Data Mining Course IT449
Text Book
Data Mining:
Concepts and Techniques
3rd Edition
Chapter 4 : Data
Warehousing and On-line
Analytical Processing
1
Data Mining:
Concepts and Techniques
(3rd ed.)
— Chapter 4 —
Jiawei Han, Micheline Kamber, and Jian Pei
©2011 Han, Kamber & Pei. All rights reserved.
2
Chapter 4: Data Warehousing(DW) and On-line
Analytical Processing
n
Data Warehouse: Basic Concepts
n
Data Warehouse Modeling: Data Cube and OLAP
n
Data Warehouse Design and Usage
n
Data Warehouse Implementation
n
Data Generalization by Attribute-Oriented Induction
n
Additional materials
n
Summary
4
What is a Data Warehouse?
n
Defined in many different ways, but not rigorously.
n
A decision support database that is maintained separately from
the organization’s operational database
n
Support information processing by providing a solid platform of
consolidated, historical data for analysis.
n
“A data warehouse is a subject-oriented, integrated, time-variant,
and nonvolatile collection of data in support of management’s
decision-making process.”—W. H. Inmon
n
Data warehousing:
n
The process of constructing and using data warehouses
5
Barry Devlin, Definitions of DW
A single, complete and
consistent store of data
obtained from a variety of
different sources made
available to end users in a
what they can understand
and use in a business
context.
6
Other DW Definition
Info/knowledge
A process of transforming
data into information and
making it available to
users in a timely enough
manner to make a
difference, it is suitable
environment for
knowledge mining
Data
7
Data mining and OLAP
OLAP: Online Analytical Processing
n
n
n
n
n
OLAP (on dataware house) is a data summarization/aggregation tool
that helps simplify data analysis.
OLAP performs multidimensional analysis of business data and
provides the capability for complex calculations, trend analysis, and
sophisticated data modeling.
Data mining involves more automated and deeper analysis than
OLAP. It is expected to have broader applications.
Data mining can help business managers find and reach more
suitable customers, as well as gain critical business insights that may
help drive market share and raise profits.
OLAP is a data summarization/aggregation tool that helps simplify
data analysis.
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Data Mining: Concepts and Techniques
8
Data Warehouse
n
“A data warehouse is a subject-oriented, integrated, time-variant,
and nonvolatile collection of data in support of management’s
decision-making process.”—W. H. Inmon
9
Data Warehouse—Subject-Oriented
n
Organized around major subjects, such as customer,
product, sales
n
Focusing on the modeling and analysis of data for
decision makers, not on daily operations or transaction
processing
n
Provide a simple and concise view around particular
subject issues by excluding data that are not useful in
the decision support process
10
Cont-Data Warehouse—Subject-Oriented
Application-Orientation
Operational
Database
Loans
Credit
Card
Trust
Subject-Orientation
G
Customer
Product
Savings
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Data
Warehouse
Vendor
Activity
Data Mining: Concepts and Techniques
11
Data Warehouse—Integrated
n
n
Constructed by integrating multiple, heterogeneous data
sources
n relational databases, flat files, on-line transaction
records
Data cleaning and data integration techniques are
applied.
