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© 2005 by Prentice Hall © 2005 by Prentice Hall 1 Chapter 11: Chapter 11: Data Warehousing Data Warehousing Modern Database Management Modern Database Management 7 7 th th Edition Edition Jeffrey A. Hoffer, Mary B. Prescott, Jeffrey A. Hoffer, Mary B. Prescott, Fred R. McFadden Fred R. McFadden
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Page 1: © 2005 by Prentice Hall 1 Chapter 11: Data Warehousing Modern Database Management 7 th Edition Jeffrey A. Hoffer, Mary B. Prescott, Fred R. McFadden.

© 2005 by Prentice Hall© 2005 by Prentice Hall 11

Chapter 11:Chapter 11: Data Warehousing Data Warehousing

Modern Database Modern Database ManagementManagement

77thth Edition EditionJeffrey A. Hoffer, Mary B. Prescott, Jeffrey A. Hoffer, Mary B. Prescott,

Fred R. McFaddenFred R. McFadden

Page 2: © 2005 by Prentice Hall 1 Chapter 11: Data Warehousing Modern Database Management 7 th Edition Jeffrey A. Hoffer, Mary B. Prescott, Fred R. McFadden.

22Chapter 11 © 2005 by Prentice Hall© 2005 by Prentice Hall

ObjectivesObjectives Definition of termsDefinition of terms Reasons for information gap between Reasons for information gap between

information needs and availabilityinformation needs and availability Reasons for need of data warehousingReasons for need of data warehousing Describe three levels of data warehouse Describe three levels of data warehouse

architecturesarchitectures List four steps of data reconciliationList four steps of data reconciliation Describe two components of star schemaDescribe two components of star schema Estimate fact table sizeEstimate fact table size Design a data martDesign a data mart

Page 3: © 2005 by Prentice Hall 1 Chapter 11: Data Warehousing Modern Database Management 7 th Edition Jeffrey A. Hoffer, Mary B. Prescott, Fred R. McFadden.

33Chapter 11 © 2005 by Prentice Hall© 2005 by Prentice Hall

DefinitionDefinition Data WarehouseData Warehouse: :

A subject-oriented, integrated, time-variant, non-A subject-oriented, integrated, time-variant, non-updatable collection of data used in support of updatable collection of data used in support of management decision-making processesmanagement decision-making processes

Subject-oriented:Subject-oriented: e.g. customers, patients, e.g. customers, patients, students, productsstudents, products

Integrated: Integrated: Consistent naming conventions, Consistent naming conventions, formats, encoding structures; from multiple data formats, encoding structures; from multiple data sourcessources

Time-variant: Time-variant: Can study trends and changesCan study trends and changes Nonupdatable: Nonupdatable: Read-only, periodically refreshedRead-only, periodically refreshed

Data MartData Mart:: A data warehouse that is limited in scopeA data warehouse that is limited in scope

Page 4: © 2005 by Prentice Hall 1 Chapter 11: Data Warehousing Modern Database Management 7 th Edition Jeffrey A. Hoffer, Mary B. Prescott, Fred R. McFadden.

44Chapter 11 © 2005 by Prentice Hall© 2005 by Prentice Hall

Need for Data WarehousingNeed for Data Warehousing Integrated, company-wide view of high-quality Integrated, company-wide view of high-quality

information (from disparate databases)information (from disparate databases) Separation of Separation of operationaloperational and and informationalinformational

systems and data (for improved performance)systems and data (for improved performance)

Page 5: © 2005 by Prentice Hall 1 Chapter 11: Data Warehousing Modern Database Management 7 th Edition Jeffrey A. Hoffer, Mary B. Prescott, Fred R. McFadden.

55Chapter 11 © 2005 by Prentice Hall© 2005 by Prentice Hall

Source: adapted from Strange (1997).

