Simple distributed example for learning purposes?
cmpython at gmail.com
Thu Jan 14 23:29:35 CET 2010
On Dec 26 2009, 3:46 pm, Shawn Milochik <sh... at milochik.com> wrote:
> The special features of the Shrek DVD showed how the rendering took so much processing power that everyone's workstation was used overnight as a rendering farm. Some kind of video rendering would make a great example. However, it might be a lot of overhead for you to set up, unless you can find someone with expertise in the area. The nice thing about this is that it would be relevant to the audience. Also, if you describe what goes into processing a single frame in enough depth that they appreciate it, they'll really "get" the power of distributed processing.
> Something else incredibly time-expensive but much easier to set up would be matching of names and addresses. I worked at a company where this was, at its very core, the primary function of the business model. Considering the different ways of entering simple data, many comparisons must be made. This takes a lot of time, and even then the match rates aren't necessarily going to be very high.
> Here are some problems with matching:
> Bill versus William
> '52 10th Street' | '52 tenth street'
> 'E. Smith street' | 'E smith street' | 'east smith street'
> 'Bill Smith' | 'Smith, Bill'
> 'William Smith Jr' | 'William Smith Junior'
> 'Dr. W. Smith' | 'William Smith'
> 'Michael Norman Smith' | 'Michael N. Smith' | 'Michael Smith' | 'Smith, Michael' | 'Smith, Michael N.' | 'Smith, Michael Norman'
> The list goes on and on, ad nauseum. Not to mention geocoding, married and maiden names, and scoring partial name matches with distance proximity matches.
I'm not sure I understand the task. Based on another comment in this
thread, the idea seems to be that the company that does this matching
work is handed a big data set, and then these sorts of matching
comparisons are made on it--but what is the goal?
I can understand with address matches, in that 'east smith street'
should be recognized as the official postal address 'E. Smith Street',
so that the company can send correspondence to the correct address.
Is that the idea?
But what about the names? If it says 'Michael Norman Smith' as the
name, or 'Michael N. Smith' or 'Smith, Michael', can't one then just
use that on the correspondence? Why do you need to match 'Michael
Norman Smith' to 'Michael N. Smith' to discover they are the same
person? Is it that those two variations appear in the same data set
and you want to make sure you don't mail twice to the same person?
I completely accept that this is a real problem, I just don't yet
understand the goal all that well. Thanks.
More information about the Python-list