Searching for a useful online tool to check certain card ranges for scheme, brand, issuer, country etc parameters; I came across:-
https://lookup.binlist.net/,
Sample use case:-
https://lookup.binlist.net/411111
Referenced also at:-
https://opendata.stackexchange.com/questions/3930/credit-card-metadata-database
Wondering have coders working on EMV and related FinTech areas come across this resource? Any feedback on accuracy / usefulness in detecting card types / originating country/acquirer etc., would be appreciated.
FWIW, it did gave reasonably accurate (though not uniformly populated) responses on the live cards in my wallet.
I have used the resource but it's not very detailed for many bin ranges especially for African ones where I come from. But at least it tells you the country where the BIN is registered.
Related
I'm trying to make a mobile AR application. I want to track the user's handwriting in real time using smartphones while the user writes on paper. That is to track every stroke made by the user.
I know some SDKs and products like ManoMotion and Leap Motion provide relatively precise hand tracking and analysis, but since writing on paper doesn't involve many motions and gestures, I don't think they are suitable.
I have searched online and haven't found any resources for my particular use case. So I would like to ask if there are other resources I should take a look at, or I should rely on some lower level APIs like OpenCV.
mhc, kind of late to answer this. But regarding ManoMotion you need to apply for access to the SDK products through the official website at www.manomotion.com the approval process is manual and they require some information regarding the intention of use in a project so I highly suggest to provide with as much detail as possible.
I'm researching Medical Data set which includes variable concerning illnesses and treatment type.
For example illnesses is colon cancer, it's decision variables (x,y,z,t) and treatment type is chemothreapy, radiothreaphy etc etc.
I want to reach such a data set for my KDD and exploratory lesson. Because I want to make useful project prototype.
if you know any data set web site pls share me (so-called site may not include medical)..
There is a standard machine learning data set repository at UC Irvine. R users can access it via the mlbench package from the CRAN network.
Try the UCI Machine Learning Repository. 189 sample ML datasets, many medical in nature.
Because the site is focussed on ML, it gives a lot of guidance that will help choose and tune your ML algorithms for good generalization performance.
Not sure this is an actual 'programming' question, strictly speaking. However, given that programs work on data, I'll go with it - and observe that the term 'medical dataset' returns quite a few (1.7m) hits in Google.
I can find the technical explanation of what data mining is in a book or on Wikipedia, but I'm wondering what sort of development does it exactly involve? Is it more about using tools or more about writing tools? Is it really any much different from other domains when it comes to R&D?
Data Mining is the process of discovering interesting patterns in large amounts of data. It is not querying data, which is just what user Treb describes (sorry Treb).
To understand DM from a developer's perspective, you should read the book Programming Collective Intelligence by Toby Segaran.
In my experience (I'm a former data miner :-)), it's a mixture of using tools and writing tools. A lot of the time, the tools you need to analyse the particular data set don't exist, so you have to write them yourself first. It can be very interesting but you often need quite a different approach to the sort of programming I do now (embedded wireless), for example.
You really ought to change the accepted answer on this question so it doesn't mislead those who come across it.
Saying that querying a database IS data mining because "[h]ow would you discover any pattern in your data without querying first?" is like saying opening your car door is driving because "how else would you be able to drive somewhere without opening the car door first."
You can read your data out of a text file if you want. My first data mining assignment used data sets from the UCI repository and those are almost all text files.
If you want to learn about data mining start by looking up clustering and classification. Learn about decision trees and rule based classification. Then look at k-nearest-neighbor and k-means. After that if you really want to see what data mining is all about look at Chameleon, DBScan, and Support Vector Machines. Don't necessarily learn the minutiae of the last three (they're pretty complex and math heavy) but understanding the abstract idea of what happens will tell you all you need to know in order to use the many tools and libraries that are available for each strategy.
These are only the algorithms that popped into my head just now. There are so many others that I don't recall or don't even know yet.
Data mining is about searching large quantities of data for hidden patterns. Web 2.0 example: News corp uses its site myspace.com as a large data mine to determine what movies and products to promote. They write software to identify trends in the data that it's users post to the site. News corp does this to gather information useful for advertising campaigns and market predictions. It's different from other domains of R&D in that from a data givers perspective its passive. Rather than going out on the street and asking people in person what movies they are likely to see this summer and other such questions, the data mining tools sort out these things by analyzing data given by users voluntarily.
Wikipedia actually does have a pretty good article on it:
- http://en.wikipedia.org/wiki/Data_mining
Data Mining as I say is finding patterns or trends from given data. A developer perspective might be in applications like Anti Money Laundring... Where given a pattern you will search data for that given pattern. One other use is in Projection Softwares... where you project a result or outcome in future against a heuristic by studying recognizing the current trend from data.
I think it's more about using off the shelf tools rather than developing your own. An academic example of that kind of tools might be WEKA. Of course, you still have to know what algorithms use, how to preprocess data (very important this part), etc.
