Refined soundex

GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. Pyphonetics is a Python 3 library for phonetic algorithms. Right now, the following algorithms are implemented and supported:.

The module was largely based on the implementation of phonetic algorithms found in the Talisman. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

Sign up. A Python 3 phonetics library. Python Branch: master. Find file. Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit. Lilykos bump version after update. Latest commit c5ab0a6 Feb 25, Pyphonetics Pyphonetics is a Python 3 library for phonetic algorithms. Instalation The module is available in PyPI, just use pip install pyphonetics. You signed in with another tab or window. Reload to refresh your session.

You signed out in another tab or window. Feb 25, Feb 21, This article is ideally suited to SQL Server developers and those data professionals wishing to know more about SQL Server functions, and wanting to get a greater understanding of which functions are available and how they can help improve the efficiency and facility of SQL routines.

Using system functions can help remove unwanted inefficiencies in code, such as loops, nested loops and string-based field manipulation.

The functions available in SQL Server can tie in well with business logic, enabling the developer to write better-suited algorithms to support business requirements. Hopefully by seeing how useful just a few functions can be, this article will encourage you to find out more about system functions in SQL Server, and at the bottom of the article are a number of links to the Books Online documentation on non-user-defined functions.

However SQL Server ships with a wide variety of different functions, and these can be used in many different contexts. For those of you of a mathematical or statistical persuasion, it may interest you to know that SQL Server supports a wide variety of statistical aggregation and calculation functions, together with a number of mathematical and algebraic functions that can assist in, for example finance calculations, often taking the place of more complex and inefficient code. Consider the following example to calculate the variance of a set of temperature values.

Here we first define a test table with some temperature readings taken on different occasions for some major cities:. Let us now say that we wish to compute the average deviation from the mean for each city in our test table. That is, we wish to find out how far away from the average, on average, each temperature reading is within the appropriate category location. However, we cannot simply calculate the deviation by taking each reading and subtracting or adding the mean, since this will give us a range of values that are mixed positive and negative and by definition will cancel each other out.

We must instead use a different method of measuring the deviation.

Using SOUNDEX and DIFFERENCE to Standardize Data in SQL Server

So to be more precise, variance, mathematically, is defined as the sum of the squared distances of each member of a set of numbers from the mean average of that set of numbers, divided by the number of members. So, in the example above, we might wish to know the mean temperature in Moscow based on the information available, and the variance of this set of data as our chosen measure of variation in temperature.

In statistics, for precisely this reason, standard deviation is the preferred variance measure as the square root of the variance provides a linear relationship instead of an exponential one. Variance still has its uses in mathematical circles, for example when working out the total variance of uncorrelated variables, and other matters familiar to those versed in discrete probability theory. For example, one may wish to identify statistical outliers in the temperature information given - if we had a hundred different readings and knew that the typical verified variance is approximately 39deg for Moscow, any figure significantly different from this would raise alarm bells.

Standard deviation would not do, since the differences are linear and may be missed when statistically aggregating the individual variances as the difference between the expected and actual standard deviations would be too small - whereas the exponential nature of variance, obtained using VARP, would quickly distort the 39deg figure into an unlikely number, rendering it identifiable to the statistical researcher.

Moving away from statistics, the SOUNDEX function is an interesting example of a function that exclusively implements a third-party specification, a proprietary algorithm developed and patented privately nearly a hundred years ago.

refined soundex

The Soundex specification is designed for use in systems where words need to be grouped by phonic sound rather than by spelling - for example, in ancestry and genealogy, the surnames Smith, Smyth and Smithe are spelled differently but could be pronounced the same. It is important for users of expert systems that deal in phonics to be able to recognize these similarities without complex and inefficient rule based systems to slow down the storage and retrieval process. A good use of Soundex could be to assist in the automatic detection of fraud.

Some time ago, I used to work as a database administrator for a small company specializing in the provision of online gambling to US customers.

refined soundex

This company would use a number of weighted measures built as stored procedures which would take many different factors, from postcode to IP address to demographic, to make an intelligent decision on whether a new customer signup was likely a duplicate of an existing account, in order to help prevent fraud.

In summary, Soundex is a specification originally designed in by Robert C. Russell and Margaret Odell, and has risen to prominence in a number of database specifications as a preferred system for categorizing and relating phonics.

For the history of Soundex and for a more detailed description of the variants in use today, see the links at the bottom of the page for more information. We could implement a SOUNDEX-based system to identify similarly-sounding names and write a stored procedure which will identify customer records based on matching phonics.

refined soundex

Here's a brief example of how it could work. As a database administrator, much of my job involves reviewing and correcting code, examining existing SQL schemas, procedures and functions, and improving performance wherever possible. As you can see, this is inefficient and bulky. It pre-separates out the source data into the distinct classes, orders the contents of those classes by TotalDeposited, and glues them back together in a results table. The output is identical to this slimline version using RANK :.