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66Chapter 11 © 2005 by Prentice Hall© 2005 by Prentice Hall

Data Warehouse Data Warehouse ArchitecturesArchitectures

Generic Two-Level ArchitectureGeneric Two-Level Architecture Independent Data MartIndependent Data Mart Dependent Data Mart and Dependent Data Mart and

Operational Data StoreOperational Data Store Logical Data Mart and @ctive Logical Data Mart and @ctive

WarehouseWarehouse Three-Layer architectureThree-Layer architecture

All involve some form of extraction, transformation and loading (ETLETL)

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77Chapter 11 © 2005 by Prentice Hall© 2005 by Prentice Hall

Figure 11-2: Generic two-level architecture

E

T

LOne, company-wide warehouse

Periodic extraction data is not completely current in warehouse

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88Chapter 11 © 2005 by Prentice Hall© 2005 by Prentice Hall

Figure 11-3: Independent data martData marts:Data marts:Mini-warehouses, limited in scope

E

T

L

Separate ETL for each independent data mart

Data access complexity due to multiple data marts

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99Chapter 11 © 2005 by Prentice Hall© 2005 by Prentice Hall

Figure 11-4: Dependent data mart with operational data store

ET

L

Single ETL for enterprise data warehouse(EDW)(EDW)

Simpler data access

ODS ODS provides option for obtaining current data

Dependent data marts loaded from EDW

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ET

L

Near real-time ETL for @active Data Warehouse@active Data Warehouse

ODS ODS and data warehousedata warehouse are one and the same

Data marts are NOT separate databases, but logical views of the data warehouse Easier to create new data marts

Page 11: © 2005 by Prentice Hall 1 Chapter 11: Data Warehousing Modern Database Management 7 th Edition Jeffrey A. Hoffer, Mary B. Prescott, Fred R. McFadden.

1111Chapter 11 © 2005 by Prentice Hall© 2005 by Prentice Hall

Figure 11-6: Three-layer data architecture

Page 12: © 2005 by Prentice Hall 1 Chapter 11: Data Warehousing Modern Database Management 7 th Edition Jeffrey A. Hoffer, Mary B. Prescott, Fred R. McFadden.

1212Chapter 11 © 2005 by Prentice Hall© 2005 by Prentice Hall

Data CharacteristicsData CharacteristicsStatus vs. Event DataStatus vs. Event Data

Status

Status

Event = a database action (create/update/delete) that results from a transaction

Page 13: © 2005 by Prentice Hall 1 Chapter 11: Data Warehousing Modern Database Management 7 th Edition Jeffrey A. Hoffer, Mary B. Prescott, Fred R. McFadden.

1313Chapter 11 © 2005 by Prentice Hall© 2005 by Prentice Hall

Data CharacteristicsData CharacteristicsTransient vs. Periodic DataTransient vs. Periodic Data

Changes to existing records are written over previous records, thus destroying the previous data content

Data are never physically altered or

deleted once they have been added to

the store

Page 14: © 2005 by Prentice Hall 1 Chapter 11: Data Warehousing Modern Database Management 7 th Edition Jeffrey A. Hoffer, Mary B. Prescott, Fred R. McFadden.

1414Chapter 11 © 2005 by Prentice Hall© 2005 by Prentice Hall

Data ReconciliationData Reconciliation Typical operational data is:Typical operational data is:

Transient – not historicalTransient – not historical Not normalized (perhaps due to denormalization for Not normalized (perhaps due to denormalization for

performance)performance) Restricted in scope – not comprehensiveRestricted in scope – not comprehensive Sometimes poor quality – inconsistencies and errorsSometimes poor quality – inconsistencies and errors

After ETL, data should be:After ETL, data should be: Detailed – not summarized yetDetailed – not summarized yet Historical – periodicHistorical – periodic Normalized – 3Normalized – 3rdrd normal form or higher normal form or higher Comprehensive – enterprise-wide perspectiveComprehensive – enterprise-wide perspective Timely – data should be current enough to assist decision-Timely – data should be current enough to assist decision-

makingmaking Quality controlled – accurate with full integrityQuality controlled – accurate with full integrity

Page 15: © 2005 by Prentice Hall 1 Chapter 11: Data Warehousing Modern Database Management 7 th Edition Jeffrey A. Hoffer, Mary B. Prescott, Fred R. McFadden.

