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records that follow the deleted record must be moved up to compact the free space2 The cost is the same as for an insert, that is, search cost plus B(D + RC) Given the rid of the record to delete, we can fetch the page containing the record directly If records to be deleted are speci ed by an equality or range condition, the cost of deletion depends on the number of qualifying records If the condition is speci ed on the sort eld, qualifying records are guaranteed to be contiguous due to the sorting, and the rst qualifying record can be located using binary search

Interpretation The bene ts of the interpretive methods are related, of course, to the disadvantages of compiling We can emphasize the following: 1 Data-oriented changes need to be introduced in the schema only, and do not need programming e ort 2 Better control exists over the contents of, and access to, the database 3 Programming can be carried out before the exact data speci cations are known 4 Synchronization of accesses to a shared le is easier to maintain in a multiprogrammed environment because more process information is available 5 Greater independence between processes and les exists This feature will protect the system from hardware changes 6 It may be possible to implement frequently used and well-established functions in hardware or in micro-coded procedures 6 The source-language statements may require less storage space than the machine instructions needed for le access Disadvantages of the interpretive method include the following: 1 The general interpreter has less exibility when special considerations have to be taken care of Recompiling of programs is not costly compared with the le reorganization e ort involved when the schema is changed 2 There is a high central processor cost to interpreting Although, in terms of the total time to complete a job, this cost may be minimal because of the lengthy le accesses, less CPU time will be available to other processes 3 Good general programs are di cult to construct and debug 4 The single generalized interpreting program may occupy more space than the separate processing programs which are brought in on demand only In a paging system, the demands on primary memory space for either approach may be substantially equal

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// testing for strings the same as String 2, ignoring case Console.WriteLine("\nWhich strings are the same as s2 [{0}] ignoring case ", s2); Console.WriteLine("s1: {0} [{1}]", String.Compare(s1, s2, true) == 0 "Same!" : "Different", s1);

Example A-42. Our solution to Exercise 15-1 (continued)

Compromises include compiled references to programs which can adapt to changes in the database schema The actual choice of approach will be determined largely by available facilities and expertise, although the wrong choice can be costly

Console.WriteLine("s2: {0} [{1}]", String.Compare(s2, s2, true) == Console.WriteLine("s3: {0} [{1}]", String.Compare(s3, s2, true) == Console.WriteLine("s4: {0} [{1}]", String.Compare(s4, s2, true) == Console.WriteLine("s5: {0} [{1}]", String.Compare(s5, s2, true) == Console.WriteLine("s6: {0} [{1}]", String.Compare(s6, s2, true) == } static void Main( ) { Tester t = new Tester( ); t.Run( ); } } } 0 "Same!" : "Different", s2); 0 "Same!" : "Different", s3); 0 "Same!" : "Different", s4); 0 "Same!" : "Different", s5); 0 "Same!" : "Different", s6);

8-4-3 Internal Representation of a Schema The internal representation of a complex schema may itself require a linked structure to describe the structural dependencies of the data elements In SOCRATE this is achieved through a number of interlinked tables as shown in Fig 8-15, which is based on the schema in Example 8-6 The dictionary table includes both attribute names and the data values permitted in domain controlled by lists of choices The next-pointers provide rings within a nest and the detail-pointers provide access to nests when required

Solution to Exercise 15-2. Take the following famous string:

In a large database, the schema itself may be of massive proportions Since not all the information in the schema is required by every user or program, the data in the schema may be categorized and selected according to several dimensions These dimensions include: Functional level of the schema user Responsibility and ownership of data Processing function Host-language adaptations Location of stored database fragments All these dimensions can be used to divide schemas into subschemas which restrict the user or the database to a subset of data and function, depending on responsibility, need-to-know, and location The diversity and complexities of schemas are in part caused by the di erences in objectives seen by schema designers

To be, or not to be: That is the question: Whether tis nobler in the mind to suffer the slings and arrows of outrageous fortune, or to take arms against a sea of troubles, and by opposing end them

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