I am presently using GUIDs to identify certain data entities. When a customer or colleague needs to relate one of these they can, of course, copy and paste the IDs into instant messages or emails. However, they aren't very pretty and are a pain to describe over the phone or in person.
I came across the PGP cryptography suite in my youth, which includes a word list that been designed to be phonetically distinct, so you won't confuse words when heard over the phone.
https://en.wikipedia.org/wiki/PGP_word_list
I wrote code that turns integer IDs into 'horse battery staple'-style identifiers using this list.
Unfortunately, the PGP word list contains many negative-sounding and distracting words. For example, I don't want a customer to ring up and say "Hey, I'm having trouble with 'drunken-Eskimo-revenge'" (in fact nearly all the random ones I tried were outrageous).
Are there any good alternatives to the PGP word list? Ideally the words should be 'phonically distinct' and inoffensive.
Related
I am doing a project for my degree and I have an actual client from another college. They want me to do all this stuff with topic modeling to an sql file of paper abstracts he's given me. I have zero experience with topic modeling but I've been using Gensim and Nlkt in a Jupyter notebook for this.
What he want's right now is for me to generate 10 or more topics, record the top 10 most overall common words from the LDA's results, and then if they are very frequent in each topic, remove them from the resulting word cloud and if they are more variant, remove the words from just the topics where they are infrequent and keep them in the more relevant topics.
He also wants me to compare the frequency of each topic from the sql files of other years. And, he wants these topics to have a name generated smartly from the computer.
I have topic models per year and overall, but of course they do not appear exactly the same way in each year. My biggest concern is the first thing he wants with the removal process. Is any of this possible? I need help figuring out where to look as google is giving me not what I want as I am probably searching it wrong.
Thank you!
Show some of the code you use so we can give you more useful tips. Also use nlp tag, the tags you used are kind of specific and not followed by many people so your question might be hard to find for the relevant users.
By the whole word-removal thing do you mean stop words too? Or did you already remove those? Stop words are very common words ("the", "it", "me" etc.) which often appear high in most frequent word lists but do not really have any meaning for finding topics.
First you remove the stop words to make the most common words list more useful.
Then, as he requested, you look which (more common) words are common in ALL the topics (I can imagine in case of abstracts this is stuff like hypothesis, research, paper, results etc., so stuff that is abstract-specific but not useful for determining topics within different abstracts and remove those. I can imagine for this kind of analysis as well as the initial LDA it makes sense to use all the data from all years to have a large amount of data for the model to recognize patterns. But you should try around the variations and see if the per year or overall versions get you nicer results.
After you have your global word lists per topic you go back to the original data (split up by year) to count the frequencies of how often the combined words from a topic occur per year. If you view this over the years you probably can see trends like some topics that are popular in the last few years/now but if you go back far enough they werent relevant.
The last thing you mentioned (automatically assigning labels to topics) is actually something quite tricky, depending on how you go about it.
The "easy" way would be e.g. just use the most frequent word in each topic as label but the results will probably be underwhelming.
A more advanced approach is Topic Labeling. Or you can try an approach like modified text summarization using more powerful models.
I am creating a database of keyboards using MySQL and PHP.
Is there a standard way to number the individual keys on a keyboard based on their physical position?
I want to use these numbers in the index column of my database. I have been looking at this, but it is missing some of the newer keys:
https://www.ibm.com/support/knowledgecenter/en/ssw_aix_71/com.ibm.aix.keyboardtechref/doc/kybdtech/Key.htm#dbffbe3450dagi
Using scancodes might be an alternative, but converting back and forth between decimal and hexadecimal would be an annoyance. Also, some keys sometimes have two sets of scancodes, which would be problematic for my database. Thanks.
Short answer: No.
Long answer: There are more keyboards out there than you could catalog in a lifetime. Every major vendor has produced localized keyboards for hundreds of countries in dozens of versions and iterations over the last thirty-plus years. Some countries have more than one keyboard layout, like Canada, which has both US English and Canadian French.
Even focusing on a single vendor with a relatively standardized layout, like Apple, would require hundreds of hours of work to identify, precisely, the nature of each key and its physical location and shape on the keyboard.
