Data Warehousing

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Data Warehousing

• University of California, Berkeley
• School of Information Management and Systems
• SIMS 257 Database Management

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Lecture Outline

• Review
• Application of Object Relational DBMS the
  Berkeley Environmental Digital Library
• Data Warehouses
• Introduction to Data Warehouses
• Data Warehousing
• (Based on lecture notes from Joachim Hammer,
  University of Florida, and Joe Hellerstein and
  Mike Stonebraker of UCB)

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Lecture Outline

• Review
• Application of Object Relational DBMS the
  Berkeley Environmental Digital Library
• Data Warehouses
• Introduction to Data Warehouses
• Data Warehousing
• (Based on lecture notes from Joachim Hammer,
  University of Florida, and Joe Hellerstein and
  Mike Stonebraker of UCB)

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A Digital Library Infrastructure Model
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UC Berkeley Digital Library Project

• Focus Work-centered digital information
  services
• Testbed Digital Library for the California
  Environment
• Research Technical agenda supporting
  user-oriented access to large distributed
  collections of diverse data types.
• Part of the NSF/NASA/DARPA Digital Library
  Initiative (Phases 1 and 2)

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The Environmental Library - Contents

• As of late 2002, the collection represents over
  one terabyte of data, including over 183,000
  digital images, about 300,000 pages of
  environmental documents, and over 2 million
  records in geographical and botanical databases.

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Botanical Data

• The CalFlora Database contains taxonomical and
  distribution information for more than 8000
  native California plants. The Occurrence Database
  includes over 600,000 records of California plant
  sightings from many federal, state, and private
  sources. The botanical databases are linked to
  the CalPhotos collection of California plants,
  and are also linked to external collections of
  data, maps, and photos.

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Geographical Data

• Much of the geographical data in the collection
  has been used to develop our web-based GIS
  Viewer. The Street Finder uses 500,000 Tiger
  records of S.F. Bay Area streets along with the
  70,000-records from the USGS GNIS database.
  California Dams is a database of information
  about the 1395 dams under state jurisdiction. An
  additional 11 GB of geographical data represents
  maps and imagery that have been processed for
  inclusion as layers in our GIS Viewer. This
  includes Digital Ortho Quads and DRG maps for the
  S.F. Bay Area.

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Documents

• Most of the 300,000 pages of digital documents
  are environmental reports and plans that were
  provided by California state agencies. This
  collection includes documents, maps, articles,
  and reports on the California environment
  including Environmental Impact Reports (EIRs),
  educational pamphlets, water usage bulletins, and
  county plans. Documents in this collection come
  from the California Department of Water Resources
  (DWR), California Department of Fish and Game
  (DFG), San Diego Association of Governments
  (SANDAG), and many other agencies. Among the most
  frequently accessed documents are County General
  Plans for every California county and a survey of
  125 Sacramento Delta fish species.

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Multivalent Documents
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GIS Viewer Example
http//elib.cs.berkeley.edu/annotations/gis/buildi
ngs.html
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Blobworld use regions for retrieval

• We want to find general objects? Represent
  images based on coherent regions

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Lecture Outline

• Review
• Application of Object Relational DBMS the
  Berkeley Environmental Digital Library
• Data Warehouses
• Introduction to Data Warehouses
• Data Warehousing
• (Based on lecture notes from Joachim Hammer,
  University of Florida, and Joe Hellerstein and
  Mike Stonebraker of UCB)

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Overview

• Data Warehouses and Merging Information Resources
• What is a Data Warehouse?
• History of Data Warehousing
• Types of Data and Their Uses
• Data Warehouse Architectures
• Data Warehousing Problems and Issues

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Problem Heterogeneous Information Sources
Heterogeneities are everywhere
Personal Databases
World Wide Web
Scientific Databases
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
Personal Databases
Digital Libraries
Scientific 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
Metadata
Integration System
. . .
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
• Hasnt caught on in industry

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The Warehousing Approach

• Information integrated in advance
• Stored in WH for direct querying and analysis

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Advantages of Warehousing Approach

• High query performance
• But not necessarily most current information
• Doesnt 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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Data Warehouse Evolution
Building the DW Inmon (1992)
Data Replication Tools
Relational Databases
Company DWs
2000
1995
1990
1985
1980
1960
1975
Information- Based Management
Data Revolution
Middle Ages
Prehistoric Times
TIME
PCs and Spreadsheets
End-user Interfaces
1st DW Article
DW Confs.
Vendor DW Frameworks
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What is a Data Warehouse?

• A Data Warehouse is a
• subject-oriented,
• integrated,
• time-variant,
• non-volatile
• collection of data used in support of management
  decision making processes.
• -- Inmon Hackathorn, 1994 viz. Hoffer, Chap 11

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DW Definition

• Subject-Oriented
• The data warehouse is organized around the key
  subjects (or high-level entities) of the
  enterprise. Major subjects include
• Customers
• Patients
• Students
• Products
• Etc.

