June 9, 2009 at 11:59 pm
Comments posted to this topic are about the item Roll Your Own Fuzzy Match / Grouping (Jaro Winkler) - T-SQL
[font="Comic Sans MS"]Ira Warren Whiteside[/font]
June 10, 2009 at 7:21 am
Ira, interesting article. Timely, too, as I've been working on a project to create an index of people listed in multiple data sources, locating the same people in each database by means of matching on various fields.
I'm currently using a similar approach, but using a Levenshtien distance algoritm instead of Jaro-Winkler. Would Jaro-Winkler be more efficient?
Thanks,
Ed Z
June 10, 2009 at 7:31 am
Yes, Jaro-Winkler will give more weight to the length of the string match from the beginning of the string. It should be more accurate the Levi, however the real challenge is to implement a blocking/partioning strategy to limits the num of records sent to Jaro.
[font="Comic Sans MS"]Ira Warren Whiteside[/font]
June 10, 2009 at 7:49 am
Thanks, I'll give Jaro-Winkler a try.
I've already a strategy in place to filter the number of rows through to the fuzzy match query, including pre-weeding out exact matches and matches that don't start with the same first letter as the match target. (In my experience here, we tend to at least get the first letter of the name right!) Those filters alone are enough to bring 2+ minute queries down to sub-second queries, which is good enough for me.
Now if I could just get my nickname-based matching working as fast, I'd be a truly happy camper. 🙂
June 10, 2009 at 8:04 am
Good stuff Ira. I had been using the built in Fuzzy Matching Package, but there's nothing like being able to use a regular query.
A small thing - a slightly more accurate translation of the Hindi saying at the bottom is - Whether you do or do not do [somethin], there is nothing like trying.
Cheers 🙂
June 10, 2009 at 8:46 am
There are a few misspellings in the posted content, in the jaro procedure the string cleansing function called is 'Clean_sting' yet below the function created is 'Cleansing'
June 10, 2009 at 8:47 am
[Edited after an accidental early post]
First and foremost, thank you very much for writing Jaro-Winkler for T-SQL; that was an algorithm in a language I was looking for just a few weeks ago for name and address matching.
Second, thank you for taking the time to post a comprehensive article, including test cases, on a very important topic.
Third, I have a few suggestions for what I would consider improvements:
1) Descriptive variable names. @max_len, for instance, is much better than @m, @z, and so on.
2) Typo?
2a) You call [dbo].[clean_sting] and give source code to a function [dbo].[Cleansing]
2b) In [dbo].[Cleansing], there appears to be an extraneous ') just before the RETURN
3) Efficiency improvements
3a) In the cleansing function, instead of
Declare @ret_value varchar(max)
set @ret_value = @p_str1
set @ret_value = REPLACE(@ret_value, '.', '')
set @ret_value = REPLACE(@ret_value, ',', '')
set @ret_value = REPLACE(@ret_value, '-', '')
set @ret_value = REPLACE(@ret_value, ';', '')
set @ret_value = REPLACE(@ret_value, ':', '') ')
RETURN @ret_value
I would suggest
Declare @ret_value varchar(max)
set @ret_value = @p_str1
set @ret_value = REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(@ret_value, '.', ''), ',', ''), '-', '') , ';', ''), ':', '')
RETURN @ret_value
On a sample size of 98,304 rows (and a pretty pathetic SQL Server), CPU on SQL Server profiler dropped from 4422 to 3906 with this change (i.e. this uses only 88% the CPU time of the original).
4) Suggested higher cost, higher sanitization REPLACE() for dirtier strings in general when one wants to compare alphanumerics, presented in bite size pieces (combine the REPLACE() sets for higher efficiency:
-- HTML character entity references
-- An authoritative list for HTML 4.0 is at http://www.w3.org/TR/REC-html40/sgml/entities.html
SET @String = REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(@String, '&',' '), '&',' '),'',' '), ' ',' '),'"',' '), '"',' ')
-- (U.S. centric) Punctuation merely gets in the way of matching; replace with spaces to keep our word separation; we won't do a fully complete replacement, but we'll do what we do relatively quickly.
-- EXCEPT FOR THE # SIGN (which merely gets turned to ' # ', and is used for further address splitting, and is cleaned at the end of that), the bottom three rows of punctuation on the right hand side of a U.S. keyboard, with and without shift by row, then the top row's symbols the same way.
