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I have an SQL db with about 200,000 words. I need a query which I will be able to solve an anagram kind of. The difference is that I need all the possible words that could be made with the input characters. For example, if you input ofdg, it should output the words: do, go, and dog. Can you estimate the amount of time a query like this would take. How can I make it faster and more efficient? Also, in general how long does it take SQL to parse a 200000 row database.
To solve this problem, the first thing you need to do is reduce every word to what Scrabble players call an alphagram. That is, all the letters in the word but in alphabetical order. So do, go and dog make do, go and dgo. Of course, any given alphagram may correspond to more than one word, so, for example, alphagram dgo corresponds to both the words dog and god.
The next thing you need to do is construct a table with a key alphagram-sequence number and a single attribute field word.
Word lists tend to be static. For example, the two Scrabble word lists in the English-speaking world change about every 5 years of so. So you construct this lookup table beforehand. Performance is O( n ) and it is a sunk cost. That is, you do it once and store it, so it is not counted against the cost of the query. You have to do this beforehand. It makes absolutely no sense to build such an index on the fly every time a query comes in.
You may be wondering "What is all this about Scrabble?" The answer is that your figure of 200,000 words falls neatly between the two approved tournament word lists in the English-speaking world. The US National Scrabble Association's Official Tournament and Club Word List (2006) contains 178,691 words, and the international list, maintained by the World English Scrabble Players' Association, contains 246,691.
When you get a query you reduce the supplied word to a bunch of alphagrams. Input odfg makes alphagrams od fo go df dg fg dfo dgo fgo dfg dfgo (which is a pretty programming problem in pure SQL, so I have to assume there is a PHP or Python or JavaScript front-end that will do that for you). Then you do the lookup in the database. The cost of each query should be approximately O(log2 n), in other words pretty damn immediate. That sort of query is what relational databases are good at.
BTW, your example output is poor. Alphagram dfgo with what Scrabble players call 'build' (all possible subsets) makes do od of go dog god fog.
(I hate to have to do this rigmarole, but Hasbro's lawyers are touchy, so: Scrabble is a registered trademark owned in the USA by Hasbro, Inc.; in Canada by Hasbro Canada Corporation; and throughout the rest of the world by J. W. Spear & Sons, a Mattel Company.)
Well, the number of possible letter combination in a word of length n is n!. Apparently you have a few more options as you want the shorter words as well, but that does not change that much the general O(n!) relationship. So a simple algorithm trying all combinations and looking the up in the database will have that as complexity.
Making the algorithm more efficient is apparently to reduce the search space - for which there are a few options.
How long it takes to look up a 200.000 row table depends on what kind of data is stored in there, in what format and what indexes you have on that table.
Related
I am creating a machine learning model that essentially returns the correctness of one text to another.
For example; “the cat and a dog”, “a dog and the cat”. The model needs to be able to identify that some words (“cat”/“dog”) are more important/significant than others (“a”/“the”). I am not interested in conjunction words etc. I would like to be able to tell the model which words are the most “significant” and have it determine how correct text 1 is to text 2, with the “significant” words bearing more weight than others.
It also needs to be able to recognise that phrases don’t necessarily have to be in the same order. The two above sentences should be an extremely high match.
What is the basic algorithm I should use to go about this? Is there an alternative to just creating a dataset with thousands of example texts and a score of correctness?
I am only after a broad overview/flowchart/process/algorithm.
I think TF-IDF might be a good fit to your problem, because:
Emphasis on words occurring in many documents (say, 90% of your sentences/documents contain the conjuction word 'and') is much smaller, essentially giving more weight to the more document specific phrasing (this is the IDF part).
Ordering in Term Frequency (TF) does not matter, as opposed to methods using sliding windows etc.
It is very lightweight when compared to representation oriented methods like the one mentioned above.