n Ensure consistency in naming conventions, encoding
structures, attribute measures, etc. among different
data sources
12
Data Warehouse—Time Variant
n
The time horizon for the data warehouse is significantly
longer than that of operational systems
n
n
n
Operational database: current value data no deal of in
Data warehouse data: provide information from a
historical perspective (e.g., past 5-10 years), static
data
element of tin
Every key structure in the data warehouse
n
n
Contains an element of time, explicitly or implicitly
But the key of operational data may or may not
contain “time element”
13
Data Warehouse—Nonvolatile
n
A physically separate store of data transformed from the
operational environment
n
Operational update of data does not occur in the data
warehouse environment
n
Does not require transaction processing, recovery,
and concurrency control mechanisms
n
Requires only two operations in data accessing:
n
initial loading of data and access of data
14
OLTP (on database) vs. OLAP (on data warehouse)
OLTP
OLAP
users
clerk, IT professional
knowledge worker
function
day to day operations
decision support
DB design
application-oriented
subject-oriented
data
historical,
summarized, multidimensional
integrated, consolidated
lots of scans
unit of work
current, up-to-date
detailed, flat relational
isolated
read/write
index/hash on prim. key
short, simple transaction
# records accessed
tens
millions
#users
thousands
hundreds
DB size
100MB-GB
100GB-TB
metric
transaction throughput
query throughput, response
access
complex query
15
Data Warehouse: A Multi-Tiered Architecture
Metadata
Other
sources
Operational
DBs of QU
Extract
Transform
Load
Refresh
Monitor
&
Integrator
Data
Warehouse
Raped Miner
Server
Serve
Analysis
Query
Reports
Data mining
Data Marts
QU Data Sources
Data Storage
Raped Miner Front-End Tools
Engine
16
Three Data Warehouse Models
n
n
n
Enterprise warehouse
n collects all of the information about subjects spanning the
entire organization such as QU
Data Mart
n a subset of corporate-wide data that is of value to a specific
groups of users. Its scope is confined to specific, selected
groups, such as marketing data mart
n Independent vs. dependent (directly from warehouse)
data mart
Virtual warehouse
n A set of views over operational databases
n Only some of the possible summary views may be
materialized
17
Extraction, Transformation, and Loading (ETL)
n
n
n
n
n
Data extraction
n get data from multiple, heterogeneous, and external sources
Data cleaning
n detect errors in the data and rectify them when possible
Data transformation
n convert data from legacy or host format to warehouse format
Load
n sort, summarize, consolidate, compute views, check integrity,
and build indicies and partitions
Refresh
n propagate the updates from the data sources to the
warehouse
18
Chapter 4: Data Warehousing and On-line
Analytical Processing
n
Data Warehouse: Basic Concepts
n
Data Warehouse Modeling: Data Cube and OLAP
n
Data Warehouse Design and Usage
n
Data Warehouse Implementation
n
Data Generalization by Attribute-Oriented Induction
n
Additional materials
n
Summary
19
Multidimensional Data Model
n
A data warehouse is based on a multidimensional data model
which views data in the form of a data cube
n
A data cube, such as
sales, allows data to be modeled and viewed
in multiple dimensions
n
Dimension tables, such as item (item_name, brand, type), or
time(day, week, month, quarter, year)
n
Fact table contains measures (such as dollars_sold (sales
amount in dollars and keys to each of the related dimension
tables
n
The data cube summarizes the measure with respect to a set
of n dimensions and provides summarizations for all subsets
of them
20
Multidimensional Data Model
21
Conceptual Modeling of Data Warehouses
n
Modeling data warehouses: dimensions & measures
n
Star schema: A fact table in the middle connected to a
set of dimension tables
n
Snowflake schema: A refinement of star schema
where some dimensional hierarchy is normalized into a
set of smaller dimension tables, forming a shape
similar to snowflake
n
Fact constellations: Multiple fact tables share
‫ﻛو ﻛﺑ ﺔ‬
dimension tables, viewed as a collection of stars,
therefore called galaxy schema or fact constellation
22
Example of Star Schema
time
item
time_key
day
day_of_the_week
month
quarter
year
Sales Fact Table
time_key
item_key
branch_key
branch
branch_key
branch_name
branch_type
location_key
units_sold
dollars_sold
avg_sales
item_key
item_name
brand
type
supplier_type
location
location_key
street
city
state_or_province
country
Measures
23
Example of Snowflake Schema
‫ﻧدﻓﺔ اﻟﺛﻠﺞ‬
time
time_key
day
day_of_the_week
month
quarter
year
item
Sales Fact Table
time_key
item_key
branch_key
branch
location_key
branch_key
branch_name
branch_type
units_sold
dollars_sold
avg_sales
Measures
item_key
item_name
brand
type
supplier_key
supplier
supplier_key
supplier_type
location
location_key
street
city_key
city
city_key
city
state_or_province
country
24
Example of Fact Constellation
time
time_key
day
day_of_the_week
month
quarter
year
item
Sales Fact Table
time_key
item_key
item_key
item_name
brand
type
supplier_type
location_key
branch_key
branch_name
branch_type
units_sold
dollars_sold
avg_sales
Measures
time_key
item_key
shipper_key
from_location
branch_key
branch
Shipping Fact Table
location
to_location
location_key
street
city
province_or_state
country
dollars_cost
units_shipped
shipper
shipper_key
shipper_name
location_key
25
shipper_type
A Concept Hierarchy:
Dimension (location)
all
all
Europe
region
country
city
office
Germany
Frankfurt
...