The scancodes are fairly consistent, but I'm not sure that's much help when the key itself may be remapped in software to do different things, or may have different meanings in different languages.
You're going to need to experiment, explore, and probably buy an embarrassing number of keyboards on eBay if you want to create a comprehensive database.
What's the most efficient method or tool to randomize a list of database table columns to obscure sensitive information?
I have a Django application used by several clients, and I need to onboard some development contractors to do work on the application. When they work on bugs (e.g. page /admin/model/123 has an error), ideally they'd need a snapshot of the client database in order to reproduce and fix the bug. However, because they're off-site contractors, I'd like to mitigate risk in the event they expose the client database (unintentionally or otherwise). I don't want to have to explain to a client why all their data's been published online because a foreign contractor left his laptop in an unlocked car.
To do this, I'd like to find or write a tool to "randomize" sensitive fields in the database, like usernames, email addresses, account numbers, company names, phone numbers, etc so that the structure of the data is maintained, but all personally identifiable information is removed.
Presumably, this is a task that many other people have had to do, but I'm not sure what the technical term is, so I'm not finding much through Google. Are there any existing tools to do this with a Django application running a MySQL or PostgreSQL backend?
Anonymize and sanitize are good words for this chore.
It's relatively easy to do. Use queries like
UPDATE person
SET name = CONCAT('Person', person_id),
email = CONCAT('Person', person_id, '#example.com')
and so forth, to stomp actual names and emails and all that. It's helpful to preserve the uniqueness of entries, and the autoincrementing IDs of various tables can help you do that.
(Adding this as an answer, as I am not allowed to comment yet.)
As Cerin said, O. Jones approach for anonymizing/sanitizing works for simple fields, but not more complicated ones like addresses, phone number or account numbers that need to match a specific format. However, the method can be modified to allow this too.
Let's take a phone number with format aaa-bbbb-ccc as an example and use the autoincrementing person_id as the source of unique numbers. For the ccc part of the phone number, use MOD(person_id,1000). This will give the remainder of person_id divided by a 1000. For bbbb, take MOD((person_id-MOD(person_id,1000))/1000,10000). It looks complicated, but what this is doing is taking person_id, removing the last three digits (which were used for ccc), then dividing by a 1000. The last four digits are taken from the resulting number to use as bbbb. I think you'll be able to figure out how to calculate aaa.
The three parts of the phone number can then be concatenated to give the complete phone number: CONCAT(aaa,"-",bbbb,"-",ccc)
(You might have to explicitly convert the numbers to string, I'm not sure)
I made a vocabulary app for Android that has a list of ~5000 words stored in a local database (SQLite), and I want to find out which words are more difficult than others.
To find out, I'm thinking of adding a very simple feature that puts two random words on the screen, asking the user to choose the more difficult one. Then another pair of random words will show, and this process can be repeated for as long as the user wants. The more users who participate in this 'more difficult word', the app would in theory be able to distinguish difficult words from easy words.
Since the difficulty would be based on input from all users, I know I need to keep track of it online so that every app could then fetch them from the database on my website (which is MySQL). I'm not sure what would be the most efficient way to keep track of the difficulty, but I came up with two possible solutions:
1) Add a difficulty column that holds integer values to the words table. Then for every pair of words that a user looks at and ranks, the word that he/she chooses more difficult would have have its difficulty increased by one, and the word not chosen would have its difficulty decreased by one. I could simply order by that integer value to get the most difficult ones.
2) Create a difficulty table with two columns, more and less, that hold words (or ID's of the words to save space) based on the results of each selection a user makes. I'm still unsure how I would get the most difficult words - some combination of group by and order by?
The benefit of my second solution is that I can know how many times each word has been seen (# of rows from the more column that contain the word + # rows from the less column that contain the word). That helps with statistics, like if I wanted to find out which word has the highest ratio of more / less. But it would also take up much more space than my first suggested solution would, and don't know how it could scale.
Which do you think is the better solution, or what other ones should I consider?
Did you try sphinx for this? Guess a full text search engine like sphinx would solve with great performance.
