I have developed a Linux/Umbutu program running on what will be an imbedded P-based device. I wish for that piece of code to be able to send and receive the SNMP data for the entire product. So, I know the OID down to the company level, 1.3.6.1.4.1.34843, and I further know that the two SNMP enabled products we sell are 1.3.6.1.4.1.34843.1 and 1.3.6.1.4.1.34843.2. Pretty sure mine will be .3.
The question is, is there a known structure/convention for the nodes below this point? What I read seems to imply that I can use whatever structure I want. Would ...34843.1.0 exist? Is it a node to fetch (get) the list or max-count of same-level nodes? Would ...34843.1.0.0 exist? Would it make any sense? The guy who developed the first two MIBs is out of town for a while and I can't really ask him, and I'd like to make sure I understand his answer when he gives it to me.
Is there a document that describes the required/suggested organization? Probably some RFC, right. So, is there a document in a language spoken by humans?
Thanks in advance.
The most popular resource is the understanding SNMP MIBs book by Perkins and McGinnis. It describes MIBs in a more readable format. The Net-SNMP tutorials online might be helpful too, as they discuss a lot about MIBs. If you want the RFC, then RFC2578 is the right thing to read.
The quick hierarchy guidance would be something along the lines of:
Use 3 sections: one for objects, one for notifications, and one for conformance information
For notifications, root them at the .0 (see RFC3584 for details on why)
For each table, you might want a scalar showing the number of objects in a table and potentially a LastChanged object for showing how frequently configuration within the table might have changed).
There is a lot more guidance that can be found in the books and other sources listed above than can be repeated here, of course. RFCs 4181, and 5249 may be helpful as well.
Related
I need to change an existing system (written in Python using python-hl7) to accept messages with details of a surgical procedure for a patient and then record those details in a database as part of their medical record. This question is more about HL7 (v2) than Python or python-hl7, though. I couldn't find a better Stack Exchange "community" to post this in.
What message types, segments should be used to record details of a surgical procedure. e.g. what procedure was done, when it started and ended, what the outcome was, where it took place, who the primary and assistant surgeons were, etc.
From what I've been able to find so far it seems some sort of ADT message (e.g. A04) with a PR1 segment should be used. Is this correct? Are there example messages?
Thanks.
EDIT: To be clear I am not looking for Python examples. I am looking for sample HL7 messages to get an idea of how other people do it. Of course clear and unambiguous documentation that says exactly what fields are required and exactly what they can/should contain would also help.
It's very unlikely that you can find a single message that covers everything related to surgery (ORU is the closest one). In general surgery is type of Act similar to other events that may happen with the patient. For that reason you need to decide what you are looking for since a “surgery” can be divided into admission/discharge/transfer (ADT), scheduling (SIU, OMS), patient summary (MDM), preoperative (MFN, ORU), intraoperative (ORU, DFT), postoperative (ORU, MDM, DFT, MFN).
Exact type and place of the surgery can be specified by ICD-10 or SNOMED CT.
Systems have to sometimes accommodate the possibility of real world bad data. Consider that some data originates with paper forms. And forms inherently have a limited means of validating data.
Example 1: On one form users are expected to enter an integer distance (in miles) into a blank. We capture the information as written as a string since we don't always end up getting integer values.
Example 2: On another form we capture a code. That code should map to one of the codes in our system. However, sometimes the code written on the form is incorrect. We capture the code and allow it to exist with an invalid value until some future time of resolution. That is, we temporarily allow bad data since it's important to record the record even if some of it is invalid.
I'm interested in learning more about how systems accommodate bad data, that is, human error. Databases are supposed to be bastions of data integrity, but the real world is messy and people make mistakes. Systems must allow us to reflect those mistakes.
What are some ways systems you've developed accommodate human error? What practices have you used? What lessons have you learned?
Any further reading on the topic? (I had trouble Googling it.)
I agree with you, whatever we do there's no guarantee that we can get rid of bad or incorrect data. Especially, but not only, if it comes to user input. In my experience the same problems exist in complex integration projects, in which you have to integrate and merge (often inconsistent) data retrieved from different systems.