...
...
Spain
North_America
Canada
Vancouver ...
L. Chan
...
Mexico
Toronto
... M. Wind
26
Measures of Data Cubes: Three Categories
n
Distributive: if the result derived by applying the function
to n aggregate values is the same as that derived by
applying the function on all the data without partitioning
n
n
Algebraic: if it can be computed by an algebraic function
with M arguments (where M is a bounded integer), each of
which is obtained by applying a distributive aggregate
function
n
n
E.g., count(), sum(), min(), max()
E.g., avg(), min_N(), standard_deviation()
Holistic: if there is no constant bound on the storage size
needed to describe a subaggregate.
n
E.g., median(), mode(), rank()
Q. What is the main difference between these three measures?
27
Multidimensional Data
Sales volume as a function of product, month,
and region
Re
gi
on
Dimensions: Product, Location, Time
Hierarchical summarization paths
Industry Region
Year
Category Country Quarter
Product
n
Product
City
Office
Month Week
Day
Month
28
A Sample Data Cube
• The data cube summarizes the fact (measure with respect to
a set of n dimensions and provides summarizations for all
Pr
od
TV
PC
VCR
sum
1Qtr
Date
2Qtr 3Qtr
4Qtr
sum
Total annual sales
of TVs in U.S.A.
U.S.A
Country
uc
t
subsets of them
Canada
Mexico
sum
29
Typical OLAP Operations
n
Roll up (drill-up): summarize data
n
by climbing up hierarchy or by dimension reduction
n
Drill down (roll down): reverse of roll-up
n
from higher level summary to lower level summary or detailed data, or introducing new
dimensions
drill across: involving (across) more than one fact table
n
n
n
n
Slice
Dice
Pivot (rotate):
n
reorient the cube, visualization, 3D to series of 2D planes
30
Typical OLAP Operations
31
Browsing a Data Cube
n
n
n
Visualization
OLAP capabilities
Interactive manipulation
32
Chapter 4: Data Warehousing and On-line
Analytical Processing
n
Data Warehouse: Basic Concepts
n
Data Warehouse Modeling: Data Cube and OLAP
n
Data Warehouse Design and Usage
n
Data Warehouse Implementation
n
Data Generalization by Attribute-Oriented Induction
n
Additional materials
n
Summary
33
Design of Data Warehouse: A Business
Analysis Framework
n
Four views regarding the design of a data warehouse
n
Top-down view
n
n
Data source view
n
n
Exposes the information being captured, stored, and
managed by operational systems
Data warehouse view
n
n
The top-down view allows the selection of the relevant
information necessary for the data warehouse. This
information matches current and future business needs.
consists of fact tables and dimension tables
Business query view
n
Sees the perspectives of data in the warehouse from the view
of end-user
34
Data Warehouse Design strategies
n
n
Top-down, bottom-up approaches or a combination of both
n
Top-down: Starts with overall design and planning (mature)
n
Bottom-up: Starts with experiments and prototypes (rapid)
From software engineering point of view
n
n
n
From the software engineering point of view, the design and
construction of a data warehouse may consist of the following
steps:
planning, requirements study, problem analysis, warehouse
design, data integration and testing, and finally deployment of
the data warehouse.
Large software systems can be developed using one of two
methodologies: the waterfall method or the spiral method.
35
warehouse design process
1. Choose a business process to model (e.g., orders, invoices,
shipments, inventory, account administration, sales, or the general
ledger).
2. Choose the business process “grain”, which is the fundamental ,
atomic level of data to be represented in the fact table for this
process (e.g., individual transactions, individual daily snapshots, and
so on).
3. Choose the dimensions that will apply to each fact table record.
Typical dimensions are time, item, customer, supplier, warehouse,
transaction type, and status.
4. Choose the measures that will populate each fact table record.
Typical measures are numeric additive quantities like dollars sold
and units sold.
