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Data Warehouse Data Mining

Rahul Sachdeva

Syllabus

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Syllabus

Data Warehouse and OLAP

• Why data warehouse

• What’s data warehouse
• What’s multi-dimensional data model
• What’s difference between OLAP and
OLTP

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The Traditional Research Approach

Query-driven (lazy, on-demand)
Clients

Integration System Metadata

...
Wrapper Wrappe Wrapper
r

...
Source Source Source

Disadvantages of Query-Driven
Approach

• Delay in query processing

− Slow or unavailable information sources
− Complex filtering and integration
• Inefficient and potentially expensive for
frequent queries
• Competes with local processing at sources
• Hasn’t caught on in industry

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From DBMS to Decision Support

• DBMSs widely used to maintain transactional data

• Attempts to use of these data for analysis,
exploration, identification of trends etc. has led to
Decision Support Systems.
• Rapid Growth since mid 70’s
• DBMSs vendors have answered this trend by
adding new features to existing products
• Rarely enough

DBs for Decision Support

• Trend towards Data Warehousing

• Data Warehousing – consolidation of data
from several databases which are in turn
maintained by individual business units
along with historical and summary
information

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Characteristics of TPSs

Characteristic OLTP

Typical operation Update

Level of analytical requirements Low

Screens Unchanging

Amount of data per transaction Small

Data level Detailed

Age of data Current

Orientation Records

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Relational Database Theory

• Relational database modeling process –

normalization, relations or tables are progressively
decomposed into smaller relations to a point
where all attributes in a relation are very tightly
coupled with the primary key of the relation.
− First normal form: data items are atomic,
− Second normal form: attributes fully depend on primary
key,
− Third normal form: all non-key attributes are
completely independent of each other.

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Student University Tables

matricN fName lName gender year super Course
um reg visor course credit
121212 Mary Hill F 200 1234 code value
3 c1 120
232323 Steve Gray M 200 1234 c3 60
5 c5 60
123456 Jimm Smith M 200 1111 Enrolled
y 0 course student
code Num
Staff staff first last gender c1 121212
Num Name Name
c3 121212
1234 Jane Smith F
c3 123456
2323 Tom Green M
c1 232323
1111 Jim Brow M
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n Etc etc Etc etc 13

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Relation Database Theory, cont’d

• The process of normalization generally

breaks a table into many independent tables.
• A normalized database yields a flexible
model, making it easy to maintain dynamic
relationships between business entities.
• A relational database system is effective
and efficient for operational databases – a
lot of updates (aiming at optimizing update
performance).

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Problems

• A fully normalized data model can perform

very inefficiently for queries.
• Historical data are usually large with static
relationships:
− Unnecessary joins may take unacceptably long
time
• Historical data are diverse

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Problem: Heterogeneous Information

Sources
“Heterogeneities are everywhere”
Personal
Databases

World
Scientific Databases
Wide
Web
Digital Libraries

Different interfaces
Different data representations
Duplicate and inconsistent information
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Problem: Data Management in Large

Enterprises
• Vertical fragmentation of informational systems
(vertical stove pipes)
• Result of application (user)-driven development of
operational systems
Sales Planning Suppliers Num. Control
Stock Mngmt Debt Mngmt Inventory
... ... ...

Sales Administration Finance Manufacturing ...

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Goal: Unified Access to Data

Integration System

World
Wide
Personal
Web
Digital Libraries Scientific Databases Databases

• Collects and combines information

• Provides integrated view, uniform user interface
• Supports sharing
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The Traditional Research Approach

• Query-driven (lazy, on-demand)
Clients

Integration System Metadata

...
Wrapper Wrapper Wrapper

...
Source Source Source

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Disadvantages of Query-Driven
Approach

 Delay in query processing

 Slow or unavailable information sources
 Complex filtering and integration
 Inefficient and potentially expensive for
frequent queries
 Competes with local processing at sources
 Hasn’t caught on in industry

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The Warehousing Approach

• Information Clients
integrated in
advance Data
Warehouse
• Stored in wh for
direct querying
Integration System Metadata
and analysis
...
Extractor/ Extractor/ Extractor/
Monitor Monitor Monitor