A good strategy is to decouple the input from the operational system itself. First, place user (or external system) provided data in a separate datastore (e.g. different schema). In a second step load this data into your operational datastore, but only if it confirms to strict rules (e.g. use address verification software to verify a given address). This Extract, Transform, Load (ETL) approach is fairly common in Data Warehousing (DWH) solutions, but can be applied programmatically in transactional systems as well (in my experience).
The above approach often leads to asynchronous processes in which the input is subitted first and (maybe) at a later time the external entity (user or system) retrives feedback whether its data was correct or not.
EDIT: For further readings I recommend to have a look at DWH concepts. Alhtough, you may not want to build such a thing, you could partially apply those concepts:
http://en.wikipedia.org/wiki/Extract,_transform,_load
http://en.wikipedia.org/wiki/Data_warehouse
http://en.wikipedia.org/wiki/Data_cleansing
A government department I worked in does a lot of surveys, most of which are (were) still paper based.
All the results were OCR'd into the system.
As part of the OCR process a digital scan of the forms is kept.
Data is then validated, data that is undecipherable or which fails validation is flagged.
When a human operator reviews the digital data they can modify the data if they are confident that they can correctly interpret what the code could not; they (here's the cool bit) can also bring up the scan of the paper based original, and use that to determine what the user was trying to say.
On a different thread; at some point you want to validate the data coming in against any expected data ranges that you want it to conform to; buy rejecting it at the point of entry you give the user a chance to correct it - the trade off is that every time you reject it you increase the chance of them abandoning the whole process.
At some point in your system you need to specify the rules which will be used for validation. At the end of the day a system is only going to be as smart as those rules. You can develop these yourself into the code (probably the business logic) or you might use a 3rd party component.
having flexible control over the validation is pretty important as they are likely to change overtime.
To be honest with you, one point of migrating from paper-based systems to IT is to remove these errors and make sure all data is always correct. I doubt any correctly planned and developed IT system (especially business financial systems) would allow such errors. Not in the company I am working for anyway...
There are lots of software tools that address the kinds of problems you mention. There are platforms and tools that let you define rules for scrubbing and transforming data and handling validation errors. Those techniques are widely used for Data Integration and Business Intelligence applications. Google for "Data Quality" or "Data Integration".
The easiest thing to do is to (this is not always possible) design the interface where users enter the data to limit as much as possible the amount of text that they need to enter. In my experience this seems to be where a lot of problems come from. One simple example of this is to provide a select, or auto-complete select field
One thing that you could do is do everything possible to determine if the data is correct before going into the db. I try to give the user entering the data as much feedback as possible so they can (ideally) fix some of the issues before the data gets persisted. For example, it is a very quick check to determine if the data being entered is of the correct type.
I got started in legal systems before the PC era. Litigation support databases routinely have to accommodate factually incorrect, incomplete, and contradictory information. It takes a different way of thinking.
The short version . . .
Instead of recording a single fact, you record multiple assertions about a fact. It boils down to designing a database to store data from assertions like these.
In an interview at 2011-01-03 08:13, Neil Rimes told Officer Cane
that he was at home from 2011-01-02 20:00 until 2011-01-03 08:13.
In an interview at 2011-01-03 08:25, Liza Nevers told Officer Cane
that Neil Rimes came home at 2011-01-02 23:45.
In a deposition at 2011-05-13 10:22, Cody Maxon told attorney Kurt
Schlagel that he saw Neil Rimes at Kroger at 2011-01-03 03:00
I am looking for algorithms that allow text extraction from websites. I do not mean "strip html", or any of the hundreds of libraries that allow this.
So for example for a news article I would like to identify the heading and all the text, but not the comments section and so on.
Are there any algorithms for that out there? Thank you!
