Warehousing
1. Data organized by detailed subject with information relevant for decision
support
2.Integrated data
3.Time-variant data
4.Non-volatile data
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Mining, Querying and Analysis
Online Analytical processing OLAP
– DSS and EIS computing done by end-users in online systems
– Versus online transaction processing OLTP
OLAP Activities
– Generating queries – Requesting ad hoc reports
– Conducting statistical analyses – Building multimedia applications
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson Copyright 1998, Prentice Hall, Upper Saddle River, NJ
and a
set of tools
, usually
with multidimensional
capabilities
Query tools
Spreadsheets
Data mining tools
Data visualization tools
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Data Sources
I nternal Data
Sources
E xternal Data
Sources Data
Acquisition, E xtraction,
Delivery Transformation
Data Warehouse
Business Communication
Querying Report
Generation
Spreadsheet F orecasting
Analysis M odeling
M ultimedia E I S,
Others
Online Analytical Processing Data
P resentation and
Visualization
FIGURE 4.1 Data Warehousing and Online Analytical Processing OLAP.
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson Copyright 1998, Prentice Hall, Upper Saddle River, NJ
DSS In Focus 4.8: Database Queries
Managers may ask many questions from a computer. Here are selected representative questions that were used as benchmarks to test DSS query software by
Corporate Computing August 1992.
Query Group 1--Phone Number Queries List the telephone numbers of the contacts at Sand Energy, particularly if this number is
different from the company number or is missing. Otherwise list the main company phone number.
Query Group 2--Product Queries List the number of units of each product that Sand Energy Company has ordered.
Query Group 3--Financial Queries List the product that is part of the largest order and that is also the product most
commonly ordered.
Query Group 4--Periodic Queries Generate a cross-tabular report of the revenues per ordering customer per product in 1992.
Query Group 5--Graphing Queries Create a pie chart that shows total dollar sales to top five customers separately, and groups
total dollar sales for all other customers.
Query Group 6--Reporting Queries Generate an order report for the latest order placed by Sand Energy Company. Include:
todays date; company name; order information; line item information; total dollar amount.
Source: Condensed from Corporate Computing, August 1992.
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Using SQL
for Querying
SQL Structured Query Language
Data language English-like, nonprocedural, very user
friendly language Free format
Example: SELECT
Name, Salary FROM
Employees WHERE
Salary 2000
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson Copyright 1998, Prentice Hall, Upper Saddle River, NJ
DSS In Focus 4.9: Sam pler of SQL
Statem ents
N a
tu ra
l La n
g u
a g
e S
Q L
Lis t o
f a ll p
u rch
a s
e s
o f L.B
. U n
iv e
rs ity
s in
ce Ja
n u
a ry
o f 1
9 9
6 , in
te rm
s o
f p ro
d u
cts ,
p rice
s , a
n d
q u
a n
titie s
S ELECT P
RO D
U CTS
P U
RCH P
RICE Q U
A N
TITY FR O
M P
U RCH
A S
E-H IS
T W H
ERE CU
S T-N
A M
E EQ L.B
. U
N IV
ERS ITY AN
D P
U RCH
- D
A TE G
E 0 1
1 9
6
Lis t th
e p
rice o
f co tto
n s
h irts
, m e
d iu
m s
ize ,
w ith
s h
o rt s
le e
v e
s a
n d
w h
ite co
lo r
S ELECT P
RICE, A M
O U
N T-
A V
AIL FRO M
P RO
D U
CT W
H ERE P
RO D
-N A
M E EQ
CO TTO
N S
H IRT A
N D
S IZE
EQ M
ED IU
M A
N D
S TYLE
EQ S
H O
RT S LEEV
ES A
N D
CO LO
R EQ W
H ITE
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Data Mining
For
Knowledge discovery in databases
Knowledge extraction
Data archeology
Data exploration
Data pattern processing
Data dredging
Information harvesting
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Characteristics and
Objectives
Data are often buried deep
Clientserver architecture
Sophisticated new tools--including advanced visualization tools--help to remove the information “ore”
Massaging and synchronizing data
Usefulness of “soft” data
End-user minor is empowered by “data drills” and other power query tools with little or no programming skills
Often involves finding unexpected results
Tools are easily combined with spreadsheets etc.
Parallel processing for data mining
Example in Figure 4.4
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Areas
Marketing
Banking:
Retailing and sales
Manufacturing and production
Brokerage and securities trading
Insurance
Computer hardware and software
Government and defense
Airlines
Health care
Broadcasting
Law Enforcement
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Multidimensionality
Data Visualization Technologies
Digital images
Geographic information systems
Graphical user interfaces
Multidimensions
Tables and graphs
Virtual reality
Presentations
Animation
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson Copyright 1998, Prentice Hall, Upper Saddle River, NJ
DSS In Action 4.11: Data Visualization
To p
re v
e n
t s y
s te
m s
fro m
a u
to m
a tica
lly id
e n
tify in
g m
e a
n in
g le
s s
p a
tte rn
s in
d a
ta , CFO
s w
a n
t to m
a k
e s
u re
th a
t th e
p ro
ce s
s in
g p
o w
e r o
f a co
m p
u te
r is a
lw a
y s
te m
p e
re d
w ith
th a
t o f th
e in
s ig
h t o
f a h
u m
a n
b e
in g
. O n
e w
a y
to d
o th
a t is
th ro
u g
h d
a ta
v is
u a
liza tio
n , w
h ich
u s
e s
co lo
r, fo rm
Kategori