...
Source Source Source
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Advantages of Warehousing Approach

• High query performance
− But not necessarily most current information
• Doesn’t interfere with local processing at sources
− Complex queries at warehouse
− OLTP at information sources
• Information copied at warehouse
− Can modify, annotate, summarize, restructure, etc.
− Can store historical information
− Security, no auditing
• Has caught on in industry
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Not Either-Or Decision

• Query-driven approach still better for

− Rapidly changing information
− Rapidly changing information sources
− Truly vast amounts of data from large numbers
of sources
− Clients with unpredictable needs

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A Data Warehouse is...

• Stored collection of diverse data

− A solution to data integration problem
− Single repository of information
• Subject-oriented
− Organized by subject, not by application
− Used for analysis, data mining, etc.
• Optimized differently from transaction-
oriented db
• User interface aimed at executive
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A Data Warehouse is...

(continued)
• Large volume of data (Gb, Tb)
• Non-volatile
− Historical
− Time attributes are important
• Updates infrequent
• May be append-only
• Examples
− All transactions ever at WalMart
− Complete client histories at insurance firm
25 − Stockbroker financial information and portfolios

Summary
Business Business Information
Information Guide Interface

Data
Data Warehouse
Warehouse
Catalog
Data Warehouse
Population

Enterprise
Modeling
Operational Systems

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Warehouse is a Specialized DB

Standard DB Warehouse
• Mostly updates Mostly reads
• Many small transactions Queries are long and complex
• Mb - Gb of data Gb - Tb of data
• Current snapshot History
• Index/hash on p.k. Lots of scans
• Raw data Summarized, reconciled data
• Thousands of users (e.g., clerical Hundreds of users (e.g.,
users) decision-makers, analysts)

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Warehousing and Industry

• Warehousing is big business

− $2 billion in 1995
− $3.5 billion in early 1997
− Predicted: $8 billion in 1998 [Metagroup]
• WalMart has largest warehouse
− 900-CPU, 2,700 disk, 23 TB Teradata system
− ~7TB in warehouse
− 40-50GB per day

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Types of Data

• Business Data - represents meaning

− Real-time data (ultimate source of all business
data)
− Reconciled data
− Derived data
• Metadata - describes meaning
− Build-time metadata
− Control metadata
− Usage metadata
• Data as a product* - intrinsic meaning
29 − Produced and stored for its own intrinsic value

MIS and Decision Support

Ad hoc access
Production
platforms

Operational reports Decision makers

• MIS systems provided business data

• Reports were developed on request
• Reports provided little analysis capability
• no personal ad hoc access to data

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Analyzing Data from Operational

Systems
• Data structures are complex
• Systems are designed for high performance and
ERP throughput
• Data is not meaningfully represented
• Data is dispersed
• TPS systems unsuitable for intensive queries

Production
platforms

Operational reports

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Data Extract Processing

Operational systemsExtracts Decision makers

• End user computing offloaded from the

operational environment
• User’s own data

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Management Issues

Operational systems Extracts Decision makers

Extract explosion
• Duplicated effort
• Multiple technologies
• Obsolete reports
• No metadata
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Data Quality Issues

• No common time basis

• Different calculation algorithms
• Different levels of extraction
• Different levels of granularity
• Different data field names
• Different data field meanings
• Missing information
• No data correction rules
• No drill-down capability

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What is a Data Warehouse?

A Practitioners Viewpoint

“A data warehouse is simply a single,

complete, and consistent store of data
obtained from a variety of sources and made
available to end users in a way they can
understand and use it in a business context.”
-- Barry Devlin, IBM Consultant

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What is a Data Warehouse?

An Alternative Viewpoint

“A DW is a
− subject-oriented,
− integrated,
− time-varying,
− non-volatile
collection of data that is used primarily in
organizational decision making.”
-- W.H. Inmon, Building the Data Warehouse, 1992

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The Data Warehouse

• The Data Warehouse is an integrated,
subject-oriented, time-variant, non-volatile
database that provides support for decision
making.