In computer science literature this problem is usually referred to as the page segmentation or boiler plate detection problem. See the report Boilerplate Detection using Shallow Text Features and its related blog post. Also, I have a few reports and software sites bookmarked that address the problem. Also, see this stackoverflow question.
there are a few open source tools available that do similar article extraction tasks.
https://github.com/jiminoc/goose which was open source by Gravity.com
It has info on the wiki as well as the source you can view. There are dozens of unit tests that show the text extracted from various articles.
"Content extraction" is a very difficult topic. There are no common standards to identify the "main-article" content (there are several approaches to make HTML easier readably for crawlers, e.g. schema.org, but none of these is very popularly used).
So it turns out, that if you want good results, its probably best to define your own XPath selectors for each (news) website you want to scrape. Although there are some APIs for HTML content extraction, but as I said its very hard to develop an algorithm which works for every site.
Some APIs you could use:
alchemyapi.com
diffbot.com
boilerpipe-web.appspot.com
aylien.com
textracto.com
What you're trying to do is called "content extraction". It turns out to be a surprisingly hard problem to solve well, and many naive solutions do quite badly.
Instapaper and Readability both have to solve this, and you may learn something from looking at their solutions. They also both provide services that you may be able to take advantage of - perhaps you can outsource your problem to them and let their API take care of it. :)
Failing that, a search for "html content extraction" returns a great deal of useful results, including a number of papers on the subject.
I compared a few different libraries, and had really great luck with Mozilla's Readability library (Node), or its Python wrapper.
For example, take this CNN article: https://edition.cnn.com/2022/06/01/tech/elon-musk-tesla-ends-work-from-home/index.html
Readability successfully returns only the relevant data:
New York (CNN Business) Elon Musk is demanding that Tesla office workers return to in-person work or leave the company. The policy, disclosed in leaked emails Musk sent to Tesla's executive staff Tuesday, was first reported by electric vehicle news site Electrek. "Anyone who wishes to do remote work must be in the office for a minimum (and I mean *minimum*) of 40 hours per week or depart Tesla. This is less than we ask of factory workers," Musk wrote, adding that the office must be the employee's primary workplace where the other workers they regularly interact with are based — "not a remote branch office unrelated to the job duties." Musk said he would personally review any request for exemption from the policy, but that for the most part, "If you don't show up, we will assume you have resigned."
etc.
I think your best shoot is study what information can you get from the metadata and write a good html parser, oEmbed could be a good standard =)
https://oembed.com/#section7
I know a little about SNMP, but not enough. I need to develop an application that can read standard SNMP MIBs and read/write the various properties. The network end is no problem, but the actual MIBs and exactly what they may contain is something of a black art to me.
I believe I should be able to use LIBSMI to 'parse' the MIBs, but I don't really understand what the output of the 'parser' is going to be, and how best to use it.
All suggestions welcome...
At the risk of throwing you in the deep end, you might want to take a look at net-snmp. The default installation contains a number of standard mibs with their associated implementation. It also contains a utility (mib2c) that will generate boilerplate code from your mib files.
Once you've read your way through a couple of mibs you should have no trouble familiarising yourself with the way that snmp does things.
Most standard MIB documents were defined in corresponding RFCs published at IETF.
http://www.ietf.org/
Please go to this site to find more details.
Muonics Mib Smithy User Guide provides a good overview of how to build a MIB, which can help you understand the necessary elements for parsing.
You should also look at the ASN.1 ITU specification X.690, because that is the language SNMP is defined in. It also helps if you scour the SNMP RFCs for any Bachus-Naur formatting in the SNMP RFCs. I'm going to suggest you start with RFC3642 and RFC2252.
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Is it even possible to perform address (physical, not e-mail) validation? It seems like the sheer number of address formats, even in the US alone, would make this a fairly difficult task. On the other hand it seems like a task that would be necessary for several business requirements.
Here's a free and sort of "outside the box" way to do it. Not 100% perfect, but it should reject blatantly non-existent addresses.
Submit the entire address to Google's geocoding web service. This service attempts to return the exact coordinates of the location you feed it, i.e. latitude and longitude.
In my experience if the address is invalid you will get a result of 602 from the service. There's definitely a possibility of false positives or false negatives, but used in conjunction with other consistency checks it could be useful.
