X Informatics Unit 3 Data Deluge 7 Business II


Hello, first, some reminders
of things we did before. The key ideas or
concepts in the field are data, which is the raw bits and
bytes produced by instruments, or gathered from the web, or
e-mail, and social media sites. This information,
which is the cleaned up data, but without any deep
processing applied to it. And then we have something which
is called knowledge, wisdom and decisions, which come from
analyzing the information. There are great
barriers between data, or even raw data, data information,
knowledge, wisdom and decisions. But the general idea that we go from the raw bits and
bytes through a variety of filters. Each time adding value and
usually reducing size, but not always is
important in all areas. We have the field of data analytics,
which is the process of converting data to information or
information to knowledge. Then there’s the field that studies
the transformations of filters that convert the forms of data
into a higher value form. Finally, we have data science,
which describes the whole process. And we have X-Informatics, which
is the actual application area for which we’re doing this. And, of course,
X refers to the different fields. As we said last time, the course in
one sentence is studying clouds, running data analytics programs,
processing big data to solve issues or problems in
the field of X-Informatics. And as discussed, we will have the
following structure of the course, which will be this initial
survey followed by use cases, which study big data in
various application areas. And we will possibly use Python in some of
the applications, not all of them. But the language used in the course
whenever it is used will be Python. And sometimes we will also use
online resources to explore different areas. And we will actually survey
some use cases as a sort of project in research in that area. The last slide from the previous
lecture is given here just to bring us back to square one. That is the slide where we
pointed out that an interesting area of big data, which comes
from the Internet of Things. Broad area,
which is the monitors on engines. And this came from a presentation
from General Electric. And map that points
out that the amount of data in this area is very large. And then I don’t discuss on
what they do or they could do. But they monitor in real time their
entire set of 25,000 engines. Gathering that data,
looking for anomalies, and taking preventive action
when the planes land. So now we continue
Big Data in Business. And these are set of slides
I gathered from the web just to summarize that area. The first slide is one
from a speaker in SAS, which points out that the issue for statisticians, which is, Which have opportunities,
which are gonna building these integrated software
solutions for business problems. So at least applying
the statistics part of that. And they identify various
applications, fraud detection, credit risk, credit scoring,
analysis of warranty data. Customer attention that’s
in analyzing what causes people to leave or
not leave their phone subscription. And then the important area of
optimizing pricing in the retail business. Now we come to a set of slides
coming from a talk from Williams from eBay. And the first slide is,
A statement as to what eBay does. It mainly sells Mustangs every
49 minutes, cell phones every 5 seconds, and shoes every 6
seconds for the fancy shoes. Here’s some statistics on the actual
volumes of activity at eBay. They have a 108 million or
more than that of active buyers and sellers worldwide. 250 million queries to
their search engine. 350 million live global listings. They have 20 petabytes of data. And they use a mix of Hadoop,
which is MapReduce, and more traditional database
using Teradata solutions. And they have 2 billion
page views every day. And 75 billion database
calls every day. So here’s eBay’s view as to why
big data is transformational. In this regard, I was at
a meeting at NIST, January 15. And at that meeting, it was stated that big data
is the killer application for clouds, which is
consistent with this view. The big data analyzed, of course,
on clouds, is a transformation or a disruptive technology,
which is changing many fields. And here, eBay points out that big
data tells you about patterns, which will give an example
of anomalies and outliers. It generalizes microtransactions,
the broad principles. It gives you predictions and
holistic customer picture. And it has
many applications at eBay. From system performance, fraud
detection, predicting purchases, customer support,
product development, and so on.

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