Big drawback: Your data, depending on the size of corpus, may have too many dimensions (the same number of dimensions as unique words), you could use stemming/lemmatization in order to mitigate this problem to some degree.
You may calculate similiarity between two TF-IDF vector using cosine similiarity for example.
EDIT: Woops, this question is 8 months old, sorry for the bump, maybe it will be of use to someone else though.
I'm working with Google BigQuery to scrape the reddit comments database. I'll start with the query I'm working on:
SELECT
DATE(SEC_TO_TIMESTAMP(created_utc)) AS date,
subreddit,
author AS comment_author,
ups AS upvotes,
LOWER(body)
FROM
[fh-bigquery:reddit_comments.2015_01]
WHERE
body CONTAINS 'acid'
OR body CONTAINS 'ecstasy'
OR body CONTAINS 'fire'
OR body CONTAINS 'heroin'
LIMIT 10;
I need to scrape the reddit database for a list of about 30 drug-related word (I limited it to 3 for brevity).
I'm having trouble with two things:
I want to be able to correctly query the DB, but a lot of the results that are returned do not meet the criteria a.k.a. do not contain any of the matching words.
I want to be able to create a column which displays the specific word which was matched....so if it matched the word 'drug', that word would appear in a 'word_matched' column, along with the body, author, date, etc.
I've tried regular expressions as well for matching the words, but that doesn't seem to be helping either:
WHERE (REGEXP_MATCH(body,'drug|acid|ecstacy|fire|heroin|joint|marijuana|weed|bud|ganja|hash|blazing|blaze|meth|molly|pcp|shrooms|speed|uppers|valium|xanax|tripping|smoke|liquor|beer|alcohol|booze|acid|benzos|blow|cocaine|crack|crank|dank|dope|downers'))
Any and all help will be greatly appreciated. Thanks all!
Below adressing both points of the question
1. Have in output only matching words and not those which are part of another/different word. This is easy to accomplish using REGEXP_MATCH function
2. Have column wich consists of all matching words. (i think it makes more sense to have all matching words vs. just one as it is asked in question.
SELECT
[date],
subreddit,
comment_author,
upvotes,
GROUP_CONCAT(word) AS matches,
body
FROM (
SELECT
[date],
subreddit,
comment_author,
upvotes,
body,
word
FROM (
SELECT
DATE(SEC_TO_TIMESTAMP(created_utc)) AS [date],
subreddit,
author AS comment_author,
ups AS upvotes,
LOWER(body) AS body
FROM
[fh-bigquery:reddit_comments.2015_01]
WHERE REGEXP_MATCH(body, r'\b(drug|ecstacy|fire|heroin|joint|marijuana|weed|bud|ganja|hash|blazing|blaze|meth|molly|pcp|shrooms|speed|uppers|valium|xanax|tripping|smoke|liquor|beer|alcohol|booze|acid|benzos|blow|cocaine|crack|crank|dank|dope|downers)\b')
) x
CROSS JOIN (
SELECT SPLIT(list,'|') AS word FROM
(SELECT 'drug|ecstacy|fire|heroin|joint|marijuana|weed|bud|ganja|hash|blazing|blaze|meth|molly|pcp|shrooms|speed|uppers|valium|xanax|tripping|smoke|liquor|beer|alcohol|booze|acid|benzos|blow|cocaine|crack|crank|dank|dope|downers' AS list)
) y
HAVING body CONTAINS word
)
GROUP BY [date], subreddit, comment_author, upvotes, body
LIMIT 1000
Above solution provides list of matching words on best-effort basis, so please note:
If column matches consists one word - it is for sure exact matched word
But if this columns consist of few words - still one of those is exact match, but others can be not exact match.
