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Chapter 8: Relational Database Design

Chapter 8: Relational Database Design

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Chapter 8: Relational Database Design

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  1. Chapter 8: Relational Database Design • First Normal Form • Functional Dependencies • Decomposition • Boyce-Codd Normal Form • Third Normal Form

  2. First Normal Form • R is in first normal form if the domains of all attributes of R are atomic • In object relational/object oriented databases, attributes can be composite or multi-valued • But in relational databases, composite attributes will need to be flatten out and multi-valued attributes need to be represented by another relation

  3. Pitfalls in Relational Database Design • We can create tables to represent an ER design in many different ways • Combine attributes differently to create tables • Why do we choose some ways over the others? • Redundancy • Inability to represent certain information • E.g. relationships among attributes

  4. ER for Banking Enterprise

  5. Schema Diagram for the Banking Enterprise

  6. Example • Consider the relation schema:Lending-schema = (branch-name, branch-city, assets, customer-name,loan-number, amount) • Redundancy, why? • Inability to represent certain information, why? • Cannot store information about a branch if no loans exist • Can use null values, but they are difficult to handle.

  7. Why Redundancy Is Bad? • Wastes space • Complicates updating, introducing possibility of inconsistency of assets value • We know why inability to represent certain information is bad.

  8. 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)

  9. Lossless-join decomposition • All attributes of an original schema (R) must appear in the decomposition (R1, R2): R = R1  R2 • For all possible relations r on schema R r = R1 (r) R2 (r)

  10. Non Lossless-Join Decomposition • Decomposition of R = (A, B) • R1 = (A) R2 = (B) A B A B    1 2 1   1 2 B(r) A(r) r A B A (r) B (r) We do not loss any tuple but we lose the relationship between A and B     1 2 1 2

  11. Relational DB Design Process • 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 {R1, R2, ..., Rn} such that • each relation is in good form • the decomposition is a lossless-join decomposition • Our theory is based on: • functional dependencies • multivalued dependencies

  12. Functional Dependencies • 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. • A functional dependency is a generalization of the notion of a key.

  13. Functional Dependencies • Let R be a relation schema   R and   R • The functional dependency  holds onR if and only if for any legal relations r(R), whenever any two tuples t1and t2 of r agree on the attributes , they also agree on the attributes . That is, t1[] = t2 []  t1[ ] = t2 [ ]

  14. Example • Example: Consider r(A,B) with the following instance of r. • On this instance, AB does NOT hold, but BA does hold. • Note: function dependency needs to hold for any possible instance of a relation. • 4 • 1 5 • 3 7

  15. More Example • Consider the schema: Loan-info-schema = (customer-name,loan-number, branch-name, amount). We expect this set of functional dependencies to hold: loan-numberamount loan-number  branch-name but would not expect the following to hold: loan-number customer-name (why?)

  16. Keys Defined by Functional Dependencies • 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

  17. Example Keys • Lending-schema = (branch-name, branch-city, assets, customer-name,loan-number, amount) • Superkeys? • Candidate keys?

  18. Functional Dependencies • A functional dependency is trivial if it is satisfied by all instances of a relation • E.g. • customer-name, loan-number customer-name • customer-name customer-name • In general,   is trivial if   

  19. Closure of Attribute Sets • Given a set of attributes a, define the closureof aunderF (denoted by a+) as the set of attributes that are functionally determined by a under F:ais in F+   a+

  20. Algorithm to compute a+ • The closure of a under F result := a;while (changes to result) do for each in F do begin if  result • then result := result end

  21. Example of Attribute Set Closure • R = (A, B, C, G, H, I) • F = {A BA C CG HCG IB H} • (AG)+ 1. result = AG 2. result = ABCG (A C and A  B) 3. result = ABCGH (CG H and CG  AGBC) 4. result = ABCGHI (CG I and CG  AGBCH)

  22. Example Closures • Lending-schema = (branch-name, branch-city, assets, customer-name,loan-number, amount) • Closure of attribute set (loan-number)? • Closure of attribute set (customer-name, loan-number)? • Closure of attribute set (branch-name)? • Given: • loan-number  branch-name, amount • branch-name  branch-city, assets • customer-name,loan-number  Lending-schema

  23. Testing for superkey and candidate key Testing functional dependencies Computing closure of F Uses of Attribute Closure

  24. Testing for Keys • To test if  is a superkey, we compute +, and check if +contains all attributes of R. • To test if  is a candidate key, first make sure  is a superkey. Then make sure no subset of  is a superkey • In the previous example, is AG a candidate key? • Is AG a super key? • Does AG R? == Is (AG)+  R • Is any subset of AG a superkey? • Does AR? == Is (A)+  R • Does GR? == Is (G)+  R

  25. Example: Testing for keys • Lending-schema = (branch-name, branch-city, assets, customer-name,loan-number, amount) • Is (loan-number) a superkey/candidate key? • Is (customer-name, loan-number) a superkey/candidate key? • Is (branch-name) a superkey/candidate key? • Is (customer-name, loan-number,branch-name) a superkey/candidate key? • Given: • loan-number  branch-name,amount • branch-name  branch-city, assets • customer-name,loan-number  Lending-schema

  26. Testing Functional Dependencies • To check if a functional dependency    holds (or, in other words, is in F+), just check if   +.

