The UTL_MATCH
package facilitates matching two records. This is typically used to match names, such as two First Names or two Last Names.
This chapter contains the following topics:
Overview
Security Model
"Edit Distance" also known as "Levenshtein Distance "(named after the Russian scientist Vladimir Levenshtein, who devised the algorithm in 1965), is a measure of Similarity between two strings, s1 and s2. The distance is the number of insertions, deletions or substitutions required to transform s1 to s2.�
The Edit Distance between strings "shackleford" and "shackelford" = 2
The "Jaro-Winkler algorithm" is another way of calculating Edit distance between two strings. This method, developed at the U.S. Census, is a String Comparator measure that gives values of partial agreement between two strings. The string comparator accounts for length of strings and partially accounts for typical human errors made in alphanumeric strings.
Table 258-1 shows similarity values returned by Jaro-Winkler and Edit Distance
Table 258-2 DBMS_ALERT Package Subprograms
Subprogram | Description |
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Calculates the number of changes required to transform string-1 into string-2 |
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Calculates the number of changes required to transform string-1 into string-2, returning a value between 0 (no match) and 100 (perfect match) |
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Calculates the measure of agreement between string-1 and string-2 |
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Calculates the measure of agreement between string-1 and string-2, returning a value between 0 (no match) and 100 (perfect match) |
This function calculates the number of insertions, deletions or substitutions required to transform string-1 into string-2.
This function calculates the number of insertions, deletions or substations required to transform string-1 into string-2, and returns the Normalized value of the Edit Distance between two Strings. The value is typically between 0 (no match) and 100 (perfect match).