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DW Definition

• Integrated
• The data housed in the data warehouse are defined
  using consistent
• Naming conventions
• Formats
• Encoding Structures
• Related Characteristics

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DW Definition

• Time-variant
• The data in the warehouse contain a time
  dimension so that they may be used as a
  historical record of the business

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DW Definition

• Non-volatile
• Data in the data warehouse are loaded and
  refreshed from operational systems, but cannot be
  updated by end-users

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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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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 decision makers
  and analysts

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Contd

• 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
• Stockbroker financial information and portfolios

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Warehouse is a Specialized DB

• Standard DB
• Mostly updates
• Many small transactions
• Mb - Gb of data
• Current snapshot
• Index/hash on p.k.
• Raw data
• Thousands of users (e.g., clerical users)

• Warehouse
• Mostly reads
• Queries are long and complex
• Gb - Tb of data
• History
• Lots of scans
• Summarized, reconciled data
• Hundreds of users (e.g., decision-makers,
  analysts)

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Summary
Business Information Guide
Business Information Interface
Data Warehouse
Data Warehouse Catalog
Data Warehouse Population
Operational Systems
Enterprise Modeling
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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
• Produced and stored for its own intrinsic value
• e.g., the contents of a text-book

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Data Warehousing Architecture
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Ingest
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Data Warehouse Architectures Conceptual View

• Single-layer
• Every data element is stored once only
• Virtual warehouse
• Two-layer
• Real-time derived data
• Most commonly used approach in
• industry today

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Three-layer Architecture Conceptual View

• Transformation of real-time data to derived data
  really requires two steps

View level Particular informational needs
Physical Implementation of the Data Warehouse
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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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Data Warehousing Two Distinct Issues

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

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Data Extraction

• Source types
• Relational, flat file, WWW, etc.
• How to get data out?
• Replication tool
• Dump file
• Create report
• ODBC or third-party wrappers

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Wrapper

• Converts data and queries from one data model to
  another

Queries
Data Model A
Data

• Extends query capabilities for sources with
  limited capabilities

Queries
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Wrapper Generation

• Solution 1 Hard code for each source
• Solution 2 Automatic wrapper generation

Wrapper Generator
Definition
Wrapper
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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
• Log sniffer
• 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 dont match
• Patch missing, unreadable data
• Notify sources of errors found

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Warehouse Maintenance

• Warehouse data ? materialized view
• Initial loading
• View maintenance
• View maintenance

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Differs from Conventional View Maintenance...

• Warehouses may be highly aggregated and
  summarized
• Warehouse views may be over history of base data
• Process large batch updates
• Schema may evolve

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Differs from Conventional View Maintenance...

• Base data doesnt participate in view maintenance
• Simply reports changes
• Loosely coupled
• Absence of locking, global transactions
• May not be queriable

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Warehouse Maintenance Anomalies

• Materialized view maintenance in loosely coupled,
  non-transactional environment
• Simple example

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Warehouse Maintenance Anomalies
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Maintenance Anomaly - Solutions

• Incremental update algorithms (ECA, Strobe, etc.)
• Research issues Self-maintainable views
• What views are self-maintainable
• Store auxiliary views so original auxiliary
  views are self-maintainable

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Self-Maintainability Examples

• Sold(item,clerk,age)
• Sale(item,clerk) Emp(clerk,age)
• Inserts into Emp
• If Emp.clerk is key and Sale.clerk is foreign key
  (with ref. int.) then no effect
• Inserts into Sale
• Maintain auxiliary view
• Emp-?clerk,age(Sold)
• Deletes from Emp
• Delete from Sold based on clerk

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Self-Maintainability Examples

• Deletes from Sale
• Delete from Sold based on item,clerk
• Unless age at time of sale is relevant
• Auxiliary views for self-maintainability
• Must themselves be self-maintainable
• One solution all source data
• But want minimal set

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Partial Self-Maintainability

• Avoid (but dont prohibit) going to sources
• SoldSale(item,clerk) Emp(clerk,age)
• Inserts into Sale
• Check if clerk already in Sold, go to source if
  not
• Or replicate all clerks over age 30
• Or ...

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Warehouse Specification (ideally)
View Definitions
Warehouse Configuration Module
Warehouse
Integration rules
Integrator
Metadata
Change Detection Requirements
Extractor/ Monitor
Extractor/ Monitor
Extractor/ Monitor
...
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Optimization

• Update filtering at extractor
• Similar to irrelevant updates in constraint and
  view maintenance
• Multiple view maintenance
• If warehouse contains several views
• Exploit shared sub-views

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Additional Research Issues

• Historical views of non-historical data
• Expiring outdated information
• Crash recovery
• Addition and removal of information sources
• Schema evolution

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More Information on DW

• Agosta, Lou, The Essential Guide to Data
  Warehousing. Prentise Hall PTR, 1999.
• Devlin, Barry, Data Warehouse, from Architecture
  to Implementation. Addison-Wesley, 1997.
• Inmon, W.H., Building the Data Warehouse. John
  Wiley, 1992.
• Widom, J., Research Problems in Data
  Warehousing. Proc. of the 4th Intl. CIKM Conf.,
  1995.
• Chaudhuri, S., Dayal, U., An Overview of Data
  Warehousing and OLAP Technology. ACM SIGMOD
  Record, March 1997.