275 UTL_MATCH

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:

275.1 UTL_MATCH Overview

UTL_MATCH can use either the Edit Distance algorithm or Jaro-Winkler algorithm when determining matches.

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.

The following table shows similarity values returned by Jaro-Winkler and Edit Distance

Table 275-1 Comparison between normalized values returned by Jaro-Winkler and Edit Distance algorithms

String 1 String 2 Jaro Winkler Edit Distance

Dunningham

Cunnigham

89

80

Abroms

Abrams

92

83

Lampley

Campley

90

86

Marhta

Martha

96

67

Jonathon

Jonathan

95

88

Jeraldine

Geraldine

92

89

275.2 UTL_MATCH Security Model

The UTL_MATCH package runs with definer's rights. UTL_MATCH must be created under SYS. Operations provided by this package are performed with SYS privileges.

275.3 Summary of UTL_MATCH Subprograms

This table lists the UTL_MATCH subprograms and briefly describes them.

Table 275-2 UTL_MATCH Package Subprograms

Subprogram Description

EDIT_DISTANCE Function

Calculates the number of changes required to transform string-1 into string-2

EDIT_DISTANCE_SIMILARITY Function

Calculates the number of changes required to transform string-1 into string-2, returning a value between 0 (no match) and 100 (perfect match)

JARO_WINKLER Function

Calculates the measure of agreement between string-1 and string-2

JARO_WINKLER_SIMILARITY Function

Calculates the measure of agreement between string-1 and string-2, returning a value between 0 (no match) and 100 (perfect match)

275.3.1 EDIT_DISTANCE Function

This function calculates the number of insertions, deletions or substitutions required to transform string-1 into string-2.

Syntax

UTL_MATCH.EDIT_DISTANCE (
   s1  IN  VARCHAR2, 
   s2  IN  VARCHAR2) 
 RETURN PLS_INTEGER;

Parameters

Table 275-3 EDIT_DISTANCE Function Parameters

Parameter Description

s1

The string to be transformed

s2

The string into which s1 is to be transformed

Examples

SELECT UTL_MATCH.EDIT_DISTANCE('shackleford', 'shackelford') FROM DUAL;  
-------------
returns 2 

275.3.2 EDIT_DISTANCE_SIMILARITY Function

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).

Syntax

UTL_MATCH.EDIT_DISTANCE_SIMILARITY (
   s1  IN  VARCHAR2, 
   s2  IN  VARCHAR2) 
 RETURN PLS_INTEGER;

Parameters

Table 275-4 EDIT_DISTANCE_SIMILARITY Function Parameters

Parameter Description

s1

The string to be transformed

s2

The string into which s1 is to be transformed

Examples

SELECT UTL_MATCH.EDIT_DISTANCE_SIMILARITY('shackleford', 'shackelford') FROM DUAL;
--------------
returns 82

275.3.3 JARO_WINKLER Function

This function calculates the measure of agreement between two strings.

Syntax

UTL_MATCH.JARO_WINKLER (
   s1  IN  VARCHAR2, 
   s2  IN  VARCHAR2) 
 RETURN BINARY_DOUBLE;

Parameters

Table 275-5 JARO_WINKLER Function Parameters

Parameter Description

s1

Input

s2

input

Examples

SELECT UTL_MATCH.JARO_WINKLER('shackleford', 'shackelford') FROM DUAL;
--------------
returns 9.818E-001

275.3.4 JARO_WINKLER_SIMILARITY Function

This function calculates the measure of agreement between two strings, and returns a score between 0 (no match) and 100 (perfect match).

Syntax

UTL_MATCH.JARO_WINKLER_SIMILARITY (
   s1  IN  VARCHAR2, 
   s2  IN  VARCHAR2) 
 RETURN PLS_INTEGER;

Parameters

Table 275-6 JARO_WINKLER_SIMILARITY Function Parameters

Parameter Description

s1

Input

s2

input

Examples

SELECT UTL_MATCH.JARO_WINKLER_SIMILARITY('shackleford', 'shackelford') FROM DUAL;
--------------
returns 98