As you can see, this is easier to read. It doesn't use a cursor and the sorting and ordering are taken care of internally.

In essence, when there is a tie-break in the conditions for the RANK then the ranking 'skips' one value and continues on.

This is illustrated by adding another line to our example:. This will insert another row with the TotalDeposited value at Released: Feb 25, View statistics for this project via Libraries.

Pyphonetics is a Python 3 library for phonetic algorithms.

18 интегральных усилителей с записью звука. #soundex_review

Right now, the following algorithms are implemented and supported:. The module was largely based on the implementation of phonetic algorithms found in the Talisman.

Feb 25, Feb 21, Nov 17, Download the file for your platform. If you're not sure which to choose, learn more about installing packages. Warning Some features may not work without JavaScript. Please try enabling it if you encounter problems. Search PyPI Search. Latest version Released: Feb 25, A Python 3 phonetics library. Navigation Project description Release history Download files. Project links Homepage. Maintainers Lilykos.

Project description Project details Release history Download files Project description Pyphonetics is a Python 3 library for phonetic algorithms. Instalation The module is available in PyPI, just use pip install pyphonetics. Project details Project links Homepage. Release history Release notifications This version. Download files Download the file for your platform. Files for pyphonetics, version 0.

Close Hashes for pyphonetics File type Wheel. Python version py2. Upload date Feb 25, My client wants to standardize address information for existing and future addresses collected for their customers, particularly the street suffixes. The application used to enter and collect address information has the street suffix separated from the address field, but it is a textbox instead of a drop down list therefore things are not standardized.

Forgotten SQL Server Functions - VARP, SOUNDEX and NTILE

I know there are some options out there to standarize data, but they would like a less expensive alternative. The first character is the first letter of the phrase. Vowels are omitted unless the first letter of the phrase is a vowel and the other three characters represent the rest of the phrase. Use this sample script RoadTypeInsert. Once the values are in the table you can use either function to retrieve the best match for the street suffix.

Below are samples of each function being used to retrieve the best match:. Post a comment or let the author know this tip helped.

refined soundex

All comments are reviewed, so stay on subject or we may delete your comment. Note: your email address is not published. Signup for our newsletter. I have read the privacy statement and understand I may unsubscribe at any time. Why re-invent the wheel? While the soundex and difference functions are useful I found that the generalizations they produce in evaluating address information is too broad to be a dependable method of evaluating addresses.

In many cases the data entry issues are significant and ultimately they are the key factor that you want to address. I have a scoring system set up that actually is The key is to score the addresses and order them by the combined score. Done in this fashion you can achieve an exceptionally high accuracy rating.

Here is the primary scoring function that drives my system. The function also takes into account the issue of matching streets with numeric names and transposed characters since a good portion of the time street names are not entered correctly. DECLARE nums bigintchars varcharnums2 bigintchars2 varcharcresults decimal 182maxstring1 decimalmaxstring2 decimal. I would expect "ST" to be in my results At least in the top 10! Any ideas what is happening here?

View all my tips. Email me updates. A frenid of mine has pointed out that at some point, someone might have manually edited the field of this row possible I guess, unlikely but possible. My frenid tells me that with an Oracle date, that they store date plus time and zone information all in one. That makes sense to me, as every other field was displaying through ASP as a plain date.

Have you heard of anyone else having a similar problem? Tuesday, November 23, - AM - Steven Christensen Back To Top While the soundex and difference functions are useful I found that the generalizations they produce in evaluating address information is too broad to be a dependable method of evaluating addresses. Recommended Reading.Soundex is a phonetic algorithm for indexing names by sound, as pronounced in English.

The goal is for homophones to be encoded to the same representation so that they can be matched despite minor differences in spelling. Soundex was developed by Robert C.

Russell and Margaret King Odell [8] and patented in [9] and The Soundex code came to prominence in the s when it was the subject of several articles in the Communications and Journal of the Association for Computing Machineryand especially when described in Donald Knuth's The Art of Computer Programming.

The Soundex code for a name consists of a letter followed by three numerical digits : the letter is the first letter of the name, and the digits encode the remaining consonants. Consonants at a similar place of articulation share the same digit so, for example, the labial consonants B, F, P, and V are each encoded as the number 1.

Using this algorithm, both "Robert" and "Rupert" return the same string "R" while "Rubin" yields "R". The two algorithms above do not return the same results in all cases primarily because of the difference between when the vowels are removed. The first algorithm is used by most programming languages and the second is used by SQL. In designing an application, which combines SQL and a programming language, the architect must decide whether to do all of the Soundex encoding in the SQL server or all in the programming language.

The MySQL implementation can return more than 4 characters. A similar algorithm called "Reverse Soundex" prefixes the last letter of the name instead of the first. Daitch—Mokotoff Soundex D—M Soundex was developed in by genealogist Gary Mokotoff and later improved by genealogist Randy Daitch because of problems they encountered while trying to apply the Russell Soundex to Jews with Germanic or Slavic surnames such as Moskowitz vs.

