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tion It should be clear from our example that a lossy-join decomposition is, in general, a bad database design Why is the decomposition lossy There is one attribute in common between Branchcustomer-schema and Customer-loan-schema: Branch-customer-schema Customer-loan-schema = {customer-name} The only way that we can represent a relationship between, for example, loan-number and branch-name is through customer-name This representation is not adequate because a customer may have several loans, yet these loans are not necessarily obtained from the same branch Let us consider another alternative design, in which we decompose Lending-schema into the following two schemas: Branch-schema = (branch-name, branch-city, assets) Loan-info-schema = (branch-name, customer-name, loan-number, amount) There is one attribute in common between these two schemas: Branch-loan-schema Customer-loan-schema = {branch-name} Thus, the only way that we can represent a relationship between, for example, customer-name and assets is through branch-name The difference between this example and the preceding one is that the assets of a branch are the same, regardless of the customer to which we are referring, whereas the lending branch associated with a certain loan amount does depend on the customer to which we are referring For a given branch-name, there is exactly one assets value and exactly one branch-city;



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CHAPTER 6:

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(constrains proportions to 1:1), and then drag while watching the size fields on the Property Bar If you re close but not precisely 5", type 50 into either field with the Lock Proportions icon clicked (see following illustration), and then press ENTER

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whereas a similar statement cannot be made for customer-name That is, the functional dependency branch-name assets branch-city holds, but customer-name does not functionally determine loan-number The notion of lossless joins is central to much of relational-database design Therefore, we restate the preceding examples more concisely and more formally Let R be a relation schema A set of relation schemas {R1 , R2 , , Rn } is a decomposition of R if R = R1 R2 Rn That is, {R1 , R2 , , Rn } is a decomposition of R if, for i = 1, 2, , n, each Ri is a subset of R, and every attribute in R appears in at least one Ri Let r be a relation on schema R, and let ri = Ri (r) for i = 1, 2, , n That is, {r1 , r2 , , rn } is the database that results from decomposing R into {R1 , R2 , , Rn } It is always the case that r r1 1 r 2 1 1 r n To see that this assertion is true, consider a tuple t in relation r When we compute the relations r1 , r2 , , rn , the tuple t gives rise to one tuple ti in each ri , i = 1, 2, , n These n tuples combine to regenerate t when we compute r1 1 r2 1 1 rn The details are left for you to complete as an exercise Therefore, every tuple in r appears in r1 1 r2 1 1 rn In general, r = r1 1 r2 1 1 rn As an illustration, consider our earlier example, in which n = 2 R = Lending-schema R1 = Branch-customer-schema R2 = Customer-loan-schema r = the relation shown in Figure 71 r1 = the relation shown in Figure 79 r2 = the relation shown in Figure 710 r1.

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