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Exhaustive and Partial Indexes Indexes may be exhaustive, that is, have TIDs to every record in the le; or partial, that is, have only TIDs to records where the attribute value is signi cant Signi cance can mean simply that a value exists (is not unde ned or zero) or that the value has a good selectivity A partial index could occur in a personnel le which includes health data Only indexes to current employee health problems are maintained, although complete historical data is kept in the records for review purposes This was shown symbolically in Fig 3-13 as a Chronic diseases index; here NULL or Healthy data values do not lead to entries Another partial index could identify all individuals that smoke, to help in risk assessments If there is not even one exhaustive index, there is no easy way to list each record once Now a space allocation table is required to allow the le to be properly maintained Such a table gives an accounting of all the space allocated to the le If the le is read serially according to this table, a record ordering similar to the pile le may be perceived A Concordance One older use of computerized indexing by linguists is a concordance Here a single index is created to show all the words in a body of writing Such an index has as entries all the unique words (types) in the le, and is essentially a vocabulary With each entry there will be pointers to all the instances (tokens) in the text where the words appear In a printed concordance the pointers are augmented by a sample of the text An excerpt from a concordance is shown in Fig 3-14

In this section we discuss how to organize elds within a record While choosing a way to organize the elds of a record, we must take into account whether the elds of the record are of xed or variable length and consider the cost of various operations on the record, including retrieval and modi cation of elds Before discussing record formats, we note that in addition to storing individual records, information that is common to all records of a given record type (such as the number of elds and eld types) is stored in the system catalog, which can be thought of as a description of the contents of a database, maintained by the DBMS (Section 132) This avoids repeated storage of the same information with each record of a given type

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After this line executes, the variable s1 will point to a new string object that will contain the appropriate substring of the string that s1 used to point to. That original string will eventually be destroyed by the garbage collector because no variable now references it.

Sec 3-4

You reassign index to the last (remaining) space, which points you to the beginning of the word Three. You then extract the word Three into string s3. Continue like this until you ve populated s4 and s5. Finally, display the results:

Following our concepts a text le has only one type of attribute, words, and the index has multiple entries per record, one for each word Partial inversions may exclude high-frequency words or other words not of interest, such as initials of authors

A more effective solution to the problem illustrated in Example 15-6 would be to use the String class s Split( ) method, which parses a string into substrings. To use Split( ), pass in an array of delimiters (characters that indicate where to divide the words). The method returns an array of substrings (which Example 15-7 illustrates). The complete analysis follows the code.

Many le and database systems do not have the capability to manipulate TIDs In that case it becomes very important to select the most selective index for a query Even when we can combine indexes as shown above, it may not be worthwhile to merge indexes with a relatively low selectivity The attribute bedrooms in Ex 3-4 is of dubious value We discussed earlier that attributes with absolute poor selectivity should not even be indexed Estimation of selectivity of an attribute is often di cult Most systems make the assumption of a uniform distribution Unfortunately, uniform distributions are rare in practice unless the partitioning of the attribute values has been planned that way Most common in practice are Zip an distributions, as presented in Chap 143-2 Other distributions, as normal and Erlang, are covered in Chap 6-1 All these are distinguished by having frequent occurrences of some values, and fewer occurrences of other values

using using using using System; System.Collections.Generic; System.Linq; System.Text;

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