(Yahoo's geocoding web service, on the other hand, will return the coordinates of the center of the town if the town exists but the rest of the address is bogus. Potentially useful as long as you pay close attention to the "precision" field in the result).
There are a number of good answers in here but most of them make the assumption that the user wants an "API" solution where they must write code to connect to a 3rd-party service and/or screen scrape the USPS. This is all well and good, but should be factored into the business requirements and costs associated with the implementation and then weighed against the desired benefits.
Depending upon the business requirements and the way that the data is received into the system, a real-time address processing solution may be the best bet. If a real-time solution is required, you will want to consider the license agreement and technical limitations of the Google Maps/Bing/Yahoo APIs. They typically limit the number of calls you can make each day. The USPS web tools API is the same in additional they restrict how/why you can use their system and how you are allowed to use the data thereafter.
At the same time, there are a handful of great service providers that can easily process a static list of addresses. Essentially, you give the service provider a CSV file or Excel file, they clean it up and get it back to you. It's a one-time deal with no long-term commitment or obligation—usually.
Full disclosure: I'm the founder of SmartyStreets. We do address verification for addresses within the United States. We are easily able to CASS certify a list and we also offer a address verification web service API. We have no hidden fees, contracts, or anything. You use our service until you no longer need it and you can walk away. (Unlike cell phone companies that require a contract.)
USPS has an address cleaner online, which someone has screen scraped into a poor man's webservice. However, if you're doing this often enough, it'd be a better idea to apply for a USPS account and call their own webservice.
I will refer you to my blog post - A lesson in address storage, I go into some of the techniques and algorithms used in the process of address validation. My key thought is "Don't be lazy with address storage, it will cause you nothing but headaches in the future!"
Also, there is another StackOverflow question that asks this question entitled How should international geographic addresses be stored in a relational database.
In the course of developing an in-house address verification service at a German company I used to work for I've come across a number of ways to tackle this issue. I'll do my best to sum up my findings below:
Free, Open Source Software
Clearly, the first approach anyone would take is an open-source one (like openstreetmap.org), which is never a bad idea. But whether or not you can really put this to good and reliable use depends very much on how much you need to rely on the results.
Addresses are an incredibly variable thing. Verifying U.S. addresses is not an easy task, but bearable, but once you're going for Europe, especially the U.K. with their extensive Postal Code system, the open-source approach will simply lack data.
Web Services / APIs
Enterprise-Class Software
Money gets it done, obviously. But not every business or developer can spend ~$0.15 per address lookup (that's $150 for 1,000 API requests) - a very expensive business model the vast majority of address validation APIs have implemented.
What I ended up integrating: streetlayer API
Since I was not willing to take on the programmatic approach of verifying address data manually I finally came to the conclusion that I was in need of an API with a price tag that would not make my boss want to fire me and still deliver solid and reliable international verification results.
Long story short, I ended up integrating an API built by apilayer, called "streetlayer API". I was easily convinced by a simple JSON integration, surprisingly accurate validation results and their developer-friendly pricing. Also, 100 requests/month are entirely free.
Hope this helps!
I have used the services of http://www.melissadata.com Their "address object" works very well. Its pricey, yes. But when you consider costs of writing your own solutions, the cost of dirty data in your application, returned mailers - lost sales, and the like - the costs can be justified.
For us-based address data my company has used GeoStan. It has bindings for C and Java (and we created a Perl binding). Note that it is a commercial product and isn't cheap. It is quite fast though (~300 addresses per second) and offers features like CASS certification (USPS bulk mail discount), DPV (Delivery point verification) flagging, and LON/LAT geocoding.
There is a Perl module Geo::PostalAddress, but it uses heuristics and doesn't have the other features mentioned for GeoStan.
Edit: some have mentioned 'doing it yourself', if you do decide to do this, a good source of information to start with is the US Census Tiger Data Set, which contains a lot of information about the US including address information.