36
Data Warehouse Usage
n
Three kinds of data warehouse applications
n
Information processing
n
n
n
supports querying, basic statistical analysis, and reporting
using crosstabs, tables, charts and graphs
Analytical processing
n
multidimensional analysis of data warehouse data
n
Eg: OLAP operations, slice-dice, drilling, pivoting
Data mining
n
n
knowledge discovery from hidden patterns
supports associations, constructing analytical models,
performing classification and prediction, and presenting the
mining results using visualization tools
37
Chapter 4: Data Warehousing and On-line
Analytical Processing
n
Data Warehouse: Basic Concepts
n
Data Warehouse Modeling: Data Cube and OLAP
n
Data Warehouse Design and Usage
n
Data Warehouse Implementation
n
Data Generalization by Attribute-Oriented Induction
n
Additional materials
n
Summary
38
Data Warehouse Implementation
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Data Mining: Concepts and Techniques
39
Indexing OLAP Data: Bitmap Index
n
n
n
n
Index on a particular column
Each value in the column has a bit vector: bit-op is fast
The length of the bit vector: # of records in the base table
The i-th bit is set if the i-th row of the base table has the value for
the indexed column
Base table
Index on Type
Index on Region
Cust
Region
Type
C1
C2
C3
C4
C5
Asia
Europe
Asia
America
Europe
Retail
Dealer
Dealer
Retail
Dealer
RecID Asia
1
2
3
4
5
1
0
1
0
0
Europe
America
RecID
Retail
Dealer
0
1
0
0
1
0
0
0
1
0
1
2
3
4
5
1
0
0
1
0
0
1
1
0
1
40
Chapter 4: Data Warehousing and On-line
Analytical Processing
n
Data Warehouse: Basic Concepts
n
Data Warehouse Modeling: Data Cube and OLAP
n
Data Warehouse Design and Usage
n
Data Warehouse Implementation
n
Data Generalization by Attribute-Oriented Induction
n
Additional materials
n
Summary
41
Attribute-Oriented Induction
n
n
n
Collect the task-relevant data (initial relation) using a
relational database query
Perform generalization by attribute removal or attribute
generalization
Apply aggregation by merging identical, generalized
tuples and accumulating their respective counts
n
Interaction with users for knowledge presentation
n
Example: …
42
Attribute-Oriented Induction: An Example
Example: Describe general characteristics of graduate
students in the University database
n
Step 1. Fetch relevant set of data using an SQL
statement, e.g.,
Select * (i.e., name, gender, major, birth_place,
birth_date, residence, phone#, gpa)
from student
where student_status in {“Msc”, “MBA”, “PhD” }
n
n
Step 2. Perform attribute-oriented induction
Step 3. Present results in generalized relation, cross-tab,
or rule forms
43
Class Characterization: An Example
Name
Gender
Jim
Initial
Woodman
Relation Scott
M
Birth_date
Vancouver,BC, 8-12-76
Canada
CS
Montreal, Que, 28-7-75
Canada
Physics Seattle, WA, USA 25-8-70
…
…
…
M
F
…
Removed
Retained
Sci,Eng,
Bus
Gender Major
M
F
…
Birth-Place
CS
Lachance
Laura Lee
…
Prime
Generalized
Relation
Major
Science
Science
…
Country
Age range
Residence
Phone #
GPA
3511 Main St.,
Richmond
345 1st Ave.,
Richmond
687-4598
3.67
253-9106
3.70
125 Austin Ave.,
Burnaby
…
420-5232
…
3.83
…
City
Removed
Excl,
VG,..
Birth_region
Age_range
Residence
GPA
Canada
Foreign
…
20-25
25-30
…
Richmond
Burnaby
…
Very-good
Excellent
…
Count
16
22
…
Birth_Region
Canada
Foreign
Total
Gender
M
16
14
30
F
10
22
32
Total
26
36
62
44
Other Data Mining Tools
n
WEKa: WEKA is a collection of machine learning algorithms
for data mining tasks
n
Rapid Miner
n
KNIME: The Konstanz Information Miner is
a modular environment, It is designed as a teaching,
research and collaboration platform
n
TANAGRA:is open-source data analysis software for
academic and research purposes which combines
data mining techniques with statistical learning.
n
Other Tools such as OLAP
December 27, 2022
Data Mining: Concepts and Techniques
45
Chapter 4: Data Warehousing and On-line
Analytical Processing
n
Data Warehouse: Basic Concepts
n
Data Warehouse Modeling: Data Cube and OLAP
n
Data Warehouse Design and Usage
n
Data Warehouse Implementation
n
Data Generalization by Attribute-Oriented Induction
n
Additional materials
n
Summary
46
Additional materials: A producer wants to know
Which are our
lowest/highest margin
customers ?