My users will import through cut and paste a large string that will contain company names.
I have an existing and growing MYSQL database of companies names, each with a unique company_id.
I want to be able to parse through the string and assign to each of the user-inputed company names a fuzzy match.
Right now, just doing a straight-up string match, is also slow. ** Will Soundex indexing be faster? How can I give the user some options as they are typing? **
For example, someone writes:
Microsoft -> Microsoft
Bare Essentials -> Bare Escentuals
Polycom, Inc. -> Polycom
I have found the following threads that seem similar to this question, but the poster has not approved and I'm not sure if their use-case is applicable:
How to find best fuzzy match for a string in a large string database
Matching inexact company names in Java
You can start with using SOUNDEX(), this will probably do for what you need (I picture an auto-suggestion box of already-existing alternatives for what the user is typing).
The drawbacks of SOUNDEX() are:
its inability to differentiate longer strings. Only the first few characters are taken into account, longer strings that diverge at the end generate the same SOUNDEX value
the fact the the first letter must be the same or you won't find a match easily. SQL Server has DIFFERENCE() function to tell you how much two SOUNDEX values are apart, but I think MySQL has nothing of that kind built in.
for MySQL, at least according to the docs, SOUNDEX is broken for unicode input
Example:
SELECT SOUNDEX('Microsoft')
SELECT SOUNDEX('Microsift')
SELECT SOUNDEX('Microsift Corporation')
SELECT SOUNDEX('Microsift Subsidary')
/* all of these return 'M262' */
For more advanced needs, I think you need to look at the Levenshtein distance (also called "edit distance") of two strings and work with a threshold. This is the more complex (=slower) solution, but it allows for greater flexibility.
Main drawback is, that you need both strings to calculate the distance between them. With SOUNDEX you can store a pre-calculated SOUNDEX in your table and compare/sort/group/filter on that. With the Levenshtein distance, you might find that the difference between "Microsoft" and "Nzcrosoft" is only 2, but it will take a lot more time to come to that result.
In any case, an example Levenshtein distance function for MySQL can be found at codejanitor.com: Levenshtein Distance as a MySQL Stored Function (Feb. 10th, 2007).
SOUNDEX is an OK algorithm for this, but there have been recent advances on this topic. Another algorithm was created called the Metaphone, and it was later revised to a Double Metaphone algorithm. I have personally used the java apache commons implementation of double metaphone and it is customizable and accurate.
They have implementations in lots of other languages on the wikipedia page for it, too. This question has been answered, but should you find any of the identified problems with the SOUNDEX appearing in your application, it's nice to know there are options. Sometimes it can generate the same code for two really different words. Double metaphone was created to help take care of that problem.
Stolen from wikipedia: http://en.wikipedia.org/wiki/Soundex
As a response to deficiencies in the
Soundex algorithm, Lawrence Philips
developed the Metaphone algorithm for
the same purpose. Philips later
developed an improvement to Metaphone,
which he called Double-Metaphone.
Double-Metaphone includes a much
larger encoding rule set than its
predecessor, handles a subset of
non-Latin characters, and returns a
primary and a secondary encoding to
account for different pronunciations
of a single word in English.
At the bottom of the double metaphone page, they have the implementations of it for all kinds of programming languages: http://en.wikipedia.org/wiki/Double-Metaphone
Python & MySQL implementation: https://github.com/AtomBoy/double-metaphone
Firstly, I would like to add that you should be very careful when using any form of Phonetic/Fuzzy Matching Algorithm, as this kind of logic is exactly that, Fuzzy or to put it more simply; potentially inaccurate. Especially true when used for matching company names.
A good approach is to seek corroboration from other data, such as address information, postal codes, tel numbers, Geo Coordinates etc. This will help confirm the probability of your data being accurately matched.
There are a whole range of issues related to B2B Data Matching too many to be addressed here, I have written more about Company Name Matching in my blog (also an updated article), but in summary the key issues are:
Looking at the whole string is unhelpful as the most important part
of a Company Name is not necessarily at the beginning of the Company
Name. i.e. ‘The Proctor and Gamble Company’ or ‘United States Federal
Reserve ‘
Abbreviations are common place in Company Names i.e. HP, GM, GE, P&G,
D&B etc..