Moskovitz or Levine vs. D—M Soundex is sometimes referred to as "Jewish Soundex" or "Eastern European Soundex", [14] although the authors discourage the use of those names. The D—M Soundex algorithm can return as many as 32 individual phonetic encodings for a single name. Results of D-M Soundex are returned in an all-numeric format between and This algorithm is much more complex than Russell Soundex.

As a response to deficiencies in the Soundex algorithm, Lawrence Philips developed the Metaphone algorithm in Philips developed an improvement to Metaphone inwhich 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.

Philips created Metaphone 3 as a further revision in to provide a professional version that provides a much higher percentage of correct encodings for English words, non-English words familiar to Americans, and first and last names found in the United States.

It also provides settings that allow more exact consonant and internal vowel matching to allow the programmer to focus the precision of matches more closely. From Wikipedia, the free encyclopedia. Redirected from Refinedsoundex. Phonetic algorithm for indexing names by sound.

This article is about the phonetic algorithm. For the Rock n' Soul band, see the SoundEx. National Archives and Records Administration. Retrieved New York. Categories : Phonetic algorithms. Hidden categories: Articles with short description All articles needing examples Articles needing examples from December Namespaces Article Talk. Views Read Edit View history. By using this site, you agree to the Terms of Use and Privacy Policy.Phonetic matching algorithms may be used to encode tokens so that two different spellings that are pronounced similarly will match.

For examples of how to use this encoding in your analyzer, see Beider Morse Filter in the Filter Descriptions section. In general, phonetic matching lets you search a name list for names that are phonetically equivalent to the desired name. BMPM is similar to a soundex search in that an exact spelling is not required.

Unlike soundex, it does not generate a large quantity of false hits. From the spelling of the name, BMPM attempts to determine the language. It then applies phonetic rules for that particular language to transliterate the name into a phonetic alphabet. If it is not possible to determine the language with a fair degree of certainty, it uses generic phonetic instead. Finally, it applies language-independent rules regarding such things as voiced and unvoiced consonants and vowels to further insure the reliability of the matches.

For example, assume that the matches found when searching for Stephen in a database are "Stefan", "Steph", "Stephen", "Steve", "Steven", "Stove", and "Stuffin".

Also rejected were "Steph", "Steve", and "Stove". Of those, "Stove" is probably not one that we would have wanted. But "Steph" and "Steve" are possibly ones that you might be interested in. The name matching is also applicable to non-Jewish surnames from the countries in which those languages are spoken.

The Daitch-Mokotoff Soundex algorithm is a refinement of the Russel and American Soundex algorithms, yielding greater accuracy in matching especially Slavic and Yiddish surnames with similar pronunciation but differences in spelling.

To use this encoding in your analyzer, see Double Metaphone Filter in the Filter Descriptions section. Encodes tokens using the double metaphone algorithm by Lawrence Philips. Encodes tokens using the Soundex algorithm, which is used to relate similar names, but can also be used as a general purpose scheme to find words with similar phonemes. Caverphone is an algorithm created by the Caversham Project at the University of Otago. The algorithm is optimised for accents present in the southern part of the city of Dunedin, New Zealand.

NYSIIS is an encoding used to relate similar names, but can also be used as a general purpose scheme to find words with similar phonemes. We welcome feedback on Solr documentation. However, we cannot provide application support via comments. If you need help, please send a message to the Solr User mailing list. Phonetic Matching. Language Analysis Running Your Analyzer. Comments on this Page.The Fuzzy Match step finds strings that potentially match using duplicate-detecting algorithms that calculate the similarity of two streams of data.

This step returns matching values as a separated list as specified by user-defined minimal or maximal values. The General tab enables you to define the source transformation step, field, and which algorithm to use to match similar strings of data.

Identifies if streams can or cannot differ based on the use of uppercase and lowercase lettersonly for use with the Levenshtein algorithms. When checked, returns a single result with the highest similarity scorewhen unchecked, returns all matches that satisfy the minimal and maximal value setting as a separated list, separated by the values separator. Identifies the string that separate the matches.

Only available for specific algorithms and when the Get closer value option is unchecked. Evaluate Confluence today. Pentaho Data Integration. Pages Blog. Child pages. Pentaho Data Integration Steps. Fuzzy match. Browse pages. A t tachments 0 Page History. Jira links. Created by Jens Bleuellast modified on Feb 02, Description The Fuzzy Match step finds strings that potentially match using duplicate-detecting algorithms that calculate the similarity of two streams of data.

General Tab The General tab enables you to define the source transformation step, field, and which algorithm to use to match similar strings of data.

Option Definition Match field Defines the name of the column that contains the comparison value Value field Defines the similarity score for which to return a value. No labels. Powered by Atlassian Confluence 6.


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