  27. Example: Testing for functional dependencies • Lending-schema = (branch-name, branch-city, assets, customer-name,loan-number, amount) • Does loan-number  branch-city, assets hold? • Does customer-name, loan-number, amount  amount hold? • Given: • loan-number  branch-name,amount • branch-name  branch-city, assets • customer-name,loan-number  Lending-schema

  28. Computing Closure of F • But what is a closure of F? • Given a set F set of functional dependencies, there are certain other functional dependencies that are logically implied by F. • E.g. If A  B and B  C, then we can infer that A  C • The set of all functional dependencies logically implied by F is the closure of F. • We denote the closure of F by F+.

  29. Armstrong’s Axioms • Sound and Complete rules: • if   , then   (reflexivity) • if  , then    (augmentation) • if  , and   , then   (transitivity) • These rules are • sound (generate only functional dependencies that actually hold) and • complete (generate all functional dependencies that hold).

  30. Example • 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 • by augmenting A C with G, to get AG CG and then transitivity with CG I • CG HI • from CG H and CG I : “union rule” can be inferred from • definition of functional dependencies, or • Augmentation of CG I to infer CG  CGI, augmentation ofCG H to inferCGI HI, and then transitivity

  31. Closure of Functional Dependencies • Derived rules from Armstrong’s axioms • If   holds and  holds, then    holds (union) • If    holds, then   holds and  holds (decomposition) • If   holds and   holds, then   holds (pseudotransitivity)

  32. When we compute the closure of attribute set , we already implicitly used Armstrong’s axioms • Let us just use the closure of attribute set to calculate the closure of functional dependency set

  33. Now Compute Closure of F • For each   R, we find the closure +, and for each S  +, we output a functional dependency   S.

  34. Example: Closure of F • Lending-schema = (branch-name, branch-city, assets, customer-name,loan-number, amount) • How to compute the closure of F (F is given below)? • Given F: • loan-number  branch-name,amount • branch-name  branch-city, assets • customer-name,loan-number  Lending-schema

  35. We just talked about the maximal set of functional dependencies that can be derived from a functional dependent set F • Closure of F • What about the minimal set of functional dependencies that is equivalent functional dependent set F • Canonical cover of F

  36. Canonical Cover • Intuitively, a canonical cover of F is a “minimal” set of functional dependencies equivalent to F, having no redundant dependencies or redundant parts of dependencies • What does “equivalent” mean? • What does “minimal” mean?

  37. Equivalent Means: • Give two functional dependency sets F and F’, they are equivalent if and only if: • F logically implies all functional dependencies in F’ • F’ logically implies all functional dependencies in F • We use to represent logically imply and to represent equivalent • How to test if F F’? • For each  F’, test if it holds in F • You may need to calculate a+ in F

  38. Minimal Means: • No redundant functional dependencies! • Sets of functional dependencies may have redundant dependencies that can be inferred from the others • Eg: A  C is redundant in: {A  B, B  C, A  C} • Parts of a functional dependency may be redundant • E.g. on RHS: {A  B, B  C, A  CD} can be simplified to {A  B, B  C, A  D} • E.g. on LHS: {A  B, B  C, AC  D} can be simplified to {A  B, B  C, A  D}

  39. Extraneous Attributes • For any   in F • Attribute A   is extraneous ifF (F – {})  {( – A) }. • Attribute A  is extraneous if F (F – {})  { (– A)} • Use attribute closures to check equivalence

  40. Examples • Given F = {AC, ABC } • B is extraneous in AB C because {AC, AB C} is equivalent to {AC, AC } = {AC} • Given F = {AC, ABCD} • C is extraneous in ABCD because {AC, ABCD} is equivalent to {AC, ABD}

  41. Canonical Cover • A canonical coverfor F is a set of dependencies Fc such that • F logically implies all dependencies in Fc, and • Fclogically implies all dependencies in F, and • No functional dependency in Fccontains an extraneous attribute, and • Each left side of functional dependency in Fcis unique.

  42. Compute a canonical cover for F repeatUse the union rule to replace any dependencies in F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 does not change • Note: Union rule may become applicable after some extraneous attributes have been deleted, so it has to be re-applied

  43. 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} • Is B extraneous in ABC • {A BC, B C, ABC} ? {A C, B C, ABC} NO • Is C extraneous in ABC • {A BC, B C, ABC} ? {A B, B C, ABC} Yes • Set is now {A B, B C, ABC} • Is A is extraneous in ABC • {A B, B C, ABC} ? {A B, B C, BC} Yes • Set is now {A B, B C} • The canonical cover is: {A B, B C}

  44. Example: Canonical Cover of F • Lending-schema = (branch-name, branch-city, assets, customer-name,loan-number, amount) • How to compute the Canonical Cover of F (F is given below)? • Given F: • loan-number  branch-name,branch-city,assets,amount • branch-name  branch-city, assets • customer-name,loan-number  Lending-schema