asp.net pdf viewer annotation

asp.net pdf viewer annotation

asp.net pdf viewer annotation

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"IPX" "IP" "IPX"

3

Observe that, in our example, we took the union of two sets, both of which consisted of customer-name values In general, we must ensure that unions are taken between compatible relations For example, it would not make sense to take the union of the loan relation and the borrower relation The former is a relation of three attributes; the latter is a relation of two Furthermore, consider a union of a set of customer names and a set of cities Such a union would not make sense in most situations Therefore, for a union operation r s to be valid, we require that two conditions hold: 1 The relations r and s must be of the same arity That is, they must have the same number of attributes 2 The domains of the ith attribute of r and the ith attribute of s must be the same, for all i Note that r and s can be, in general, temporary relations that are the result of relationalalgebra expressions

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"Novell_ Client1" "[132207129164]" "Novell_

.

The decomposition of Lending-schema does not suffer from the problem of repetition of information that we discussed in Section 72 In Lending-schema, it was necessary to repeat the city and assets of a branch for each loan The decomposition separates branch and loan data into distinct relations, thereby eliminating this redundancy Similarly, observe that, if a single loan is made to several customers, we must repeat the amount of the loan once for each customer (as well as the city and assets of the branch) in lending-schema In the decomposition, the relation on schema Borrowerschema contains the loan-number, customer-name relationship, and no other schema does Therefore, we have one tuple for each customer for a loan in only the relation

The McGraw Hill Companies, 2001

13:32:50" "11/20/1999 13:30:11" "11/20/1999 13:32:26"

on Borrower-schema In the other relations involving loan-number (those on schemas Loan-schema and Borrower-schema), only one tuple per loan needs to appear Clearly, the lack of redundancy in our decomposition is desirable The degree to which we can achieve this lack of redundancy is represented by several normal forms, which we shall discuss in the remainder of this chapter

the columns Restrict uses a condition or logical expression to indicate what rows should be retained in the output Project uses a list of column names to indicate what columns to retain in the output Restrict and project are often used together because tables can have many rows and columns It is rare that a user wants to see all rows and columns The logical expression used in the restrict operator can include comparisons involving columns and constants Complex logical expressions can be formed using the logical oper ators AND, OR, and NOT For example, Table 38 shows the result of a restrict operation on Table 34 where the logical expression is: OffDays = ' M W AND OffTerm = 'SPRING' AND OffYear = 2006 A project operation can have a side effect Sometimes after a subset of columns is retrieved, there are duplicate rows When this occurs, the project operator removes the duplicate rows For example, if Offering CourseNo is the only column used in a project operation, only three rows are in the result (Table 39) even though the Offering table (Table 34) has nine rows The column Offering CourseNo contains only three unique values in Table 34 Note that if the primary key or a candidate key is included in the list of columns, the resulting table has no duplicates For example, if OfferNo was included in the list of columns, the result table would have nine rows with no duplicate removal necessary This side effect is due to the mathematical nature of relational algebra In relational al gebra, tables are considered sets Because sets do not have duplicates, duplicate removal is a possible side effect of the project operator Commercial languages such as SQL usually take a more pragmatic view Because duplicate removal can be computationally expensive, duplicates are not removed unless the user specifically requests it.

Using functional dependencies, we can de ne several normal forms that represent good database designs In this section we cover BCNF (de ned below), and later, in Section 77, we cover 3NF

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