− Integrated
• The Data Warehouse is a centralized, consolidated
database that integrates data retrieved from the
entire organization.

− Subject-Oriented
• The Data Warehouse data is arranged and optimized
to provide answers to questions coming from
diverse functional areas within a company.

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The Data Warehouse

− Time Variant
• The Warehouse data represent the flow of data
through time. It can even contain projected data.

− Non-Volatile
• Once data enter the Data Warehouse, they are
never removed.
• The Data Warehouse is always growing.

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A Data Warehouse is...

• Stored collection of diverse data
− A solution to data integration problem
− Single repository of information
• Subject-oriented
− Organized by subject, not by application
− Used for analysis, data mining, etc.
• Optimized differently from transaction-
oriented db
• User interface aimed at executive

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… Cont’d
• Large volume of data (Gb, Tb)
• Non-volatile
− Historical
− Time attributes are important
• Updates infrequent
• May be append-only
• Examples
− All transactions ever at Sainsbury’s
− Complete client histories at insurance firm
− LSE financial information and portfolios
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Generic Warehouse Architecture

Client Client
Query & Analysis

Design Phase Loading

Warehouse Metadata
Maintenance
Integrator Optimization

Extractor/ Extractor/ Extractor/

Monitor Monitor Monitor

...
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Data Warehouse Architectures:

Conceptual View
Operational Informational

• Single-layer
systems systems

− Every data element is stored once only “Real-time data”

− Virtual warehouse

• Two-layer Operational Informational

− Real-time + derived data

systems systems

− Most commonly used approach in

Derived Data
industry today
Real-time data

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Three-layer Architecture:
Conceptual View
• Transformation of real-time data to derived
data really requires two steps
Operational Informational
systems systems

View level
“Particular informational
Derived Data
needs”

Reconciled Data
Physical Implementation
of the Data Warehouse

Real-time data

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DW vs DM

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Data Warehousing: Two Distinct

Issues
(1) How to get information into warehouse
“Data warehousing”
(2) What to do with data once it’s in
warehouse
“Warehouse DBMS”
• Both rich research areas
• Industry has focused on (2)

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Issues in Data Warehousing

• Warehouse Design
• Extraction
− Wrappers, monitors (change detectors)
• Integration
− Cleansing & merging
• Warehousing specification & Maintenance
• Optimizations
• Miscellaneous (e.g., evolution)

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OLTP vs. OLAP

• OLTP: On Line Transaction Processing

− Describes processing at operational sites
• OLAP: On Line Analytical Processing
− Describes processing at warehouse

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Issues (1)

• Warehouse uses relational data model or

multi-dimensional data model (e.g., data
cube)
• On the other hand, source types
− Relational, OO, hierarchical, legacy
− Semistructured: flat file, WWW
• How do we get the data out?

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Issues (2)

• Warehouse must be kept current in light of

changes to underlying sources
• How do we detect updates in sources?

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Wrapper

Converts data and queries from one data model to

another
Data Queries Data
Model Model
A Data B

Extends query capabilities for sources with

limited capabilities

Queries Wrapper Source

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Wrapper Generation

• Solution 1: Hard code for each source

• Solution 2: Automatic wrapper generation
Wrapper
Wrapper Definition
Generator

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Routine When...
• Many tools for dealing with “standard situations”
− Standard sources with full/many capabilities
• e.g., most commercial DBMSs, all ODBC-compliant
sources
− Standard interactions
• e.g., pass-through queries, extraction from rel. tables,
replication
− Cooperative sources or sources under our control
• Tools
− Replication tools, ODBC, report writers, third-party
“wrappers”

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Not So Routine When...

