I am facing a weird problem.
I have extracted data from an Excel file. It should contain an IBAN account number.
Then I tried to analyze the set of account numbers (which the source guarantees to be good) with a Java library.
To keep the scope of the question narrow, I can't explain the following. The below strings are different
03069
03069
The first is a copy & paste from the Excel file, the second is handwritten. Google returns different results for abi [above number] and in fact in the second case I can find that it is the bank code for Intesa Sanpaolo bank (exact page displaying the ABI code, localized, here).
So, to keep the scope narrow: how is that possible? Is it something to do with the encoding?
Try it yourself: do CTRL+F and try type "030", it will select both lines. Now type 6, it will match only the 2nd line.
Same happened in Notepad++
There's an U+200B ZERO WIDTH SPACE in between 030 and 69 in the first text.
Paste the text in https://www.branah.com/unicode-converter for example, or edit in a hexadecimal capable editor.
The solution for cleaning such strings could be for example to whitelist characters, so replace everything that isn't A-Z0-9 will be scrubbed.
I am currently thinking of how to find a location from a text, such as a blogpost, without the user having to input any additional information. For example a post could look like this:
"Aberdeen, With a Foot on the Seafloor
Since the early 1970s, Aberdeen, Scotland, has evolved from a gritty fishing town into the world’s center of innovation in technology for the offshore energy industry."
By reading it I realize that the post is about Aberdeen Scotland but how can I geotag it? I have been using the geocoder (https://github.com/alexreisner/geocoder) by Alex Reisner but it seems weird to check every word against the google/nominatim(osm). My initial idea was to simply bruteforce it by checking every word with the geocoder and try to see if there are similarities between the words. But it seems like there could be a better way around this.
Has anyone done anything similar to this? Any algorithm that could be suggested (or gem :) ) would be immensely appreciated!
I'm sure there have been projects dedicated to this - for example, google's uncanny ability to geotag and pick data out of your personal emails effortlessly.
The most obvious answer I can see here, would be to create a few regular expressions for locations. The most simple one would be for City, Country:
Regexp.new("((?:[a-z][a-z]+))(.)(\\s+)((?:[a-z][a-z]+))",Regexp::IGNORECASE);
This would recognize Aberdeen, Scotland, but also course, I or even thanks, bye. It would be a start though, to query only those recognized spots instead of every word in the document.
There are also widely known regular expressions for addresses, cities, etc. You could use those as well if you find your algorithm missing matches.
Cheers!
Right now I'm only trying to read addresses and display them. Ignoring IPP right now, just inside QB, I'm not understanding the algorithm that manages the address lines.
Further, when accessing the customer address object via IPP, there are more differences, adding to my confusion. I'll call the three areas I'm looking at the freeform block, field block and IPP object. Here's an example where I typed the text into the field block and made the text match the field name:
The freeform block and IPP object took the City, State and Zip values and combined them into line 3. The IPP object has the Note value in Line 4. And the Country value ends up in the City field in IPP and field block.
Here's an example where I simply typed "line 1 ... line 5" in the freeform block:
Lines 1 - 4 look ok in the field block after the conversion, and put "line 5" into the City field. The IPP object is missing Line 4 field and value altogether.
Can someone share with us how this works? I'm trying to read these addresses and display them in my app in a consistent way.
I'm not familiar with Quickbooks. But I think you're looking for "address standardization" since you aren't sure in what format the address will come from Quickbooks.
Addresses are tricky (trust me, I work at SmartyStreets, where we have to be smart ... about streets) but there are services -- free and paid -- which will standardize addresses and put them into a consistent "componentized" format.
Take a look at LiveAddress API for starters... or you could use the batch/list service if you export your data into a file. Either way, it's free to use for a certain number of addresses.
(Tip: You can submit addresses for standardization and verification in two fields: "Street" and "Last Line" and still get good results -- so if you're not exactly sure where the city/state are, just put anything that's not the street address in the last line field.)
We are in need of developing a back end application that can parse a full name into
Prefix (Dr. Mr. Ms. etc)
First Name
Last Name
Middle Name
etc
Challenge here is that it has to support names of multiple countries and languages. One assumption that we have is we will always get a country and language along with the full name as input.
The full name may come in any format. For the same country / language combination, it may come in with first name last name or the reverse. Comma will not be a part of the Full Name.
Is is feasible? We are also open to any commercially available software.
I think this is impossible. Consider Ralph Vaughan Williams. His family name is "Vaughan Williams" and his first name is "Ralph". Contrast this with Charles Villiers Stanford, whose family name is "Stanford", with first name "Charles" and middle name "Villiers".
Both are English-speaking composers from England, so country and language information is not sufficient to establish the correct parsing logic.
Since the OP was open to any commercially available offering...
The "IBM InfoSphere Global Name Analytics" appears to be a commercial solution satisfying the original request for the parsing of a [free-form unstructured] personal name [full name]; apparently with a degree of certainty in regards to resolving some of the name ambiguity issues alluded to in other responses.Note: I have no personal experience nor association with the product, I had merely encountered this discussion and the following reference links while re-investigating effectively the same concern as described by the OP. HTH.
