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Returns the index of the first character within the invoking string that does not match any character in the first len characters of str The search begins at index indx npos is returned if no mismatch is found Returns the index of the first character within the invoking string that does not match ch The search begins at index indx npos is returned if no mismatch is found Returns the index of the last character within the invoking string that matches any character in str The search ends at index indx npos is returned if no match is found Returns the index of the last character within the invoking string that matches any character in str The search ends at index indx npos is returned if no match is found Returns the index of the last character within the invoking string that matches any character in the first len characters of str The search ends at index indx npos is returned if no match is found Returns the index of the last occurrence of ch within the invoking string The search ends at index indx npos is returned if no match is found

Let us estimate the selectivity given the record of Example 3-2a The speci cation Tower applied to a le containing all buildings in this world, or at least all towers, would restrict our search to the 107 towers in this world (assumption: one tower per 300 people and 3 109 people in this world) The name Hoover may have a selectivity ratio of 25 10 6 (fraction of Hoovers in the world, based on 40 entries in the San Francisco telephone book: 40/8 105 , the fraction of the English-speaking population: 1/10, and the assumption that half the towers are named after people), so that the second search attribute would yield 4 possible records (Assumption: As many towers are named after Hoovers as after other family names) A third speci cation (the street name) should be su cient to identify a single speci c tower, or establish the nonexistence of a tower satisfying the request Counting the Attributes Throughout the remainder of this book, we will use the symbol a to denote the total number of attribute types in the le under consideration, and the symbol a to denote the average number of attributes occurring in a record If the record in Example 3-2a is representative for the le, then a for that le would be 4 We typically disregard subsidiary attributes as shown in Example 3-2b since they cannot be searched independently We do not need to know the value of the total attribute count, a, of an entire le in the pile organization

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As another example, consider the natural join of Student (Table 313) and Enrollment (Table 314) shown in Table 315 In each row of the result, StudentStdSSN matches EnrollmentStdSSN Only one of the join columns is included in the result Arbitrarily, StudentStdSSN is shown although EnrollmentStdSSNcould be included without changing the result

3-1-2 Use of Piles Pile les are found where data is collected prior to processing, where data is not easy to organize, and in some research on le structures They also provide a basis for performance comparison within this text Data banks that have been established for broad intelligence gathering sometimes have this form, since the potential usage of a record is di cult to assess In this type of application many attribute types which defy a priori compartmentalization may exist Many manual data collections, such as medical records, also have the form of a pile In pile les, data analysis can become very costly because of the time required for retrieval of a statistically adequate number of sample records Since much of the data collected in real-world situations is in the form of piles we can consider this le organization as the base for other evaluations If we consider piles to be essentially free, we can estimate processing e orts needed to create more e cient le organizations for retrieval by analysis of the transformation from a pile 3-1-3 Performance of Piles We now estimate the seven performance parameter values for a pile organization as described in Sec 3-1-1 We have already some intuition which parameters will be small and which will be large

static void assign(char_type &ch1, const char_type &ch2); static char_type *assign(char_type *str, size_t num, char_type ch2); static int compare(const char_type *str1, const char_type *str2, size_t num);

File density in a pile is a ected by two factors: negatively, by the need to store the attribute names with the data, and positively, by the fact that nonexistent data need not be considered at all The e ect is a relatively high density when the material collected is heterogeneous or sparse, and a relatively low density when the data is dense and the same attribute names occur redundantly in successive records We will denote the average length of the description of an attribute as A, and the average length of the value portion as V For Examples 3-2a and b the values of A are 5 and of V are 9 bytes Since name and data are of variable length, two separator characters (= and , or ; in the examples above) are stored to mark each data element We have an average of a elds Using these de nitions the expected average record length will be R = a (A + V + 2) 3-1

Appropriate values for a , A, and V will have to be based on an adequately sized sample Techniques to reduce the values of A and V by encoding are discussed in Chap 14

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