Silberschatz, Korth and Sudarshan7.10Database System Concepts Use of Functional Dependencies We shall use FDs in two ways  To test relations to see whether they are legal under a given set of functional dependencies.  If a relation r is legal under a set F of functional dependencies, we say that r satisfies F.  To specify constraints on the set of legal relations  We say that F holds on R if all legal relations on R satisfy the set of F. Note  A specific instance of a relation schema may satisfy a FD even if the FD does not hold on all legal instances.  E.g. a specific instance of Loan-schema may, by chance, satisfy loan-number  customer-name.

©Silberschatz, Korth and Sudarshan7.11Database System Concepts Functional Dependencies (Cont.) Some functional dependency are said to be trivial if they are satisfied by all relations.  E.g.  customer-name, loan-number  customer-name  customer-name  customer-name  In general, a FD    is trivial if   

©Silberschatz, Korth and Sudarshan7.12Database System Concepts Closure of a Set of Functional Dependencies Given a set F of FDs, we can prove that certain other FDs hold. We say that such FDs are “logically implied” by F.  E.g. If A  B and B  C, then we can infer that A  C The set of all FDs logically implied by F is the closure of F. We denote the closure of F by F +. We can find all of F + by applying Armstrong’s axioms:  If   , then    (reflexivity)  If   , then      (augmentation)  If   , and   , then    (transitivity)  sound and complete Rules from Armstrong’s axioms:  If    and   , then     (union rule)  If    , then    and    (decomposition rule)  If    and    δ, then    δ (pseudotransitivity rule)

©Silberschatz, Korth and Sudarshan7.13Database System ConceptsExample R = (A, B, C, G, H, I) F = { A  B A  C CG  H CG  I B  H } Some members of F +  A  H  By transitivity from A  B and B  H  AG  I  Pseudotransitivity rule from A  C and CG  I  By augmenting A  C with G, to get AG  CG and then transitivity with CG  I  CG  HI  Union rule from CG  H and CG  I  Augmentation of CG  I to infer CG  CGI, augmentation of CG  H to infer CGI  HI, and then transitivity

©Silberschatz, Korth and Sudarshan7.14Database System Concepts Procedure for Computing F + To compute the closure of a set of F: F + = F repeat for each FD f in F + apply reflexivity and augmentation rules on f add the resulting FDs to F + for each pair of FDs f 1 and f 2 in F + if f 1 and f 2 can be combined using transitivity then add the resulting FD to F + until F + does not change any further

©Silberschatz, Korth and Sudarshan7.15Database System Concepts Closure of Attribute Sets Given a set of attributes  define the closure of  under F (denoted by  + ) as the set of attributes that are functionally determined by  under F:    is in F     + Algorithm to compute  + result :=  ; while (changes to result) do for each    in F do begin if   result then result := result   end

©Silberschatz, Korth and Sudarshan7.16Database System Concepts Example of Attribute Set Closure R = (A, B, C, G, H, I) F = { A  B A  C CG  H CG  I B  H } (AG) + 1.result = AG 2.result = ABCG(A  C and A  B) 3.result = ABCGH(CG  H and CG  ABCG) 4.result = ABCGHI(CG  I and CG  ABCGH)

©Silberschatz, Korth and Sudarshan7.17Database System Concepts Uses of Attribute Closure Testing for superkey  To test if  is a superkey, we compute  +, and check if  + contains all attributes of R. Testing functional dependencies  To check if a FD    holds (in other words, is in F + ), just check if    +.  That is, we compute  + by using attribute closure, and then check if it contains . Computing closure of F  For each   R, we find the closure  +, and for each S   +, we output a functional dependency   S.

©Silberschatz, Korth and Sudarshan7.18Database System Concepts Canonical Cover Sets of FDs may have redundant dependencies that can be inferred from the others  E.g. A  C is redundant in {A  B, B  C, A  C}  Parts of a functional dependency may be redundant  E.g. {A  B, B  C, A  CD} can be simplified to {A  B, B  C, A  D} Intuitively, a canonical cover of F is a “minimal” set of functional dependencies equivalent to F, with no redundant dependencies or having redundant parts of dependencies

©Silberschatz, Korth and Sudarshan7.19Database System Concepts Extraneous Attributes Consider a set F and the FD    in F  If A  , to check attribute A is extraneous let  =  -{A}, and check if    can be inferred from F. (compute  + under F, check if  + includes all attributes in  )  If A  , to check attribute A is extraneous consider F’ = (F – {    })  {   (  – A)} and check if   A can be inferred from F’. (compute  + under F’, check if  + includes A) Example: Given F = {A  C, AB  C }  B is extraneous in AB  C because A  C logically implies AB  C. Example: Given F = {A  C, AB  CD}  C is extraneous in AB  CD since AB  C can be inferred even after deleting C