I think for lengthy body - it still valuable to have those at least as a hint to what to look for. For example as in
drug,meth,heroin,alcohol,benzos it also inhibits the reuptake of serotonin and norepinephrine which gives a hell of a lot worse withdrawal symptoms than most other drugs(incl. heroin, meth, coke and etc.). from what i have heard the only things that rival tramadol it terms of withdrawal are benzos and alcohol.
liquor,beer,alcohol,booze 1. reinforce #3 - it is not cheap to live here. not by any stretch. expect to pay more than the rest of the country pays for everything. even franchises that operate nation-wide have special wa/perth pricing. 2. petrol has literally just dropped to $1 this past month, i wouldn't go as far as quoting that as our average price just yet. average is still between $1.20-1.30. 3. parking is free at beaches & parks, do not expect to get free parking anywhere in the city though. if you're using public parking in the city all day, expect to pay $50 unless you get in early. 4. forget bribing the cops, don't even call them "mate". last time i was pulled over (last week, random stop) i said "evening mate" as i was handing him my license and was responded with "don't call me mate, i'm not your friend, i don't know you". 5. unlike the rest of the world, regular stores do not sell alcohol here. liquor stores only, don't expect to buy beer from a gas station or grocery store. 6. rent is expensive, food is expensive, booze is expensive, being alive is expensive.
drug,meth,heroin,beer that's simply not true. first there's a difference between legalization and decriminalization. second, some european countries have places to go to safely use drugs. there is middle ground between allowing heroin to be sold all over town and having users go to prison. heroin, meth and some other drugs are not good things for society and their use should encouraged by making it as easy to buy as a 6 pack of beer. i'm not really sure why you can't see a middle ground because it's clearly not as black and white as you say. you can go after the dealers while leaving the users alone.
drug,fire,joint,smoke not a story about a rave, but still relevant i think: i was working a job called "fire watch," which is just what it sounds like, at a nine inch nails concert a few years ago. our comrades, the security workers, were far from seasoned professionals. they were mostly college temps with a yellow security tee shirt and a flashlight; they didn't even have radios. the job is basically to make sure people don't go into restricted areas. ...but this one boy scout took it upon himself to tame the metal masses. mid-concert, he pulled me close and shouted "they're smoking pot!" i shrugged, and shot him an "and?" look. i guess he thought i should care because technically a joint is a tiny dangerous drug fire, and i was on the fire crew. he then proceeded to disappear into the crowd, shoving people out of the way on his heroic journey toward the countless smoke puff origins. the next time i saw him he was bleeding out of his face and getting a flashlight in the eyes from an onsite emt. i guess it's pretty harsh to say that he deserved the beating, but it's hard to argue that he didn't go asking for it. i guess the moral of my story is that security people are just people, and some people's shittyness is inflamed when combined with authority. it sounds like your event just happened to be warded by a gaggle of douches, probably being captained by king fuckwad who really wanted to be a cop, but couldn't pass the exams.
Note: If you need list of only exact matches, it is still relatively easy to do with BigQuery User-Defined Functions
I suggest debugging this using REGEXP_EXTRACT. I tried running your query, and it kept finding things like "meth" in "something", which might be what you're seeing. You probably want to check for word boundaries around the match, since some of your words you are searching for can be contained in several normal, non-drug-related words.
Something like the following should help in debugging:
SELECT
DATE(SEC_TO_TIMESTAMP(created_utc)) AS date,
subreddit,
author AS comment_author,
ups AS upvotes,
REGEXP_EXTRACT(body, '(drug|acid|ecstacy|fire|heroin|joint|marijuana|weed|bud|ganja|hash|blazing|blaze|meth|molly|pcp|shrooms|speed|uppers|valium|xanax|tripping|smoke|liquor|beer|alcohol|booze|acid|benzos|blow|cocaine|crack|crank|dank|dope|downers)') AS match,
LOWER(body),
FROM
[fh-bigquery:reddit_comments.2015_01]
WHERE (REGEXP_MATCH(body,'drug|acid|ecstacy|fire|heroin|joint|marijuana|weed|bud|ganja|hash|blazing|blaze|meth|molly|pcp|shrooms|speed|uppers|valium|xanax|tripping|smoke|liquor|beer|alcohol|booze|acid|benzos|blow|cocaine|crack|crank|dank|dope|downers'))
LIMIT 10;
I am trying to develop a search engine in my free time modeled after google.