-- Further address splitting: pull the number off the front, then pull the Suite/Apartment/#/Unit/etc. off the end, leaving the middle by itself
SET @String = REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(@String, '.', ' '), ',', ' '), '/', ' '), '', ' '), '?', ' '), ';',' '), '''',' '), ':',' '), '"',' '), '[',' '), ']',' '), '\',' '), '{',' '), '}',' '), '|',' '), '`', ' '), '-', ' '), '=', ' '), '~', ' '), '!', ' '), '@', ' '), '#', ' # '), '$',' '), '%',' '), '^',' '), '&',' '), '*',' '), '(',' '), ')',' '), '_',' '), '+',' ')
-- (U.S. centric) Special characters merely gets in the way of matching, and some may still be in use in old systems and data, particularly mainframes and mainframe derived code. Here's a list of codes, from more common, to quite rare
-- NOTE: Be careful with UNICODE, though these are all from the first 7 bits of ASCII encoding
SET @String = REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(@String, CHAR(9),' '), CHAR(11),' '), CHAR(13),' '), CHAR(10),' '), CHAR(0),' '), CHAR(27),' '), CHAR(12),' '), CHAR(1),' '), CHAR(2),' '), CHAR(3),' '), CHAR(4),' '), CHAR(8),' '), CHAR(23),' '), CHAR(24),' '), CHAR(25),' '), CHAR(26),' '), CHAR(28),' '), CHAR(29),' '), CHAR(30),' '), CHAR(31),' ')
5) Suggested higher cost, higher sanitization address cleaning):
-- Get rid of any combination of leading 0's and spaces in a moderately performant way:
SET @StringToStripLeadingZerosSpacesFrom = COALESCE(SUBSTRING(@StringToStripLeadingZerosSpacesFrom,PATINDEX('%[^0 ]%',@StringToStripLeadingZerosSpacesFrom),DATALENGTH(@StringToStripLeadingZerosSpacesFrom)),'')
-- Directions can be changed to standard abbreviations
-- This must be done before punctuations is replaced by spaces.
SET @String = REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(@String, ' north-east ',' ne '),' northeast ',' ne '), ' north-west ',' nw '),' northwest ',' nw '), ' south-east ',' se '),' southeast ',' se '), ' south-west ',' sw '),' southwest ',' sw '), ' north ',' n '), ' east ',' e '), ' south ',' s '), ' west ',' w ')
6) Suggested higher cost, higher sanitization name cleaning for highly fuzzy matches, again, consolidate for higher efficiency:
-- Dental/Medical centric abbreviation cleaning
-- A select few sourced from http://www.tabers.com/tabersonline/ub/view/Tabers/144251/0/Professional_Designations_and_Titles_in_the_Health_Sciences)
-- Precondition: Punctuation replaced by spaces, and then all consecutive spaces consolidated into single spaces
SET @String = REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(@String, ' DDS ',' '),' D D S ',' '), ' DMD ',' '),' D M D ',' '), ' PC ',' '),' P C ',' '), ' MS ',' '),' M S ',' '),' PA ',' '),' P A ',' '),' FACD ',' '),' F A C D ',' '),' RDH ',' '),' R D H ',' '),' RDA ',' '),' R D A ',' '),' CDA ',' '),' C D A ',' '),' APC ',' '),' A P C ',' '),' MSD ',' '),' M S D ',' '),' INC ',' '),' I N C ',' '),' MD ',' '),' M D ',' '),' LTD ',' '),' L T D ',' ')
-- Now we get rid of common suffixes
-- This definitely is going to generate false positive matches on parents and children!
-- Use at a fairly fuzzy level of matching, after more precise matches have been handled
SET @String = REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(@String, ' I ',' '),' II ',' '), ' III ',' '),' IV ',' '), ' V ',' '),' JR ',' '), ' SR ',' ')
7) As always, for bulk use, generate and save the "clean" string separately in its own columns using REPLACE() on sets.
Fourth, nothing to do with this article, but I would suggest adding Double Metaphone to your list of fuzzy logic functions. It's computationally expensive, but can be done in bulk, and is really quite reasonable when it comes to matching strings that sound alike. I found source at http://www.sqlservercentral.com/scripts/Miscellaneous/30219/ and updated it to generate 35 character strings instead of 5 character strings for less fuzzy matching on longer names.
