You
can compare string using equality operator (=), or using similarity
operator (LIKE). But there are cases where it is not enough. You Can
use
UTL_MATCH package to calculate string
similarity index. This packages offers 4 functions that take two
strings as parameters:
- edit_distance
- algorithm by Levenshtein
- returns number of edits that must be done to change one
string into second,
- edit_distance_similarity
- normalized results of edit_distance in percents -
integer values,
- jaro_winkler
- returns similarity based on Jaro-Winkler
distance algorithm,
- jaro_winkler_similarity
- same as above but presented as integer in range 0-100.
Lets
teke a look how it works. First we can create sample table and insert
some data:
create table countries(name varchar2(15) not null);
insert into countries values ('Poland');
insert into countries values ('Germany');
insert into countries values ('United States');
insert into countries values ('Portugal');
insert into countries values ('Czech Republic');
insert into countries values ('China');
insert into countries values ('Slovakia');
insert into countries values ('Slovenia');
commit;
Now
we can take a look at results. Lets compare to miss-spelled country
name: '
Slovnia'
select name
,to_char(utl_match.edit_distance(name, 'Slovnia'),'999') edit_dist
,to_char(utl_match.edit_distance_similarity(name, 'Slovnia'),'999') edit_dist_sim
,to_char(utl_match.jaro_winkler(name, 'Slovnia'),'999d9999') jaro_winkler
,to_char(utl_match.jaro_winkler_similarity(name, 'Slovnia'),'999') jaro_winkler_sim
from countries
order by jaro_winkler_sim desc;
NAME EDIT EDIT JARO_WINK JARO
-------------------- ---- ---- --------- ----
Slovenia 1 88 .9750 97
Slovakia 2 75 .8881 88
China 5 29 .5619 56
United States 12 8 .5531 55
Poland 6 15 .5317 53
Portugal 7 13 .5119 51
Germany 7 0 .3571 35
Czech Republic 13 8 .0000 0
Above
we
can observe differences in algorithms.
Lets test it on NULLs:
SQL> select to_char(utl_match.edit_distance('test', NULL),'999')
2 edit_dist
3 ,to_char(utl_match.edit_distance_similarity('test', NULL),'999')
4 edit_dist_sim
5 ,to_char(utl_match.jaro_winkler('test', NULL),'999d9999')
6 jaro_winkler
7 ,to_char(utl_match.jaro_winkler_similarity('test', NULL),'999')
8 jaro_winkler_sim
9 from dual;
EDIT EDIT JARO_WINK JARO
---- ---- --------- ----
-1 125 .0000 0We
can see that using
edit_distance on NULLs migth be
dengerous.
All samples were run on Oracle 10g XE
Hope You find it usefull.
Paweł