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Data Warehousing
& Data Mining
A review of Data Warehousing and Data Mining
Aqeel
Al-Jishi
Nick Farley
Masaki Osada
Data Warehousing Defined:

Data Warehouse –



The main repository of an organization
historical data, it corporate memory
It contains the raw material for management’s
decision support system
Data Mining – (Knowledge Discovery)

The process of analyzing data from different
perspectives and summarizing it into useful
information
History



In the late 1980s to early 1990s distinct computer
databases were created
These databases were designed to meet the data
analysis needs that Operational Systems were
failing to support
Operational Systems failed for many reasons:




Long report generation time
Inability to handle loads and not optimized
Many organizations had multiple Operation Systems
which was a reporting nightmare
Custom applications were required for reporting
which slowed reporting and increased costs
Goals of Data Warehousing






Make information easily accessible
Provide endless views and combinations
of data (Slicing & Dicing)
Query results returned with minimal wait
time
Be adaptive and resilient to change
Designed with the correct users in mind
(business users and management)
Keep information secure but allow access
to insiders
Common Issues



Problems with Data Acquisition may arise
80% of the time building a data
warehouse will be spent on extracting,
cleaning, and loading data
Errors with data can be rampant:



Incomplete Data (missing fields)
Incorrect Data (wrong calculations)
Readability Issues (strange formatting)
Differing Design Views, Relational

Relational Model - Bill Inmon



The data warehouse is but one part of
the Business Intelligence system
An enterprise has one data warehouse
and data marts source their
information from it.
Uses 3rd Normal Form to store
information in the database
Differing Design Views, Dimensional

Dimensional model - Ralph Kimball



Data warehouse is the conglomerate of
all data marts within the enterprise
Uses Star or Snowflake schema to
emulate a multi-dimensional database
Overall information is NOT normalized
in the database
Terminology

Data Mart –


A data mart is a subset of data from the data
warehouse, typically used when the broad scope of
the data warehouse isn’t needed
Business departments commonly create, use, and
alter their own data marts.

Level of Granularity –

The amount and level of data brought in to the data
warehouse during acquisition
Dimensional Model

Dimension Table –


A table with a single-part primary key
and descriptive attribute columns.
Describes the business entities of an
enterprise, represented as hierarchical,
categorical information such as time,
departments, locations, and products
Dimensional Model

Fact Table –


A table with numeric performance
measures (metrics) characterized by a
composite key
The elements for the composite key
come from the foreign keys from a
dimensional table
What is Data Mining?

Basic Concept


Getting “Useful Information” out of a
large amount of “Data”
In Business World

Getting “Business Intelligence” out of a
large amount of “Information”

What is the difference between
“Business Intelligence” and
“Information”?
Evolutionary
Step
Business
Question
Enabling
Technologies
Characteristics
Data Collection
(1960s)
“What was my total Computers, tapes, Retrospective,
revenue in the last disks
static data
five years?”
delivery
Data Access
(1980s)
“What were unit
sales in New
England last
March?”
Relational
Databases,
Structured Query
Language
Retrospective,
dynamic data
delivery at record
level
Data
Warehousing &
Decision Support
(1990s)
“What were unit
sales in New
England last
March? Drill down
to Boston.”
On-line analytic
processing,
multidimensional
databases
Retrospective,
dynamic data
delivery at
multiple levels
Data Mining
(Emerging
Today)
“What’s likely to
happen to Boston
unit sales next
month? Why?”
Advanced
algorithms,
multiprocessor
computers,
massive
databases
Prospective,
proactive
information
delivery
Focus of Data Mining

Predict the future trends and
behaviors using the past information

Prospective analysis
Uniqueness of Data Mining

Heuristic in nature

Capable of finding hidden patterns
users would never think of


Modeling is the key technology to find
patterns
Self-guiding
An example of Modeling
Yesterday
Today
Tomorrow
Static
Information
Known
Known
Known
Dynamic
Information
Known
Known
Target
Build a model based on known combinations
of static and dynamic information to see if it
indicates the result of a dynamic information
in the future.
Real world examples of Data Mining


“Diaper and Beer”
VISA


Blockbuster Entertainment


Personalized Recommendation
Suppliers to Wal-mart


Fraud Detection
Sales prediction
NBA Teams

Strategy analysis
Data Warehouse
Data Mart
ERP
DM
ERP
SCM
DM
CRM
DM
Questions?
Sources
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BOOKS:
Mastering Data Warehouse Design, Relational and Dimensional Techniques
Claudia Imhoff
Nicholas Galemmo
Jonathan G Geiger
© 2003, Wiley Publishing, Inc. Indianapolis, Indiana
The Data Warehouse Toolkit second edition, The Complete Guide to Dimensional Modeling
Ralph Kimball
Margy Ross
© 2002, Wiley and Songs, Inc. New York, NY
Statistical Data Mining and Knowledge Discovery
Edited by Hamparsum Bozdogan
© 2004 CRC Press LLC, Boca Raton, Florida
Data Mining and Business Intelligence: A Guide to Productivity
Stephan Kudyba and Richard Hoptroff
© 2001 Idea Group Publishing, London, England
Modern Systems Analysis and Design, fourth edition
Jeffrey A. Hoffer, et al
© 2005, 2002 by Pearson Education, Inc. Upper Saddle River, New Jersey
Database Systems An Application-Oriented Approach
Michael Kifer, Arthur Bernstein, Philip Lewis
© 2005 Pearson Education, Inc., New York, New York
ELECTRONIC:
http://www.1keydata.com/datawarehousing/inmon-kimball.html
http://www.fortunecity.com/skyscraper/oracle/699/orahtml/dbmsmag/9807d05.html
http://en.wikipedia.org/wiki/Data_warehouse
http://en.wikipedia.org/wiki/Dimension_(data_warehouse)
http://en.wikipedia.org/wiki/Data_mining
http://www.anderson.ucla.edu/faculty/jason.frand/teacher/technologies/palace/datamining.htm
http://www.dwinfocenter.org
http://www.thearling.com/text/dmwhite/dmwhite.htm
http://www.learndatamodeling.com/dimension.htm
http://www.learndatamodeling.com/fact.htm