©Silberschatz, Korth and Sudarshan7.20Database System Concepts Canonical Cover A canonical cover for F is a set of dependencies F c such that  F logically implies all dependencies in F c, and  F c logically implies all dependencies in F, and  No functional dependency in F c contains an extraneous attribute, and  Each left side of functional dependency in F c is unique. To compute a canonical cover for F: F c =F repeat Use the union rule to replace any dependencies in F c  1   1 and  1   2 with  1   1  2 Find a functional dependency    with an extraneous attribute either in  or in  If an extraneous attribute is found, delete it from    until F c does not change

©Silberschatz, Korth and Sudarshan7.21Database System Concepts Example R = (A, B, C) F = { A  BC B  C A  B AB  C } Combine A  BC and A  B into A  BC  Set is now {A  BC, B  C, AB  C} A is extraneous in AB  C because B  C logically implies AB  C.  Set is now {A  BC, B  C} C is extraneous in A  BC since A  BC is logically implied by A  B and B  C. The canonical cover is: A  B B  C Note: canonical cover might not be unique.

©Silberschatz, Korth and Sudarshan7.22Database System Concepts Decomposition Decompose the relation schema Lending-schema into: Branch-schema = (branch-name, branch-city,assets) Loan-info-schema = (customer-name, loan-number, branch-name, amount) All attributes of an original schema (R) must appear in the decomposition (R 1, R 2 ): R = R 1  R 2 Lossless-join decomposition. For all possible relations r on schema R r =  R1 (r)  R2 (r) A decomposition of R into R 1 and R 2 is lossless join if and only if at least one of the following dependencies is in F + :  R 1  R 2  R 1  R 1  R 2  R 2

©Silberschatz, Korth and Sudarshan7.23Database System Concepts Normalization Using Functional Dependencies When we decompose a relation schema R with a set of F into R 1, R 2,.., R n we want  Lossless-join decomposition: Otherwise decomposition would result in information loss.  No redundancy: The relations R i preferably should be in either Boyce- Codd Normal Form or Third Normal Form.  Dependency preservation: Let F i be the set of dependencies F + that include only attributes in R i.  Preferably the decomposition should be dependency preserving, that is, (F 1  F 2  …  F n ) + = F +  Otherwise, checking updates for violation of functional dependencies may require computing joins, which is expensive.

©Silberschatz, Korth and Sudarshan7.24Database System ConceptsExample R = (A, B, C) F = {A  B, B  C) R 1 = (A, B), R 2 = (B, C)  Lossless-join decomposition: R 1  R 2 = {B} and B  BC  Dependency preserving R 1 = (A, B), R 2 = (A, C)  Lossless-join decomposition: R 1  R 2 = {A} and A  AB  Not dependency preserving (cannot check B  C without computing R 1 R 2 )

©Silberschatz, Korth and Sudarshan7.25Database System Concepts Boyce-Codd Normal Form     is trivial (i.e.,    )  is a superkey for R A relation schema R is in BCNF with respect to a set F of FDs if for all functional dependencies in F + of the form    , where   R and   R, at least one of the following holds:

©Silberschatz, Korth and Sudarshan7.26Database System ConceptsExample R = (A, B, C) F = {A  B B  C} Key = {A} R is not in BCNF Decomposition R 1 = (A, B), R 2 = (B, C)  R 1 and R 2 in BCNF  Lossless-join decomposition  Dependency preserving

©Silberschatz, Korth and Sudarshan7.27Database System Concepts Third Normal Form There are some situations where  BCNF is not dependency preserving, and  Efficient checking for FD violation on updates is important Solution: Define a weaker normal form, called Third Normal Form.  Allows some redundancy  But, FDs can be checked on individual relations without computing a join.  There is always a lossless-join, dependency-preserving decomposition into 3NF.

©Silberschatz, Korth and Sudarshan7.28Database System Concepts A relation schema R is in third normal form (3NF) if for all:    in F + at least one of the following holds:     is trivial (i.e.,    )   is a superkey for R  Each attribute A in  –  is contained in a candidate key for R. (NOTE: each attribute may be in a different candidate key) If a relation is in BCNF it is in 3NF. Third condition is a minimal relaxation of BCNF to ensure dependency preservation 3NF (Cont.)

©Silberschatz, Korth and Sudarshan7.29Database System Concepts R = (J, K, L) F = {JK  L, L  K} Two candidate keys: JK and JL R is in 3NF JK  LJK is a superkey L  KK is contained in a candidate keyExample

©Silberschatz, Korth and Sudarshan7.30Database System Concepts Design Goals Goal for a relational database design is:  BCNF  Lossless join  Dependency preservation If we cannot achieve this, we accept one of  Lack of dependency preservation  Redundancy due to use of 3NF