Data Mining and Data Warehousing - Kennesaw State University

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20 Νοε 2013 (πριν από 3 χρόνια και 6 μήνες)

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Data Mining

and

Data Warehousing


Micah Jennison, Katie Nannen, Edwin Pound, Bryan
Roberts, Orbert Rogers, Austin Gentry

Kennesaw State University

April 27, 2012

IS 2200

Dr. Cochran


Executive Summary


Our objective is to
illustrate how data
mining and
warehousing can
impact a large
business or
organization

What is Data Mining?


Data mining, or the extraction of information
from a large database or warehouse, is a
leading information gathering tool in today’s
highly technology oriented world.


Date mining allows companies to use
information to determine what target markets
they should focus on.


(Rajalakshmi, Purusothaman, 610), (Gaboury, 17)


Methods of Data Mining


Text Analytics
:
examining text data
in order to change it
into manageable
and more easy to
understand
information


On
-
line Analytical
Processing
: is the
most common way
considering how
much of the
research is now
automated and done
by computer
programs

(Rajalakshmi, Purusothaman, 610), (Gaboury, 17)


What is Data Warehousing?


A data warehouse is a type of on
-
line
analytical processing (OLAP).


In order for businesses to make timely
educated decisions, information needs to be
updated frequently and be easily accessible.


The use of warehoused information supports
real
-
time decision
-
making about an
enterprise’s day
-
to
-
day operations.

(Singh, Upadhyay, Yadav, 2011)

Tools for Date Warehousing


The date can be
stored as a
summary of the
transactions.


The data can be
stored by every
single transaction.

In each warehouse, the data can vary widely, all depending on the

focus of the organization and the kind of information being processed.




There are two methods for storing the data



When the data is summarized the transactions move at a faster
rate, however it could be lost much easier

(Exforsys, 2006)

Tools for Data Warehousing


Data visualization is the
abstract display of
information for two purposes:
sense
-
making and
communication.


Data visualization is
becoming quite common
within the business world,
especially within companies
containing graphic
departments.


A variety of ways have been
developed to conventionalize
data, these include pie
graphs, histograms, and
even tables.

(Few, 2010), (Friedman, 2007)

How Top Management Uses
The Data



In today’s business, correct information is very
important because it gives companies who have it
the competitive advantage.


Managers must use the data to suit their company.

(Madhu, Reddy, 2349)

Is It Economical For The
Companies?


Data warehousing and mining can be a costly
endeavor.


Over time, however, it
seems that the price of
data warehousing and
mining is declining. Since
data storage has become
cheaper, it is more
encouraging for
companies to begin to use
data warehousing.


With the internet’s
growing popularity, it is
becoming useful for
companies to have
information about their
customers.



That is where data
warehousing comes in,
companies are stating to
be willing to pay more
money for good services to
retain information.

(LGI Systems, 2012), (Alexander, 2012)

Privacy With Data Mining


Data Sanitization
:
which is when data
has been altered
before it is delivered
to the data miner so
the real values are
hidden.


Secure Multiparty
Computation
: which is
when data is sent to two
or more sites, and these
sites cooperate to learn
the global data mining
results without revealing
the data at their
individual sites.


Many companies are concerned with mining data and keeping it’s
customer’s sensitive information safe.


There are two ways to help their customer.

(Rajalakshmi, Purusothaman, 610)

Common Use of Data
Warehousing


An example of data warehousing that affects
most people today is any transaction you
make online. For example: online shopping.


When a customer buys something online,
companies use a “Dimensional Modeling
approach called ‘Star Schema’.


Star Schema: is when data takes a central fact,
which different dimensions stem from.

Using
these dimensions the necessary management
team can easily analyze data they need to make
informed decisions.

(Ravikumar, Manjunath, Hegadi, Archana, 3141)

Conclusion

In today’s marketplace data mining and data
warehousing are truly among the most
important tools a business can have at their
disposal. The information is often what gives
a business the power to succeed and create
a competitive advantage over their
competition, allowing informed decisions to
be made by those with the power to execute
them.

References


Alexander, D. (2012).
Data mining
. Retrieved from
http://www.laits.utexas.edu/~norman/BUS.FOR/course.mat/Alex/


Exforsys. (2006, August 18).
Data warehouse tools
. Retrieved from
http://www.exforsys.com/tutorials/data
-
warehousing/data
-
warehouse
-
tools.html


Few, S. (2010, September 16).
Data visualization for human perception
. Retrieved from
http://www.interaction
-
design.org/encyclopedia/data_visualization_for_human_perception.html


Friedman, V. (2007, August 2).
Data visualization: Modern approaches
. Retrieved from
http://www.smashingmagazine.com/2007/08/02/data
-
visualization
-
modern
-
approaches/



Gaboury, P. (1997). Data warehousing: A mine of business information.
Computing Japan
,
4
(7),
17. Retrieved from
http://web.ebscohost.com/ehost/detail?vid=29&hid=106&sid=7b18ddd0
-
8a77
-
4dd4
-
8a99
-
288eacc47ec9@sessionmgr14&bdata=JnNpdGU9ZWhvc3QtbGl2ZSZzY29wZT1zaXRl


LGI Systems. (2012).
The data warehousing information center
. Retrieved from
http://www.dwinfocenter.org/against.html



M. Rajalakshmi; T. Purusothaman, Privacy Preserving Distributed Data Mining using
Randomized Site Selection European Journal of Scientific Research, ISSN 1450
-
216X Vol.64
No.4 (2011), pp. 610
-
624


EuroJournals Publishing, Inc.2011
http://www.europeanjournalofscientificresearch.com


Madhu, G., & Reddy, G. S. (2010). Hypothetical description for intelligent data mining.
International Journal on Computer Science and Engineering
,
2
(7), 2349
-
2352.


Ravikumar, G. K.; Manjunath, T. N.; Hegadi, Ravindra S.; Archana, R. A. International Journal
of Engineering Science and Technology, 2011, Vol. 3 Issue 4 p3141
-
3152, 12p


SINGH, AJIT; UPADHYAY, D. C.; YADAV, HEMANT.
The Analytical
Data

Warehouse: A
sustainable approach for empowering institutional Decision Making
, International Journal of
Engineering Science & Technology, 2011, Vol. 3 Issue 7, p6049
-
6057, 9p, 1 Diagram