• “Non-standard situations”
− Unstructured or semistructured sources with
little or no explicit schema
− Uncooperative sources
− Sources with limited capabilities (e.g., legacy
sources, WWW)
• Few commercial tools
• Mostly research

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Data Transformations

• Convert data to uniform format

− Byte ordering, string termination
− Internal layout
• Remove, add & reorder attributes
− Add key
− Add data to get history
• Sort tuples

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Monitors

• Goal: Detect changes of interest and

propagate to integrator
• How?
− Triggers
− Replication server
− Compare query results
− Compare snapshots/dumps

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Data Integration

• Receive data (changes) from multiple wrappers/monitors and

integrate into warehouse
• Rule-based
• Actions
− Resolve inconsistencies
− Eliminate duplicates
− Integrate into warehouse (may not be empty)
− Summarize data
− Fetch more data from sources (wh updates)
− etc.

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Data Cleansing

• Find (& remove) duplicate tuples

− e.g., Jane Doe vs. Jane Q. Doe
• Detect inconsistent, wrong data
− Attribute values that don’t match
• Patch missing, unreadable data
• Notify sources of errors found

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Warehouse is a Specialized DB
Standard DB (OLTP) Warehouse (OLAP)
• Mostly updates • Mostly reads
• Many small transactions • Queries are long and complex
• Mb - Gb of data • Gb - Tb of data
• Current snapshot • History
• Index/hash on p.k. • Lots of scans
• Raw data • Summarized, reconciled data
• Thousands of users (e.g., • Hundreds of users (e.g.,
clerical users) decision-makers, analysts)

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Data Warehouse – Blend of

Technologies

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Decision Support
• Information technology to help the
knowledge worker (executive, manager,
analyst) make faster & better decisions
− “What were the sales volumes by region and product category for
the last year?”
− “How did the share price of comp. manufacturers correlate with
quarterly profits over the past 10 years?”
− “Which orders should we fill to maximize revenues?”

• On-line analytical processing (OLAP) is an

element of decision support systems (DSS)

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Three-Tier Decision Support Systems

• Warehouse database server
− Almost always a relational DBMS, rarely flat files
• OLAP servers
− Relational OLAP (ROLAP): extended relational DBMS that
maps operations on multidimensional data to standard
relational operators
− Multidimensional OLAP (MOLAP): special-purpose server
that directly implements multidimensional data and operations
• Clients
− Query and reporting tools
− Analysis tools
− Data mining tools
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The Complete Decision Support

System
Information Sources Data Warehouse OLAP Servers Clients
Server (Tier 2) (Tier 3)
(Tier 1)
e.g., MOLAP
Semistructured Analysis
Sources
Data
Warehouse serve

extract Query/Reporting
transform
load serve
refresh
etc. e.g., ROLAP
Operational
DB’s Data Mining
serve

Data Marts
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Data Warehouse vs. Data Marts

• Enterprise warehouse: collects all information about
subjects (customers,products,sales,assets,
personnel) that span the entire organization
− Requires extensive business modeling (may take years to design
and build)
• Data Marts: Departmental subsets that focus on selected
subjects
− Marketing data mart: customer, product, sales
− Faster roll out, but complex integration in the long run
• Virtual warehouse: views over operational dbs
− Materialize sel. summary views for efficient query processing
− Easy to build but require excess capability on operat. db servers
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OLAP for Decision Support

• OLAP = Online Analytical Processing
• Support (almost) ad-hoc querying for business analyst
• Think in terms of spreadsheets
− View sales data by geography, time, or product
• Extend spreadsheet analysis model to work with
warehouse data
− Large data sets
− Semantically enriched to understand business terms
− Combine interactive queries with reporting functions
• Multidimensional view of data is the foundation of
OLAP
− Data model, operations, etc.
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Approaches to OLAP Servers

• Relational DBMS as Warehouse Servers
• Two possibilities for OLAP servers
• (1) Relational OLAP (ROLAP)
− Relational and specialized relational DBMS to
store and manage warehouse data
− OLAP middleware to support missing pieces
• (2) Multidimensional OLAP (MOLAP)
− Array-based storage structures
− Direct access to array data structures

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OLAP Server: Query Engine

Requirements
• Aggregates (maintenance and querying)
− Decide what to precompute and when
• Query language to support
multidimensional operations
− Standard SQL falls short
• Scalable query processing
− Data intensive and data selective queries

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