Who are my customers
and what products
are they buying?
What is the most
effective distribution
channel?
What product prom-otions have the biggest
effect on revenue?
Which customers
are most likely to go
to the competition ?
‫اﻻﻳﺮادات‬
What impact will
new products/services
have on revenue
and margins?
How we can know that?
47
Very Large Data Bases
n
Terabytes -- 10^12 bytes:
Walmart -- 24 Terabytes
n
Petabytes -- 10^15 bytes:
n
Exabytes -- 10^18 bytes:
Geographic Information Systems
National Medical Records
n
Zettabytes -- 10^21 bytes:
n
Zottabytes -- 10^24 bytes:
Weather images
Intelligence Agency Videos
48
Data Mining works with Warehouse Data
n
Data Warehousing provides
the Enterprise with a memory
 Data Mining provides the
Enterprise with intelligence
49
What factors make data mining possible?
n
Advances in the following areas are making data
mining deployable:
n
n
n
n
n
Better and more data (i.e., operational, behavioral, and
demographic
Ability to establish data warehousing
The emergence of easily deployed data mining tools
The advent/emergence of new data mining techniques.
Experts and supports
50
So, what’s different?
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Data Mining: Concepts and Techniques
51
OLTP vs Data Warehouse
n
OLTP
n
n
n
n
n
n
n
Application Oriented
Used to run business
Detailed data
Current up to date
Isolated Data
Repetitive access
December 27, 2022
Data Warehouse
n
n
n
n
n
n
Subject Oriented
Used to analyze business
Summarized and refined
Snapshot data
Integrated Data
Knowledge User
(Manager)
Data Mining: Concepts and Techniques
52
OLTP vs Data Warehouse
n
OLTP
n Performance Sensitive
n Few Records accessed
at a time (tens)
n
n
n
Read/Update Access
No data redundancy
Database Size
100MB -100 GB
December 27, 2022
n
Data Warehouse
n Performance relaxed
n Large volumes accessed
at a time(millions)
n Mostly Read (Batch
Update)
n Redundancy present
n Database Size 100 GB few terabytes
Data Mining: Concepts and Techniques
‫اداء ﺧﻔﯾف‬
53
OLTP vs Data Warehouse
n
OLTP
n
n
Data Warehouse
Transaction
n
performance metric
Thousands of users
n
throughput* is the
n
December 27, 2022
Query throughput is
the performance
metric
Hundreds of users
Data Mining: Concepts and Techniques
54
To summarize ...
n
OLTP Systems are
used to “run” a
business
n
December 27, 2022
The Data Warehouse
helps to “optimize”
the business
Data Mining: Concepts and Techniques
55
Heart of the Data Warehouse
n
n
n
Heart of the data warehouse is the data itself!
Single version of the truth
Data is organized in a way that represents
business -- subject orientation
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Data Mining: Concepts and Techniques
56
From the Data Warehouse to Data Marts
Information
Less
Individually
Structured
History
Normalized
Detailed
Departmentally
Structured
Organizationally
Structured
Data Warehouse
More
Data
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Data Mining: Concepts and Techniques
57
Summary
n
Data warehousing: A multi-dimensional model of a data warehouse
n
n
n
n
A data cube consists of dimensions & measures
Star schema, snowflake schema, fact constellations
OLAP operations: drilling, rolling, slicing, dicing and pivoting
Data Warehouse Architecture, Design, and Usage
n
Multi-tiered architecture
n
Business analysis design framework
Information processing, analytical processing, data mining, OLAM (Online
Analytical Mining)
Implementation: Efficient computation of data cubes
n
Partial vs. full vs. no materialization
n
Indexing OALP data: Bitmap index and join index
n
OLAP query processing
n
OLAP servers: ROLAP, MOLAP, HOLAP
n
n
n
Data generalization: Attribute-oriented induction
58
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Data Mining: Concepts and Techniques
59