In R&D I don't have much idea, but it should be like almost everything: maths, statistics, more maths...
On the development level, data mining is just another database application, but with a huge amount of data.
The mining itself is done by running specific queries on the database. It's in the creation of the queries where the important work is done. They of course depend on the data model, and on the hypotheses, what sort of trends the customer expects to find.
Therefore, the fine tuning of the queries usually can't be done in development, but only once the system is live and you have live data. Then the user can test his hypotheses and adapt the queries to show him the trends he is looking for.
So from a dev point of view, data maining is about
Managing large sets of data in your client (one query may return 100.000 rows of data)
Providing the user (who may know nothing about SQL or relational databases in general) with an effective way to modify his queries and view the results.
Are there any online repositories of completed real-world projects with their timescales that I can use to callibrate my own development time estimates?
If such a repository existed, how would you expect to correlate your project to find matches?
To expand; every software development project has unique aspects - particularly with regard to project participants - and will therefore have unique dynamics that affect estimates, possibly by orders of magnitude.
To apply past project metrics to future projects and hope they hold up you would need to assume a few things;
Developers are interchangeable (they
aren't)
Building software is like building a house or a brick wall (it isn't)
Project risks are negligible (maybe you'll get lucky?)
Finally, if all you need is a ball-park number then isn't "calibration" overkill? Just ask your most experienced developer how long they think - they are usually in the ball park.
I don't think relating your project to a set of generic other projects to determine a time est. would be hugely beneficial. You can compare to other projects for defect rates - here's a good starting point for that: http://www.scribd.com/doc/7758538/Capers-Jones-Software-Quality-in-2008
My suggestion would be to look at prior projects in your company with the same relative technology and resources and develop a table (via function points) and then a continual resync. If there is no prior info and/or the technology is new and/or the resources are different - then it's best to use the team's prior experience from prior jobs.
Here's a good book: Software Estimation: Demystifying the Black Art
Do you use a wiki in your company? Who uses it and what for. Do you share information between projects / teams / departments or not?
We use ours to store
Coding Style docs
Setup and Deployment procedures for web servers and sites
Network diagrams (what are all the servers in Dev, Staging, QA and Production called etc.)
Project docs (pdfs, visios, excel, docs, etc.) are stored in SVN. For the non-techies we have links to those docs in the wiki that point to an up-to-date share on my box. (tip: some wikis provide source control integration but ours doesn't)
Installation and Setup procedures for development tools
Howto's on things like using our bug tracking system, our unit testing philosophy
When doing research on a topic I often capture the important information in a wiki page for others to learn from
I've seen them used to keep seating charts in medium to large size organizations for the new people
At my previous company all of the emergency contacts and procedures for handling a critical outage where available on the front page of the wiki
The best part about a wiki is that it's searchable. Some wiki's support searching inside uploaded or linked docs as well.
If you setup a wiki and encourage or even require people to use it the amount of information that will accumulate can be amazing. It's definately worth the effort especially if you have someone in IT with some spare time on their hands to set it up.
Do you use a wiki in your company?
= We use it for the purpose of a Knowlede Based. Basically it is a wiki but many more functionalities intagrated.
Who uses it and what for
= Employees. Knowledge Sharing, Preparation of collaborative-documents, etc.
Do you share information between projects / teams / departments or not?
= Depends on the requirements. It is possible to set permissions between users.
We use a wiki, for documenting our systems. It's updated gradually as things update and evolve. It should go without saying that there's benefit in that, however whether you use a wiki or other methods is worth thinking about.
A wiki is great for collarborative editing. The information shouldn't go stale in theory, because as people use the systems they have the opportunity to keep it up to date.
However we have found in our organisation that people struggle a little with wiki markup. Especially tables. I think a solution that has wysiwyg editing would be better if you have non-highly-technical people editing it. Sharepoint springs to mind, but it's expensive.
I use a wiki as my virtual "story wall" for agile development. All of my stories are written and organized in the wiki. While my customers are reasonably local (we can have face-to-face meetings), they aren't co-located. To enable better customer interaction I've resorted to a wiki instead of a wall-based story tracking mechanism. It also works a little better for me due to the fact that I often have multiple, concurrent projects and limited wall space in my cube. In a larger team with more focused projects and more wall area, I'm not sure I'd make the same choice.
My company uses a wiki for project-planing but also for storing documentation and ideas.
I have found that a wiki is a great way to link the programmers in the company with the business-people.
When someone who are not on the programming-team comes up with an idea or finds a bug, it's a loot simpler to let that person document it in the wiki.
I think it's an important aspect for a small company like mine to easily synchronize the business-team with the development-team. A wiki helps with that, since it gives the feeling of being a part of the development process, instead of having to ask the programmer directly about every little detail.
we have MediaWiki to store technical information that is not ready to be published in other formats - specification drafts, diagrams (via GraphViz extension), results of short investigations, etc.
I also think this question is a wiki too :)