On 12/27/2009 7:46 AM, Shawn Milochik 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.
Another nice thing is that, depending on how much time you want to spend on it,
you can have the students refine the matching rules over time, and see how
those rules effect the match rate and the processing time. On the downside,
your class will not have the joy of being taught the 'ideal solution' to this
problem at the end; if you come up with that, you'll be able to go into
business and make millions of dollars a year. ^_^
IMHO, that's a poor example. Rather than writing a fuzzy search
algorithm, it's easier to write a normalizer before entering data to the
index (or before comparing the search string with the corpus' string).
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