A general product documentation link:
http://publib.boulder.ibm.com/infocenter/gnrgna/v4r1m0/topic/com.ibm.gnr.gna.ic.doc/topics/gnr_gna_con_gnaoverview.html
Refer to the "Parsing names using NameParser" at
http://publib.boulder.ibm.com/infocenter/gnrgna/v4r1m0/topic/com.ibm.gnr.gna.ic.doc/topics/gnr_np_con_parsingnamesusingnameparser.html
The NameParser is a component API for the product per
http://publib.boulder.ibm.com/infocenter/gnrgna/v4r1m0/topic/com.ibm.gnr.gna.ic.doc/topics/gnr_gnm_con_logicalarchitecturecapis.html
Refer to the "Parsing names using IBM NameWorks" at
http://publib.boulder.ibm.com/infocenter/gnrgna/v4r1m0/topic/com.ibm.gnr.gna.ic.doc/topics/gnr_gnm_con_parsingnamesusingnameworks.html
"IBM NameWorks combines the individual IBM InfoSphere Global Name Recognition components into a single, unified, easy-to-use application programming interface (API), and also extends this functionality to Java applications and as a Web service"
http://publib.boulder.ibm.com/infocenter/gnrgna/v4r1m0/topic/com.ibm.gnr.gna.ic.doc/topics/gnr_gnm_con_logicalarchitecturenwapis.html
To clarify why I think this answers the question, ameliorating some of the previous alluded difficulties in accomplishing the task... If I understood correctly what I read, the APIs use the "NameHunter Server" to search the "IBM InfoSphere Global Name Data Archive (NDA)" which is described as "a collection of nearly one billion names from around the world, along with gender and country of association for each name. This large repository of name information powers the algorithms and rules that IBM InfoSphere Global Name Recognition products use to categorize, classify, parse, genderize , and match names."
FWiW I also ran across a "Name Parser" which uses a database of ~140K names as noted at:
http://www.melissadata.com/dqt/websmart-web-services.htm
The only reasonable approach is to avoid having to do so in the first place. The most obvious (and common) way to do that is to have the user enter the title, first/given name, last/family name, suffix, etc., separately from each other, rather than attempting to parse them out of a single string.
Ask yourself: do you really need the different parts of a name? Parsing names is inherently un-doable, since different cultures use different conventions (e.g. "middle name" is a typical USA-ism) and some small percentage of names will always be treated wrongly.
It is much preferable to treat a name as an "atomic" not-splittable entity.
Here are two free PHP name parsing libraries for those on a budget:
https://code.google.com/p/php-name-parser/
http://jasonpriem.org/human-name-parse/
And here is a Javasript library in Node package manager:
https://npmjs.org/package/name-parser
I wrote a simple human name parser in javascript as an npm module:
https://www.npmjs.org/package/humanparser
humanparser
Parse a human name string into salutation, first name, middle name, last name, suffix.
Install
npm install humanparser
Usage
var human = require('humanparser');
var fullName = 'Mr. William R. Jenkins, III'
, attrs = human.parseName(fullName);
console.log(attrs);
//produces the following output
{ saluation: 'Mr.',
firstName: 'William',
suffix: 'III',
lastName: 'Jenkins',
middleName: 'R.',
fullName: 'Mr. William R. Jenkins, III' }
A basic algorithm could do the following:
First see if incoming string starts with a title such as Mrs and remove it if it does, checking against a fixed list of titles.
If there is one space left and one space exactly, assume first word is first name and second word is surname (which will be incorrect at times)
To go beyond that would be lots of work, see How to parse full names to identify avenues for improvement and see these involved IBM docs for further implementation clues
"Ashton Jordan" "Jordan Ashton" -- u can't tell which is the surname and which is the give name.
Also people in South India apparently don't have a surname. The same with Sherpas in the Himalayas.
But say you have a huge list of all surnames (which are never used as given names) then maybe you can use that to identify other parts of the name (Salutations/Given/Middle/Jr/Sr/I/II/...) And if there is ambiguity your name-parser could ask for human input.
As others have explained, the problem is not solvable. The best approach I can think of to storing names is storing the full name, followed by the start (and potentially also ending) offsets into a "primary collating subfield" which the person entering the name could have indicated by highlighting it or such. For example
John Robert Miller, Jr.
where the boldface is indicating what was marked as the "primary collating subfield". This range would then be moved to the beginning of the string when generating the collating key.
Of course this approach alone may not be sufficient if you also want to support titles (and ignoring them for collation purposes)...
How, in Informix IDS 11.5, do I determine in which SmartLob space does a BLOB reside?
So really it's two questions:
How can I get something like the dbschema command to produce the PUT clause.
How can I find from which sblobspace did a particular SmartLOB come from?
The answer to the DB-Schema (first) question is "with the '-ss' option", where 'ss' is mnemonic for 'server-specific'. This will include the data specific to IDS, such as the PUT clause.
The counter-question for the blobspace (second) question is:
Why do you think it matters which blobspace the blob comes from?
For an individual smart blob, you can find out which blob space a specific smart blob is stored in as long as you are using ESQL/C or one of the related C-based APIs. The function to do this is ifx_lo_specget_sbspace(), and it is documented in the ESQL/C manual.
I don't know of an SQL-based way of determining the smart blobspace that holds a particular blob.