Some companies deliberately spell their names incorrectly as part of
their branding and to differentiate themselves from other companies.
Matching exact data is easy, but matching non-exact data can be much more time consuming and I would suggest that you should consider how you will be validating the non-exact matches to ensure these are of acceptable quality.
Before we built Match2Lists.com, we used to spend an unhealthy amount of time validating fuzzy matches. In Match2Lists we incorporated a powerful Visualisation tool enabling us to review non-exact matches, this proved to be a real game changer in terms of match validation, reducing our costs and enabling us to deliver results much more quickly.
Best of Luck!!
Here's a link to the php discussion of the soundex functions in mysql and php. I'd start from there, then expand into your other not-so-well-defined requirements.
Your reference references the Levenshtein methodology for matching. Two problems. 1. It's more appropriate for measuring the difference between two known words, not for searching. 2. It discusses a solution designed more to detect things like proofing errors (using "Levenshtien" for "Levenshtein") rather than spelling errors (where the user doesn't know how to spell, say "Levenshtein" and types in "Levinstein". I usually associate it with looking for a phrase in a book rather than a key value in a database.
EDIT: In response to comment--
Can you at least get the users to put the company names into multiple text boxes; 2. or use an unambigous name delimiter (say backslash); 3. leave out articles ("The") and generic abbreviations (or you can filter for these); 4. Squoosh the spaces out and match for that also (so Micro Soft => microsoft, Bare Essentials => bareessentials); 5. Filter out punctuation; 6. Do "OR" searches on words ("bare" OR "essentials") - people will inevitably leave one or the other out sometimes.
Test like mad and use the feedback loop from users.
the best function for fuzzy matching is levenshtein. it's traditionally used by spell checkers, so that might be the way to go. there's a UDF for it available here: http://joshdrew.com/
the downside to using levenshtein is that it won't scale very well. a better idea might be to dump the whole table in to a spell checker custom dictionary file and do the suggestion from your application tier instead of the database tier.
This answer results in indexed lookup of almost any entity using input of 2 or 3 characters or more.
Basically, create a new table with 2 columns, word and key. Run a process on the original table containing the column to be fuzzy searched. This process will extract every individual word from the original column and write these words to the word table along with the original key. During this process, commonly occurring words like 'the','and', etc should be discarded.
We then create several indices on the word table, as follows...
A normal, lowercase index on word + key
An index on the 2nd through 5th character + key
An index on the 3rd through 6th character + key
Alternately, create a SOUNDEX() index on the word column.
Once this is in place, we take any user input and search using normal word = input or LIKE input%. We never do a LIKE %input as we are always looking for a match on any of the first 3 characters, which are all indexed.
If your original table is massive, you could partition the word table by chunks of the alphabet to ensure the user's input is being narrowed down to candidate rows immediately.
Though the question asks about how to do fuzzy searches in MySQL, I'd recommend considering using a separate fuzzy search (aka typo tolerant) engine to accomplish this. Here are some search engines to consider:
ElasticSearch (Open source, has a ton of features, and so is also complex to operate)
Algolia (Proprietary, but has great docs and super easy to get up and running)
Typesense (Open source, provides the same fuzzy search-as-you-type feature as Algolia)
Check if it's spelled wrong before querying using a trusted and well tested spell checking library on the server side, then do a simple query for the original text AND the first suggested correct spelling (if spell check determined it was misspelled).
You can create custom dictionaries for any spell check library worth using, which you may need to do for matching more obscure company names.
It's way faster to match against two simple strings than it is to do a Levenshtein distance calculation against an entire table. MySQL is not well suited for this.
I tackled a similar problem recently and wasted a lot of time fiddling around with algorithms, so I really wish there had been more people out there cautioning against doing this in MySQL.
Probably been suggested before but why not dump the data out to Excel and use the Fuzzy Match Excel plugin. This will give a score from 0 to 1 (1 being 100%).
I did this for business partner (company) data that was held in a database.
Download the latest UK Companies House data and score against that.
For ROW data its more complex as we had to do a more manual process.