I am using the original google research paper listed here: http://infolab.stanford.edu/~backrub/google.html
However I am having a few problems here. To be exact I am having problem developing the forward index.
In the paper it says:
If a document contains words that fall into a particular barrel, the docID is recorded into the barrel, followed by a list of wordID's with hitlists which correspond to those words.
Now there are two problem with in this statement. First who decides which words out of the huge lexicon goes into the Forward Barrels? Do all of them go. Second is the meaning of the word corresponding. Does it mean words that actually appear in that document after the previous word or something else?
I am really new to Search Engines and would really appreciate any Information Retrival Expert helping me on this. If moderators think that this question belong in some other Stack Exchange site please do so.
First Question:
The string value of every word is mapped into an integer (by a hash function). This is because integers are far more easier to handle than strings. You can then define ranges (buckets or bins or whatever else you might want to call them) over these integer values, e.g.
term ids 0 to 1000 => Bin-1
term ids 1001 to 2000 => Bin-2
and so on.
Second question:
The context information is typically not used. A word is simply a term present in a document, such as the terms "the", "quick", "brown" etc.
Since you said you are new to IR, a good way to start would be to read an introductory book to IR, e.g. the book by Manning and Schutze.
I have Persons table in SQL Server 2008.
My goal is to find Persons who have almost similar addresses.
The address is described with columns state, town, street, house, apartment, postcode and phone.
Due to some specific differences in some states (not US) and human factor (mistakes in addresses etc.), address is not filled in the same pattern.
Most common mistakes in addresses
Case sensitivity
Someone wrote "apt.", another one "apartment" or "ap." (although addresses aren't written in English)
Spaces, dots, commas
Differences in writing street names, like 'Dr. Jones str." or "Doctor Jones street" or "D. Jon. st." or "Dr Jones st" etc.
The main problem is that data isn't in the same pattern, so it's really difficult to find similar addresses.
Is there any algorithm for this kind of issue?
Thanks in advance.
UPDATE
As I mentioned address is separated into different columns. Should I generate a string concatenating columns or do your steps for each column?
I assume I shouldn't concatenate columns, but if I'll compare columns separately how should I organize it? Should I find similarities for each column an union them or intersect or anything else?
Should I have some statistics collecting or some kind of educating algorithm?
Suggest approaching it thus:
Create word-level n-grams (a trigram/4-gram might do it) from the various entries
Do a many x many comparison for string comparison and cluster them by string distance. Someone suggested Levenshtein; there are better ones for this kind of task, Jaro-Winkler Distance and Smith-Waterman work better. A libraryt such as SimMetrics would make life a lot easier
Once you have clusters of n-grams, you can resolve the whole string using the constituent subgrams i.e. D.Jones St => Davy Jones St. => DJones St.
Should not be too hard, this is an all-too-common problem.
Update: Based on your update above, here are the suggested steps
Catenate your columns into a single string, perhaps create a db "view" . For example,
create view vwAddress
as
select top 10000
state town, street, house, apartment, postcode,
state+ town+ street+ house+ apartment+ postcode as Address
from ...
Write a separate application (say in Java or C#/VB.NET) and Use an algorithm like JaroWinkler to estimate the string distance for the combined address, to create a many x many comparison. and write into a separate table
address1 | address n | similarity
You can use Simmetrics to get the similarity thus:
JaroWinnkler objJw = new JaroWinkler()
double sim = objJw.GetSimilarity (address1, addres n);
You could also trigram it so that an address such as "1 Jones Street, Sometown, SomeCountry" becomes "1 Jones Street", "Jones Street Sometown", and so on....
and compare the trigrams. (or even 4-grams) for higher accuracy.