June 10, 2009 at 9:24 am
Thank you, the translation came from some friends in New Delhi, what would be your translation for "Do, or do not. There is no try".
[font="Comic Sans MS"]Ira Warren Whiteside[/font]
June 10, 2009 at 9:25 am
Thanks for the suggestions, I try them out.
[font="Comic Sans MS"]Ira Warren Whiteside[/font]
June 10, 2009 at 9:40 am
Hi Ira,
The Hindi saying: "karo yaa na karo, koshish jaisa kuch nahi hai" literally translates to "Do or not do, there is nothing like try". However, the implied translation is "[Whether you] do or not do [something], there is nothing like try[ing]" with the implied portions in square brackets. That is because of the structure of the Hindi language.
Cheers. 🙂
June 10, 2009 at 9:51 am
"karo yaa na karo, koshish jaisa kuch nahi hai"
"Do, or do not. There is no try."
Nice, a quote from a Yogi and a quote from a Yoda.
Great article. Thanks for the code samples.
Now if we can just adjust western culture to shorten William to Will rather than Bill we'll all be better off.
Who decided that Robert should become BOB?!
-D
June 10, 2009 at 11:13 am
We did this very same "string distance" matching algorithms 3-4 years ago at a place I worked. It works great for small datasets, but SSIS fuzzy matching is much faster and easier to use for large datasets.
_______________________________
[font="Tahoma"]Jody Claggett
SQL Server Reporting Analyst[/font][/size]
June 10, 2009 at 11:48 am
And, if anyone cares, here is some U.S. centric address sanitization to add to the clean_string function or set of functions. Given that addresses vary significantly country to country, please review carefully before using in your country.
At the end is also primitive SQL to turn consecutive spaces into single spaces, which can be trivially separated from the PO Box replacements.
Unfortunately, this trivial but tedious method does result in very long SQL, and substantial CPU time for the cleansing function.
-- 238 nestings, ALLEY to LOOP plus " BY PASS " and " BL ", and " BLUFFS ", " BROOKS ", " BURGS ", " CENTERS ", " CLIFFS ", " CIRCLES ", " CORNERS ", " COURTS ", " COVES ", " DRIVES ", " ESTATES ", " EXTENSIONS ", " FALLS ", " FIELDS ", " FLATS ", " FORDS ", " FORGES ", " FORKS ", " GARDENS ", " GLENS ", " GREENS ", " GROVES ", " HARBORS ", " HILLS ", " ISLANDS ", " JUNCTIONS ", " KEYS ", " KNOLLS ", " LAKES ", " LIGHTS ", " LOCKS ", and so on to the singular abbreviation from the official plural abbreviations
-- spaces remain at this point to allow for word separations, created in bulk via Excel and global search and replace, meaningless self-replacements removed.
