Ah! The algorithm rang a bell!

Garrett: have you tried searching Archive.org? A LOT of your stuff
appears archived:
https://web-beta.archive.org/web/*/garrett%20fitzgerald%20

The equivalent FoxPro code, by the way is in the leafe downloads at
https://leafe.com/dls/vfp. Bob Calco wrote it up.

Also on Fox Wikis at: http://fox.wikis.com/wc.dll?Wiki~LevenshteinAlgorithm

Craig Boyd's blog about Spell Checking  at
http://www.sweetpotatosoftware.com/spsblog/CommentView.aspx?guid=8800bdb9-a9c2-484f-942f-6a08947d903a

On Wed, Apr 12, 2017 at 3:31 PM, Garrett Fitzgerald
<sarekofvul...@gmail.com> wrote:
> I wrote a FLL to do Levenshtein distances for fuzzy name matching, but
> everything was posted to my blog, which is no longer online. It wasn't
> amazingly hard to figure out, though, so it might be worth finding the
> algorithm in C and recreating my steps. It ran much faster than equivalent
> Fox code did.
>
> On Wed, Apr 12, 2017 at 12:49 PM, Stephen Russell <srussell...@gmail.com>
> wrote:
>
>> I remember this joy of searching names in a system that had 2+ million
>> customers and names were all varchar() instead of a key to a secondary
>> table.  My indexes sure took a beating when I got another "Williams", the
>> number one last name in the system, and it had to tear a page to make a new
>> page in this area.
>>
>> I found that making a table called NAMES fixed the search time I was
>> experiencing.  Two text boxes had input for whatever they keyed.  I added
>> the % for wildcard after any text in each box and one of the keypress
>> events was the trigger to run it.
>>
>> Select <field_list>
>> from customer
>> where lNameID in (
>> select nameID from names
>> where Name like @Lname)
>> and
>> fNameID in (
>> select nameID from names na
>> where na.Name like @Fname)
>>
>> That has been 10-13 years ago.
>>
>>
>>
>>
>> On Wed, Apr 12, 2017 at 9:55 AM, Ken Dibble <krdib...@stny.rr.com> wrote:
>>
>> > Hi folks,
>> >
>> > I've been thinking of how I can improve the ability of my users to find
>> > people's names in a system that has over 30,000 people in it.
>> >
>> > I've looked at soundex, and I've considered munging names to remove
>> > spaces, apostrophes, hyphens, etc. The thing about those approaches is
>> that
>> > in order to be efficient, they require pre-processing all of the names in
>> > the system and storing the results, which can then be queried to find
>> > matches.
>> >
>> > Unfortunately, that would require modifications to the database, which I
>> > try to avoid due to the downtime they require.
>> >
>> > I'm looking for suggestions on how to produce results that include close
>> > matches on last names that doesn't require pre-processing.
>> >
>> > I've played with various schemes to assign "weights" to matches based on
>> > the number of matching letters, but they all end up being very slooooow
>> and
>> > also producing too many false positives.
>> >
>> > I suppose there are no easy answers, but if anyone has an algorithm for
>> > this kind of thing that they would be willing to share, I'd be grateful.
>> >
>> > Thanks.
>> >
>> > Ken Dibble
>> > www.stic-cil.org
>> >
>> >
[excessive quoting removed by server]

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