Finally you can order by similarity to get a cluster of most similar addresses and decide an approprite threshold. Not sure why you are stuck
I would try to do the following:
split up the address in multiple words, get rid of punctuation at the same time
check all the words for patterns that are typically written differently and replace them with a common name (e.g. replace apartment, ap., ... by apt, replace Doctor by Dr., ...)
put all the words back in one string alphabetically sorted
compare all the addresses using a fuzzy string comparison algorithm, e.g. Levenshtein
tweak the parameters of the Levenshtein algorithm (e.g. you want to allow more differences on longer strings)
finally do a manual check of the strings
Of course, the solution to keep your data 'in shape' is to have explicit fields for each of your characteristics in your database. Otherwise, you will end up doing this exercise every few months.
The main problem I see here is to exactly define equality.
Even if someone writes Jon. and another Jone. - you will never be able to say if they are the same. (Jon-Jonethan,Joneson,Jonedoe whatever ;)
I work in a firm where we have to handle exact this problem - I'm afraid I have to tell you this kind of checking the adress lists for navigation systems is done "by hand" most of the time. Abbrevations are sometimes context dependend, and there are other things that make this difficult. Ofc replacing string etc is done with python - but telling you the MEANING of such an abbr. can only done by script in a few cases. ("St." -> Can be "Saint" and "Street". How to decide? impossible...this is human work.).
Another big problem is as you said "Is there a street "DJones" or a person? Or both? Which one is ment here? Is this DJones the same as Dr Jones or the same as Don Jones? Its impossible to decide!
You can do some work with lists as presented by another answer here - but it will give you enough "false positives" or so.
You have a postcode field!!!
So, why don't you just buy a postcode table for your country
and use that to clean up your street/town/region/province information?
I did a project like this in the last centuary. Basicly it was a consolidation of two customer files after a merger, and, involved names and addresses from three different sources.
Firstly as many posters have suggested, convert all the common words and abbreveations and spelling mistakes to a common form "Apt." "Apatment" etc. to "Apt".
Then look through the name and identifiy the first letter of the first name, plus the first surname. (Not that easy consider "Dr. Med. Sir Henry de Baskerville Smythe") but dont worry where there are amiguities just take both! So if you lucky you get HBASKERVILLE and HSMYTHE. Now get rid of all the vowels as thats where most spelling variations occur so now you have HBSKRVLL HSMTH.
You would also get these strings from "H. Baskerville","Sir Henry Baskerville Smith" and unfortunately "Harold Smith" but we are talking fuzzy matching here!
Perform a similar exercise on the street, and apartment and postcode fields. But do not throw away the original data!
You now come to the interesting bit first you compare each of the original strings and give say 50 points for each string that matches exactly. Then go through you "normalised" strings and give say 20 points for each one that matches exactly. Then go through all the strings and give say 5 points for each four character or more substring they have in common. For each pair compared you will end up with some with scores > 150 which you can consider as a certain match, some with scores less than 50 which you can consider not matched and some inbetween which have some probability of matching.
You need some more tweaking to improve this by adding various rules like "subtract 20 points for a surname of 'smith'". You really have to keep running and tweaking until you get happy with the resulting matches, but, once you look at the results you get a pretty good feel which score to consider a "match" and which are the false positives you need to get rid of.
I think the amount of data could affect what approach works best for you.
I had a similar problem when indexing music from compilation albums with various artists. Sometimes the artist came first, sometimes the song name, with various separator styles.
What I did was to count the number of occurrences on other entries with the same value to make an educated guess wether it was the song name or an artist.
Perhaps you can use soundex or similar algorithm to find stuff that are similar.
EDIT: (maybe I should clarify that I assumed that artist names were more likely to be more frequently reoccurring than song names.)
One important thing that you mention in the comments is that you are going to do this interactively.
