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You can have a switch as part of the statement sequence of an outer switch Even if the case constants of the inner and outer switch contain common values, no conflicts arise For example, the following code fragment is perfectly acceptable:

With = 01386 the sequence expresses Heising s 80-20 rule stated in Sec 6-1-6, and with = 00 the sequence expresses the type-token ratio for the words from the text by Schwartz63 used for Table 14-5 for the words ranked from 5 to 4000 Extremely high and low frequencies do not quite match Since (as discussed in Sec 14-2-3) a limited number of last names of individuals accounts for many of the names in les [SSA57 ], it is possible to replace these names by shorter representations These short names can be made up out of the 20 consonants, giving, for instance, 8000 three-consonant combinations for more than half the names in use Use of two letters and a digit in an arbitrary position of the representation would avoid the rare but possible confusion with an existing name and provide 20 280 combinations, beginning with 1AA and ending with ZZ9 The names with extremely high frequency can be represented by combinations of two characters, 1A to Z9 Lexicons to control replacement of names or words can be generated by analysis of the le or of a sample of the le If a xed- eld encoding is desirable, some of the codes can be used to refer to an over ow le for names which could not be encoded A format Zaa, for instance, could use the digits aa to point to records containing unencodable names

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A database contains business rules to support organizational policies Defining business rules is the essence of defining the semantics or meaning of a database For example, in an order entry system, an important rule is that an order must precede a shipment The data base can contain an integrity constraint to support this rule Defining business rules enables the database to actively support organizational policies This active role contrasts with the more passive role that databases have in establishing a common vocabulary In establishing the meaning of data, a database designer must choose appropriate con straint levels Selecting appropriate constraint levels may require compromise to balance the needs of different groups Constraints that are too strict may force work-around solu tions to handle exceptions In contrast, constraints that are too loose may allow incorrect data in a database For example, in a university database, a designer must decide if a course offering can be stored without knowing the instructor Some user groups may want the instructor to be entered initially to ensure that course commitments can be met Other user groups may want more flexibility because course catalogs are typically printed well in advance of the beginning of the academic period Forcing the instructor to be entered at the time a course offering is stored may be too strict If the database contains this constraint, users may be forced to circumvent it by using a default value such as TBA (to be announced) The appropriate constraint (forcing entry of the instructor or allowing later entry) depends on the importance of the needs of the user groups to the goals of the organization

Dynamic Replacement If adequate processor time at data-entry time is available, the selection of a lexicon to control replacement can be done dynamically The lexicon which is speci c to a record now has to be carried along with the record An example from DeMaine71 is shown in Fig 14-4

switch(x) { case 1: switch(y) { case 0: printf("Divide by zero error\n"); break; case 1: process(x,y); } break; case 2:

Sec 14-3

The importance of data quality is analogous to the importance of product quality in manufacturing Poor product quality can lead to loss of sales, lawsuits, and customer dis satisfaction Because data are the product of an information system, data quality is equally important Poor data quality can lead to poor decision making about communicating with customers, identifying repeat customers, tracking sales, and resolving customer problems For example, communicating with customers can be difficult if addresses are outdated or customer names are inconsistently spelled on different orders Data quality has many dimensions or characteristics, as depicted in Table 21 The im portance of data quality characteristics can depend on the part of the database in which they are applied For example, in the product part of a retail grocery database, important char acteristics of data quality may be the timeliness and consistency of prices For other parts of the database, other characteristics may be more important

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