SET @String = REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(@String,' ALLEE ',' ALY '),' ALLEY ',' ALY '),' ALLY ',' ALY '),' ANEX ',' ANX '),' ANNEX ',' ANX '),' ANNX ',' ANX '),' ARCADE ',' ARC '),' AV ',' AVE '),' AVEN ',' AVE '),' AVENU ',' AVE '),' AVENUE ',' AVE '),' AVN ',' AVE '),' AVNUE ',' AVE '),' BAYOO ',' BYU '),' BAYOU ',' BYU '),' BEACH ',' BCH '),' BEND ',' BND '),' BLUF ',' BLF '),' BLUFF ',' BLF '),' BLUFFS ',' BLF '),' BLFS ',' BLF '),' BOT ',' BTM '),' BOTTM ',' BTM '),' BOTTOM ',' BTM '),' BL ',' BLVD '),' BOUL ',' BLVD '),' BOULEVARD ',' BLVD '),' BOULV ',' BLVD '),' BRANCH ',' BR '),' BRNCH ',' BR '),' BRDGE ',' BRG '),' BRIDGE ',' BRG '),' BROOK ',' BRK '),' BROOKS ',' BRK '),' BRKS ',' BRK '),' BURG ',' BG '),' BURGS ',' BG '),' BGS ',' BG '),' BYPA ',' BYP '),' BYPAS ',' BYP '),' BYPASS ',' BYP '),' BY PASS ',' BYP '),' BYPS ',' BYP '),' CAMP ',' CP '),' CMP ',' CP '),' CANYN ',' CYN '),' CANYON ',' CYN '),' CNYN ',' CYN '),' CAPE ',' CPE '),' CAUSEWAY ',' CSWY '),' CAUSWAY ',' CSWY ') ,' CEN ',' CTR '),' CENT ',' CTR '),' CENTER ',' CTR '),' CENTR ',' CTR '),' CENTRE ',' CTR '),' CNTER ',' CTR '),' CNTR ',' CTR '),' CENTERS ',' CTR '),' CTRS ',' CTR '),' CIRC ',' CIR '),' CIRCL ',' CIR '),' CIRCLE ',' CIR '),' CRCL ',' CIR '),' CRCLE ',' CIR '),' CIRCLES ',' CIR '),' CIRS ',' CIR '),' CLIFF ',' CLF '),' CLFS ',' CLF '),' CLIFFS ',' CLF '),' CLUB ',' CLB '),' COMMON ',' CMN '),' CORNER ',' COR '),' CORNERS ',' COR '),' CORS ',' COR '),' COURSE ',' CRSE '),' COURT ',' CT '),' CRT ',' CT '),' COURTS ',' CT '),' CTS ',' CT '),' COVE ',' CV '),' CVS ',' CV '),' COVES ',' CV '),' CK ',' CRK '),' CR ',' CRK '),' CREEK ',' CRK '),' CRECENT ',' CRES '),' CRESCENT ',' CRES '),' CRESENT ',' CRES '),' CRSCNT ',' CRES ') ,' CRSENT ',' CRES '),' CRSNT ',' CRES '),' CREST ',' CRST '),' CROSSING ',' XING '),' CRSSING ',' XING '),' CRSSNG ',' XING '),' CROSSROAD ',' XRD '),' CURVE ',' CURV '),' DALE ',' DL '),' DAM ',' DM '),' DIV ',' DV '),' DIVIDE ',' DV '),' DVD ',' DV '),' DRIV ',' DR '),' DRIVE ',' DR '),' DRV ',' DR '),' DRIVES ',' DR '),' DRS ',' DR '),' ESTATE ',' EST '),' ESTATES ',' EST '),' ESTS ',' EST '),' EXP ',' EXPY '),' EXPR ',' EXPY '),' EXPRESS ',' EXPY '),' EXPRESSWAY ',' EXPY '),' EXPW ',' EXPY '),' EXTENSION ',' EXT '),' EXTN ',' EXT '),' EXTNSN ',' EXT '),' EXTENSIONS ',' EXT '),' EXTS ',' EXT '),' FALLS ',' FALL '),' FLS ',' FALL '),' FERRY ',' FRY '),' FRRY ',' FRY '),' FIELD ',' FLD '),' FIELDS ',' FLD '),' FLDS ',' FLD '),' FLAT ',' FLT '),' FLATS ',' FLT '),' FLTS ',' FLT '),' FORD ',' FRD '),' FRDS ',' FRD '),' FORDS ',' FRD '),' FOREST ',' FRST '),' FORESTS ',' FRST '),' FORG ',' FRG '),' FORGE ',' FRG '),' FRGS ',' FRG '),' FORGES ',' FRG '),' FORK ',' FRK '),' FORKS ',' FRK '),' FRKS ',' FRK '),' FORT ',' FT '),' FRT ',' FT '),' FREEWAY ',' FWY '),' FREEWY ',' FWY '),' FRWAY ',' FWY '),' FRWY ',' FWY '),' GARDEN ',' GDN '),' GARDN ',' GDN '),' GRDEN ',' GDN '),' GRDN ',' GDN '),' GARDENS ',' GDN '),' GDNS ',' GDN '),' GRDNS ',' GDN '),' GATEWAY ',' GTWY '),' GATEWY ',' GTWY '),' GATWAY ',' GTWY '),' GTWAY ',' GTWY '),' GLEN ',' GLN '),' GLNS ',' GLN '),' GLENS ',' GLN '),' GREEN ',' GRN '),' GRNS ',' GRN '),' GREENS ',' GRN '),' GROV ',' GRV '),' GROVE ',' GRV '),' GRVS ',' GRV '),' GROVES ',' GRV '),' HARB ',' HBR '),' HARBOR ',' HBR '),' HARBR ',' HBR '),' HBRS ',' HBR ') ,' HRBOR ',' HBR ') ,' HARBORS ',' HBR '),' HAVEN ',' HVN '),' HAVN ',' HVN '),' HEIGHT ',' HTS '),' HEIGHTS ',' HTS '),' HGTS ',' HTS '),' HT ',' HTS '),' HIGHWAY ',' HWY '),' HIGHWY ',' HWY '),' HIWAY ',' HWY '),' HIWY ',' HWY '),' HWAY ',' HWY '),' HILL ',' HL '),' HILLS ',' HL '),' HLS ',' HL '),' HLLW ',' HOLW '),' HOLLOW ',' HOLW '),' HOLLOWS ',' HOLW '),' HOLWS ',' HOLW '),' INLET ',' INLT '),' ISLAND ',' IS '),' ISLND ',' IS '),' ISLANDS ',' IS '),' ISLNDS ',' IS '),' ISS ',' IS '),' ISLES ',' ISLE '),' JCTION ',' JCT '),' JCTN ',' JCT '),' JUNCTION ',' JCT '),' JUNCTN ',' JCT '),' JUNCTON ',' JCT '),' JCTNS ',' JCT '),' JCTS ',' JCT '),' JUNCTIONS ',' JCT '),' KEY ',' KY '),' KEYS ',' KY '),' KYS ',' KY '),' KNL ',' KNL '),' KNOL ',' KNL '),' KNOLL ',' KNL '),' KNLS ',' KNL '),' KNOLLS ',' KNL '),' LAKE ',' LK '),' LK ',' LK '),' LAKES ',' LK '),' LKS ',' LK '),' LAND ',' LAND '),' LANDING ',' LNDG '),' LNDNG ',' LNDG '),' LA ',' LN '),' LANE ',' LN '),' LANES ',' LN '),' LIGHT ',' LGT '),' LIGHTS ',' LGT '),' LGTS ',' LGT '),' LOAF ',' LF '),' LOCK ',' LCK '),' LCKS ',' LCK '),' LOCKS ',' LCK '),' LDGE ',' LDG '),' LODG ',' LDG '),' LODGE ',' LDG '),' LOOPS ',' LOOP ')
-- 201 nestings, MALL to WELLS plus " UNIT " then all secondary designators APARTMENT to UPPER, and " MANORS ", " MEADOWS ", " MILLS ", " MOUNTAINS ", " PINES ", " PLAINS ", " POINTS ", " PORTS ", " RAPIDS ", " RIDGES ", " ROADS ", " SHOALS ", " SHORES ", " SPRINGS ", " SQUARES ", " STREETS ", " UNIONS ", " VALLEYS ", " VIEWS ", " VILLAGES ", " WAYS ", " WELLS ", and so on to the singular abbreviation from the official plural abbreviations
-- spaces remain at this point to allow for word separations, created in bulk via Excel and global search and replace, meaningless self-replacements removed.
SET @String = REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(@String,' MANOR ',' MNR '),' MNR ',' MNR '),' MANORS ',' MNR '),' MNRS ',' MNR '),' MEADOW ',' MDW '),' MDWS ',' MDW '),' MEADOWS ',' MDW ') ,' MEDOWS ',' MDW '),' MEWS ',' MEWS '),' MILL ',' ML '),' MILLS ',' ML '),' MLS ',' ML ') ,' MISSION ',' MSN '),' MISSN ',' MSN '),' MSSN ',' MSN '),' MOTORWAY ',' MTWY '),' MNT ',' MT '),' MOUNT ',' MT '),' MNTAIN ',' MTN '),' MNTN ',' MTN '),' MOUNTAIN ',' MTN '),' MOUNTIN ',' MTN '),' MTIN ',' MTN '),' MTNS ',' MTN '),' MNTNS ',' MTN ') ,' MOUNTAINS ',' MTN '),' NECK ',' NCK '),' ORCHARD ',' ORCH '),' ORCHRD ',' ORCH '),' OVL ',' OVAL '),' OVERPASS ',' OPAS '),' PK ',' PARK '),' PRK ',' PARK '),' PARKS ',' PARK '),' PARKWAY ',' PKWY '),' PARKWY ',' PKWY '),' PKWAY ',' PKWY '),' PKY ',' PKWY '),' PARKWAYS ',' PKWY ') ,' PKWYS ',' PKWY '),' PASSAGE ',' PSGE '),' PATHS ',' PATH '),' PIKES ',' PIKE '),' PINE ',' PNE '),' PINES ',' PNE '),' PNES ',' PNE '),' PLACE ',' PL '),' PLAIN ',' PLN '),' PLAINES ',' PLN '),' PLAINS ',' PLN '),' PLNS ',' PLN '),' PLAZA ',' PLZ '),' PLZA ',' PLZ '),' POINT ',' PT '),' POINTS ',' PT '),' PTS ',' PT '),' PORT ',' PRT '),' PORTS ',' PRT '),' PRTS ',' PRT '),' PRAIRIE ',' PR '),' PRARIE ',' PR '),' PRR ',' PR '),' RAD ',' RADL '),' RADIAL ',' RADL '),' RADIEL ',' RADL '),' RANCH ',' RNCH '),' RANCHES ',' RNCH '),' RNCHS ',' RNCH '),' RAPID ',' RPD '),' RAPIDS ',' RPD '),' RPDS ',' RPD '),' REST ',' RST '),' RDGE ',' RDG '),' RIDGE ',' RDG '),' RDGS ',' RDG '),' RIDGES ',' RDG '),' RIVER ',' RIV '),' RIVR ',' RIV '),' RVR ',' RIV '),' ROAD ',' RD '),' RDS ',' RD '),' ROADS ',' RD '),' ROUTE ',' RTE '),' SHOAL ',' SHL '),' SHLS ',' SHL '),' SHOALS ',' SHL '),' SHOAR ',' SHR '),' SHORE ',' SHR '),' SHR ',' SHR '),' SHOARS ',' SHR '),' SHORES ',' SHR '),' SHRS ',' SHR '),' SKYWAY ',' SKWY '),' SPNG ',' SPG '),' SPRING ',' SPG '),' SPRNG ',' SPG '),' SPGS ',' SPG '),' SPNGS ',' SPG '),' SPRINGS ',' SPG '),' SPRNGS ',' SPG '),' SPURS ',' SPUR '),' SQR ',' SQ '),' SQRE ',' SQ '),' SQU ',' SQ '),' SQUARE ',' SQ '),' SQRS ',' SQ '),' SQUARES ',' SQ ') ,' SQS ',' SQ '),' STATION ',' STA '),' STATN ',' STA '),' STN ',' STA '),' STRAV ',' STRA '),' STRAVE ',' STRA '),' STRAVEN ',' STRA '),' STRAVENUE ',' STRA '),' STRAVN ',' STRA '),' STRVN ',' STRA '),' STRVNUE ',' STRA '),' STREAM ',' STRM '),' STREME ',' STRM '),' STR ',' ST '),' STREET ',' ST '),' STRT ',' ST '),' STREETS ',' ST '),' STS ',' ST '),' SUMIT ',' SMT '),' SUMITT ',' SMT '),' SUMMIT ',' SMT '),' TERR ',' TER '),' TERRACE ',' TER '),' THROUGHWAY ',' TRWY '),' TRACE ',' TRCE '),' TRACES ',' TRCE '),' TRACK ',' TRAK '),' TRACKS ',' TRAK '),' TRK ',' TRAK '),' TRKS ',' TRAK '),' TRAFFICWAY ',' TRFY '),' TR ',' TRL '),' TRAIL ',' TRL '),' TRAILS ',' TRL '),' TRLS ',' TRL '),' TUNEL ',' TUNL '),' TUNLS ',' TUNL '),' TUNNEL ',' TUNL '),' TUNNELS ',' TUNL '),' TUNNL ',' TUNL '),' TPK ',' TPKE '),' TRNPK ',' TPKE '),' TRPK ',' TPKE '),' TURNPIKE ',' TPKE '),' TURNPK ',' TPKE '),' UNDERPASS ',' UPAS '),' UNION ',' UN '),' UNIONS ',' UN '),' UNS ',' UN '),' VALLEY ',' VLY '),' VALLY ',' VLY '),' VLLY ',' VLY '),' VALLEYS ',' VLY ') ,' VLYS ',' VLY ') ,' VDCT ',' VIA '),' VIADCT ',' VIA '),' VIADUCT ',' VIA '),' VIEW ',' VW '),' VIEWS ',' VW '),' VWS ',' VW '),' VILL ',' VLG '),' VILLAG ',' VLG '),' VILLAGE ',' VLG '),' VILLG ',' VLG '),' VILLIAGE ',' VLG '),' VILLAGES ',' VLG '),' VLGS ',' VLG '),' VILLE ',' VL '),' VIST ',' VIS '),' VISTA ',' VIS '),' VST ',' VIS '),' VSTA ',' VIS '),' WALKS ',' WALK '),' WY ',' WAY '),' WAYS ',' WAY '),' WELL ',' WL '),' WELLS ',' WL '),' WLS ',' WL '),' APARTMENT ',' APT '),' BASEMENT ',' BSMT ') ,' BUILDING ',' BLDG '),' DEPARTMENT ',' DEPT '),' FLOOR ',' FL '),' FRONT ',' FRNT '),' HANGAR ',' HNGR '),' LOBBY ',' LBBY '),' LOWER ',' LOWR '),' OFFICE ',' OFC '),' PENTHOUSE ',' PH '),' ROOM ',' RM '),' SPACE ',' SPC '),' SUITE ',' STE '),' TRAILER ',' TRLR '),' UNT ',' UNIT '),' UPPER ',' UPPR ')
-- Turn any set of spaces into a single space. Why 13 2:1 replacements? Because 2^13 is 8192, just in excess of VARCHAR(8000), so we _know_ that any number of contiguous spaces is now one space.