As seen on reddit:
$address = urlencode('1600 Pennsylvania Avenue, Washington, DC');
$json = json_decode(file_get_contents("http://where.yahooapis.com/geocode?q=$address&flags=J"));
print_r($json);
Fixaddress.com service is available that provides following services,
1) Address Validation.
2) Address Correction.
3) Address spell correcting.
4) Correct addresses phonetic mistakes.
Fixaddress.com uses USPS and Tiger data as reference data.
For more detail visit below link,
http://www.fixaddress.com/
One area where address lookups have to be performed reliably is for VOIP E911 services. I know companies reliably using the following services for this:
Bandwidth.com 9-1-1 Access API MSAG Address Validation
MSAG = Master Street Address Guide
https://www.bandwidth.com/9-1-1/
SmartyStreet US Street Address API
https://smartystreets.com/docs/cloud/us-street-api
There are companies that provide this service. Service bureaus that deal with mass mailing will scrub an entire mailing list to that it's in the proper format, which results in a discount on postage. The USPS sells databases of address information that can be used to develop custom solutions. They also have lists of approved vendors who provide this kind of software and service.
There are some (but not many) packages that have APIs for hooking address validation into your software.
However, you're right that its a pretty nasty problem.
http://www.usps.com/ncsc/ziplookup/vendorslicensees.htm
As mentioned there are many services out there, if you are looking to truly validate the entire address then I highly recommend going with a Web Service type service to ensure that changes can quickly be recognized by your application.
In addition to the services listed above, webservice.net has this US Address Validation service. http://www.webservicex.net/WCF/ServiceDetails.aspx?SID=24
We have had success with Perfect Address.
Their database has all the US street names and street number ranges. Also acts as a pretty decent parser for free-form address fields, if you are lucky enough to have that kind of data.
Validating it is a valid address is one thing.
But if you're trying to validate a given person lives at a given address, your only almost-guarantee would be a test mail to the address, and even that is not certain if the person is organised or knows somebody at that address.
Otherwise people could just specify an arbitrary random address which they know exists and it would mean nothing to you.
The best you can do for immediate results is request the user send a photographed / scanned copy of the head of their bank statement or some other proof-of-recent-residence, because at least then they have to work harder to forget it, and forging said things show up easily with a basic level of image forensic analysis.
There is no global solution. For any given country it is at best rather tricky.
In the UK, the PostOffice controlls postal addresses, and can provide (at a cost) address information for validation purposes.
Government agencies also keep an extensive list of addresses, and these are centrally collated in the NLPG (National Land and Property Gazetteer).
Actually validating against these lists is very difficult. Most people don't even know exactly how their address as it is held by the PostOffice. Some businesses don't even know what number they are on a particular street.
Your best bet is to approach a company that specialises in this kind of thing.
Yahoo has also a Placemaker API. It is good only for locations but it has an universal id for all world locations.
It look that there is no standard in ISO list.
You could also try SAP's Data Quality solutions which are available in both a server platform is processing a large number of requests or as an embeddable SDK if you wanted to run it in process with your application. We use it in our application and it's very robust and scalable.
NAICS.com is coming out with an API that will add all kinds of key business data including street address. This would happen on the fly as your site's forms are processed. https://www.naics.com/business-intelligence-api/
You can try Pitney Bowes “IdentifyAddress” Api available at - https://identify.pitneybowes.com/
The service analyses and compares the input addresses against the known address databases around the world to output a standardized detail. It corrects addresses, adds missing postal information and formats it using the format preferred by the applicable postal authority. I also uses additional address databases so it can provide enhanced detail, including address quality, type of address, transliteration (such as from Chinese Kanji to Latin characters) and whether an address is validated to the premise/house number, street, or city level of reference information.
You will find a lot of samples and sdk available on the site and i found it extremely easy to integrate.
For US addresses you can require a valid state, and verify that the zip is valid. You could even check that the zip code is in the right state, but beyond that I don't think there are many tests you could run that wouldn't provide a lot of false negatives.
What are you trying to do -- prevent simple mistakes or enforcing some kind of identity check?