This allows to parse user input and also at the same time validate guesses on any abbreviations and to correct a lot of mistakes (the way for example phone number entry works some contact management systems - the system does the best effort to parse and correct the country code, area code and the number, but ultimately the user is presented with the guess and has the chance to correct the input)
If you want to do it really good then keeping database/dictionaries of postcodes, towns, streets, abbreviations and their variations can improve data validation and pre-processing.
So, at least you would have fully qualified address. If you can do this for all the input you will have all the data categorized and matches can then be strict on certain field and less strict on others, with matching score calculated according weights you assign.
After you have consistently pre-processed the input then n-grams should be able to find similar addresses.
Have you looked at SQL Server Integration Services for this? The Fuzzy Lookup component allows you to find 'Near matches': http://msdn.microsoft.com/en-us/library/ms137786.aspx
For new input, you could call the package from .Net code, passing the value row to be checked as a set of parameters, you'd probably need to persist the token index for this to be fast enough for user interaction though.
There's an example of address matching here: http://msdn.microsoft.com/en-us/magazine/cc163731.aspx
I'm assuming that response time is not critical and that the problem is finding an existing address in a database, not merging duplicates. I'm also assuming the database contains a large number of addresses (say 3 million), rather than a number that could be cleaned up economically by hand or by Amazon's Mechanical Turk.
Pre-computation - Identify address fragments with high information content.
Identify all the unique words used in each database field and count their occurrences.
Eliminate very common words and abbreviations. (Street, st., appt, apt, etc.)
When presented with an input address,
Identify the most unique word and search (Street LIKE '%Jones%') for existing addresses containing those words.
Use the pre-computed statistics to estimate how many addresses will be in the results set
If the estimated results set is too large, select the second-most unique word and combine it in the search (Street LIKE '%Jones%' AND Town LIKE '%Anytown%')
If the estimated results set is too small, select the second-most unique word and combine it in the search (Street LIKE '%Aardvark%' OR Town LIKE '%Anytown')
if the actual results set is too large/small, repeat the query adding further terms as before.
The idea is to find enough fragments with high information content in the address which can be searched for to give a reasonable number of alternatives, rather than to find the most optimal match. For more tolerance to misspelling, trigrams, tetra-grams or soundex codes could be used instead of words.
Obviously if you have lists of actual states / towns / streets then some data clean-up could take place both in the database and in the search address. (I'm very surprised the Armenian postal service does not make such a list available, but I know that some postal services charge excessive amounts for this information. )
As a practical matter, most systems I see in use try to look up people's accounts by their phone number if possible: obviously whether that is a practical solution depends upon the nature of the data and its accuracy.
(Also consider the lateral-thinking approach: could you find a mail-order mail-list broker company which will clean up your database for you? They might even be willing to pay you for use of the addresses.)
I've found a great article.
Adding some dlls as sql user-defined functions we can use string comparison algorithms using SimMetrics library.
Check it
http://anastasiosyal.com/archive/2009/01/11/18.aspx
the possibilities of such variations are countless and even if such an algorithm exists, it can never be fool-proof. u can't have a spell checker for nouns after all.
what you can do is provide a drop-down list of previously entered field values, so that they can select one, if a particular name already exists.
its better to have separate fields for each value like apartments and so on.
You could throw all addresses at a web service like Google Maps (I don't know whether this one is suitable, though) and see whether they come up with identical GPS coordinates.
One method could be to apply the Levenshtein distance algorithm to the address fields. This will allow you to compare the strings for similarity.
Edit
After looking at the kinds of address differences you are dealing with, this may not be helpful after all.
Another idea is to use learning. For example you could learn, for each abbreviation and its place in the sentence, what the abbreviation means.
