Lecture 01.ppt

Data Warehousing & DATA
MINING (SE-409)
Lecture-1
Introduction and Background
Dr. Huma Ayub
Software Engineering department
University of Engineering and Technology, Taxila

Course Books
– W. H. Inmon, Building the Data Warehouse
(Second Edition), John Wiley & Sons Inc., NY.
– Han, J. and Kamber, M. (2011) Data Mining Concepts
and Techniques, 3rd Edition, Morgan Kaufmann.
– Paulraj Ponniah, Data Warehousing Fundamentals,
John Wiley & Sons Inc., NY.

Summary of course
1. Introduction & Background
2. De-normalization
3. On Line Analytical Processing (OLAP)
4. Dimensional modeling
5. Extract – Transform – Load (ETL)
6. Data Quality Management (DQM)
7. Need for speed (Parallelism, Join and Indexing techniques)
8. Data Mining

Why this course?
• The world is changing (actually changed), either
change or be left behind.
• Missing the opportunities or going in the wrong
direction has prevented us from growing.
• What is the right direction?
• Joining the data, in a knowledge driven
economy.

The need
Knowledge is power, Intelligence
is absolute power!
“Drowning in data and starving
for information”

The need
DATA
INFORMATION
KNOWLEDGE
POWER
INTELLIGENCE
$

Historical overview
1960
Master Files & Reports
1965
Lots of Master files!
1970
Direct Access Memory & DBMS
1975
Online high performance transaction processing 

Historical overview
1980
PCs and 4GL Technology (MIS/DSS)
1985 & 1990
Extract programs, extract processing,
The legacy system’s web



Historical overview: Crisis of Credibility




 
What is the financial health of our company?
-10%
+10%

??

Why a Data Warehouse (DWH)?
• Data recording and storage is growing.
• History is excellent predictor of the future.
• Gives total view of the organization.
• Intelligent decision-support is required for
decision-making.

Reason-1: Why a Data Warehouse?
• Size of Data Sets are going up .
• Cost of data storage is coming down .
– The amount of data average business collects and
stores is doubling every year
– Total hardware and software cost to store and
manage 1 Mbyte of data
• 1990: ~ $15
• 2002: ~ ¢15 (Down 100 times)
• By 2007: < ¢1 (Down 150 times)

–A Few Examples
• WalMart: 24 TB
• France Telecom: ~ 100 TB
• CERN: Up to 20 PB by 2006
• Stanford Linear Accelerator Center (SLAC):
500TB

Caution!
A Warehouse of Data
is NOT a
Data Warehouse

Caution!
Size
is NOT
Everything

• Businesses demand Intelligence (BI).
–Complex questions from integrated data.
–“Intelligent Enterprise”

List of all items that were sold last
month?
List of all items purchased by Tariq
Majeed?
The total sales of the last month
grouped by branch?
How many sales transactions
occurred during the month of
January?
DBMS Approach

Which items sell together? Which
items to stock?
Where and how to place the items?
What discounts to offer?
How best to target customers to
increase sales at a branch?
Which customers are most likely to
respond to my next promotional
campaign, and why?
Intelligent Enterprise

• Businesses want much more…
– What happened?
– Why it happened?
– What will happen?
– What is happening?
– What do you want to happen?
Stages of
Data
Warehouse

What is a Data Warehouse?
A complete repository of historical
corporate data extracted from
transaction systems that is available
for ad-hoc access by knowledge
workers.

Complete repository
History
Transaction System
Ad-Hoc access
Knowledge workers

Transaction System
– Management Information System (MIS)
– Could be typed sheets (NOT transaction system)
Ad-Hoc access
– Dose not have a certain access pattern.
– Queries not known in advance.
– Difficult to write SQL in advance.
Knowledge workers
– Typically NOT IT literate (Executives, Analysts, Managers).
– NOT clerical workers.
– Decision makers.

Another View of a DWH
Subject
Oriented
Integrated
Time
Variant
Non
Volatile

What is a Data Warehouse ?
It is a blend of many technologies, the basic
concept being:
 Take all data from different operational systems.
 If necessary, add relevant data from industry.
 Transform all data and bring into a uniform format.
 Integrate all data as a single entity.

What is a Data Warehouse ? (Cont…)
It is a blend of many technologies, the basic
concept being:
Store data in a format supporting easy access for
decision support.
 Create performance enhancing indices.
 Implement performance enhancement joins.
 Run ad-hoc queries with low selectivity.

Business user
needs info
User requests
IT people
IT people
create reports
IT people
send reports to
business user
IT people do
system analysis
and design
Business user
may get answers
Answers result
in more questions

?
How is it Different from MIS?
 Fundamentally different

How is it Different?
• Different patterns of hardware utilization
100%
0%
Operational DWH
Bus Service vs. Train

• Combines operational and historical data.
 Don’t do data entry into a DWH, OLTP or ERP are the source
systems.
 OLTP systems don’t keep history, cant get balance statement
more than a year old.
 DWH keep historical data, even of bygone customers. Why?
 In the context of bank, want to know why the customer left?
 What were the events that led to his/her leaving? Why?
 Customer retention/holding.

How much history?
• Depends on:
– Industry.
– Cost of storing historical data.
– Economic value of historical data.

How much history?
• Industries and history
– Telecomm calls are much much more as compared to bank
transactions- 18 months.
– Retailers interested in analyzing yearly seasonal patterns- 65
weeks.
– Insurance companies want to do actuary analysis, use the
historical data in order to predict risk- 7 years.

How much history?
Economic value of data
Vs.
Storage cost
Data Warehouse a
complete repository of data?

• Usually (but not always) periodic or batch
updates rather than real-time.
 For an ATM, if update not in real-time, then lot of real
trouble.
 DWH is for strategic decision making based on historical
data. Wont hurt if transactions of last one hour/day are
absent.

 Rate of update depends on:
 volume of data,
 nature of business,
 cost of keeping historical data,
 benefit of keeping historical data.

Lecture 01.ppt

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