-- Also, standardize the most common misspelled PO BOX types _after_ the spaces are collapsed.
-- single spaces remain at this point to allow for word separations
SET @String = REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(@String,' ',' '),' ',' '),' ',' '),' ',' '),' ',' '),' ',' '),' ',' '),' ',' '),' ',' '),' ',' '),' ',' '),' ',' '),' ',' '), ' po bx ',' PO BOX '), ' p o box ',' PO BOX '), ' P0 BOX ',' PO BOX ')
June 10, 2009 at 1:46 pm
Ira, I apologize, but it appears there is at least one error in the algorithm, most likely dealing with transpositions (or I accidentally changed something important when making what I thought were cosmetic changes).
Most importantly, I would not expect ('Yvette','Yevett'), ('Martha','Marhta'), and ('Shackleford','Shackelford') to have the 0.999999 scores they're getting.
Additionally, ('Massey','Massie') is also scored significantly lower by your T-SQL code than by the Winkler paper below (0.8888 vs. 0.9444).
Most other scores are either almost identical, or relatively similar.
Jaro-Winkler output validation against a paper by William E. Winkler and Yves Thibaudeau, U.S. Census: url=http://www.census.gov/srd/papers/pdf/rr91-9.pdf
Referenced from the U.S. National Institute of Standards and Technology: url=http://www.itl.nist.gov/div897/sqg/dads/HTML/jaroWinkler.html
SELECT [dbo].[JaroWinkler]('shackleford','shackelford') -- expect 0.9848
SELECT [dbo].[JaroWinkler]('cunningham','cunnigham') -- expect 0.9833
SELECT [dbo].[JaroWinkler]('campell','campbell') -- expect 0.9792
SELECT [dbo].[JaroWinkler]('nichleson','nichulson') -- expect 0.9630
SELECT [dbo].[JaroWinkler]('massey','massie') -- expect 0.9444
SELECT [dbo].[JaroWinkler]('abroms','abrams') -- expect 0.9333
SELECT [dbo].[JaroWinkler]('galloway','calloway') -- expect 0.9167
SELECT [dbo].[JaroWinkler]('lampley','campley') -- expect 0.9048
SELECT [dbo].[JaroWinkler]('dixon','dickson') -- expect 0.8533
SELECT [dbo].[JaroWinkler]('frederick','fredrick') -- expect 0.9815
SELECT [dbo].[JaroWinkler]('michele','michelle') -- expect 0.9792
SELECT [dbo].[JaroWinkler]('jesse','jessie') -- expect 0.9722
SELECT [dbo].[JaroWinkler]('marhta','martha') -- expect 0.9667
SELECT [dbo].[JaroWinkler]('jonathon','jonathan') -- expect 0.9583
SELECT [dbo].[JaroWinkler]('julies','juluis') -- expect 0.9333
SELECT [dbo].[JaroWinkler]('jeraldine','geraldine') -- expect 0.9246
SELECT [dbo].[JaroWinkler]('yvette','yevett') -- expect 0.9111
SELECT [dbo].[JaroWinkler]('tanya','tonya') -- expect 0.8933
SELECT [dbo].[JaroWinkler]('dwayne','duane') -- expect 0.8578
June 10, 2009 at 3:06 pm
Very interesting, I am stepping through the code, I am getting the same results. I will let you know.
[font="Comic Sans MS"]Ira Warren Whiteside[/font]
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