3 Jane Dr. -> Dr (in 3rd position (or last)) means Drive
Dr. Jones St -> Dr (in 1st position) means Doctor
You could, for example, use decision trees and have a user train the system. Probably few examples of each use would be enough. You wouldn't classify single-letter abbreviations like D.Jones that could be David Jones, or Dr. Jones as likely. But after a first level of translation you could look up a street index of the town and see if you can expand the D. into a street name.
Again, you would run each address through the decision tree before storing it.
It feels like there should be some commercial products doing this out there.
A possibility is to have a dictionary table in the database that maps all the variants to the 'proper' version of the word:
*Value* | *Meaning*
Apt. | Apartment
Ap. | Apartment
St. | Street
Then you run each word through the dictionary before you compare.
Edit: this alone is too naive to be practical (see comment).
I have several sources of tables with personal data, like this:
SOURCE 1
ID, FIRST_NAME, LAST_NAME, FIELD1, ...
1, jhon, gates ...
SOURCE 2
ID, FIRST_NAME, LAST_NAME, ANOTHER_FIELD1, ...
1, jon, gate ...
SOURCE 3
ID, FIRST_NAME, LAST_NAME, ANOTHER_FIELD1, ...
2, jhon, ballmer ...
So, assuming that records with ID 1, from sources 1 and 2, are the same person, my problem is how to determine if a record in every source, represents the same person. Additionally, sure not every records exists in all sources. All the names, are written in spanish, mainly.
In this case, the exact matching needs to be relaxed because we assume the data sources has not been rigurously checked against the official bureau of identification of the country. Also we need to assume typos are common, because the nature of the processes to collect the data. What is more, the amount of records is around 2 or 3 millions in every source...
Our team had thought in something like this: first, force exact matching in selected fields like ID NUMBER, and NAMES to know how hard the problem can be. Second, relaxing the matching criteria, and count how much records more can be matched, but is here where the problem arises: how to do to relax the matching criteria without generating too noise neither restricting too much?
What tool can be more effective to handle this?, for example, do you know about some especific extension in some database engine to support this matching?
Do you know about clever algorithms like soundex to handle this approximate matching, but for spanish texts?
Any help would be appreciated!
Thanks.
The crux of the problem is to compute one or more measures of distance between each pair of entries and then consider them to be the same when one of the distances is less than a certain acceptable threshold. The key is to setup the analysis and then vary the acceptable distance until you reach what you consider to be the best trade-off between false-positives and false-negatives.
One distance measurement could be phonetic. Another you might consider is the Levenshtein or edit distance between the entires, which would attempt to measure typos.
If you have a reasonable idea of how many persons you should have, then your goal is to find the sweet spot where you are getting about the right number of persons. Make your matching too fuzzy and you'll have too few. Make it to restrictive and you'll have too many.
If you know roughly how many entries a person should have, then you can use that as the metric to see when you are getting close. Or you can divide the number of records into the average number of records for each person and get a rough number of persons that you're shooting for.
If you don't have any numbers to use, then you're left picking out groups of records from your analysis and checking by hand whether they look like the same person or not. So it's guess and check.
I hope that helps.
This sounds like a Customer Data Integration problem. Search on that term and you might find some more information. Also, have a poke around inside The Data Warehousing Institude, and you might find some answers there as well.
Edit: In addition, here's an article that might interest you on spanish phonetic matching.
I've had to do something similar before and what I did was use a double metaphone phonetic search on the names.
Before I compared the names though, I tried to normalize away any name/nickname differences by looking up the name in a nick name table I created. (I populated the table with census data I found online) So people called Bob became Robert, Alex became Alexander, Bill became William, etc.
Edit: Double Metaphone was specifically designed to be better than Soundex and work in languages other than English.
SSIS , try using the Fuzzy Lookup transformation
Just to add some details to solve this issue, I'd found this modules for Postgresql 8.3
Fuzzy String Match
Trigrams
You might try to cannonicalise the names by comparing them with a dicionary.
This would allow you to spot some common typos and correct them.
Sounds to me you have a record linkage problem. You can use the references in the link.