1515Chapter 11 © 2005 by Prentice Hall© 2005 by Prentice Hall

The ETL ProcessThe ETL Process

Capture/ExtractCapture/Extract Scrub or data cleansingScrub or data cleansing TransformTransform Load and IndexLoad and Index

ETL = Extract, transform, and load

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1616Chapter 11 © 2005 by Prentice Hall© 2005 by Prentice Hall

Static extractStatic extract = capturing a snapshot of the source data at a point in time

Incremental extractIncremental extract = capturing changes that have occurred since the last static extract

Capture/Extract…obtaining a snapshot of a chosen subset of the source data for loading into the data warehouse

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1717Chapter 11 © 2005 by Prentice Hall© 2005 by Prentice Hall

Scrub/Cleanse…uses pattern recognition and AI techniques to upgrade data quality

Fixing errors:Fixing errors: misspellings, erroneous dates, incorrect field usage, mismatched addresses, missing data, duplicate data, inconsistencies

Also:Also: decoding, reformatting, time stamping, conversion, key generation, merging, error detection/logging, locating missing data

Page 18: © 2005 by Prentice Hall 1 Chapter 11: Data Warehousing Modern Database Management 7 th Edition Jeffrey A. Hoffer, Mary B. Prescott, Fred R. McFadden.

1818Chapter 11 © 2005 by Prentice Hall© 2005 by Prentice Hall

Transform = convert data from format of operational system to format of data warehouse

Record-level:Record-level:Selection – data partitioningJoining – data combiningAggregation – data summarization

Field-level:Field-level: single-field – from one field to one fieldmulti-field – from many fields to one, or one field to many

Page 19: © 2005 by Prentice Hall 1 Chapter 11: Data Warehousing Modern Database Management 7 th Edition Jeffrey A. Hoffer, Mary B. Prescott, Fred R. McFadden.

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Load/Index= place transformed data into the warehouse and create indexes

Refresh mode:Refresh mode: bulk rewriting of target data at periodic intervals

Update mode:Update mode: only changes in source data are written to data warehouse

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2020Chapter 11 © 2005 by Prentice Hall© 2005 by Prentice Hall

Figure 11-11: Single-field transformation

In general – some transformation function translates data from old form to new form

Algorithmic transformation uses a formula or logical expression

Table lookup – another approach, uses a separate table keyed by source record code

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Figure 11-12: Multifield transformation

M:1 – from many source fields to one target field

1:M – from one source field to many target fields

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Derived DataDerived Data ObjectivesObjectives

Ease of use for decision support applicationsEase of use for decision support applications Fast response to predefined user queriesFast response to predefined user queries Customized data for particular target audiencesCustomized data for particular target audiences Ad-hoc query supportAd-hoc query support Data mining capabilitiesData mining capabilities

CharacteristicsCharacteristics Detailed (mostly periodic) dataDetailed (mostly periodic) data Aggregate (for summary)Aggregate (for summary) Distributed (to departmental servers)Distributed (to departmental servers)

Most common data model = star schemastar schema(also called “dimensional model”)

Page 23: © 2005 by Prentice Hall 1 Chapter 11: Data Warehousing Modern Database Management 7 th Edition Jeffrey A. Hoffer, Mary B. Prescott, Fred R. McFadden.

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Figure 11-13: Components of a star schemastar schema

Fact tables contain factual or quantitative data

Dimension tables contain descriptions about the subjects of the business

1:N relationship between dimension tables and fact tables

Excellent for ad-hoc queries, but bad for online transaction processing

Dimension tables are denormalized to maximize performance

Page 24: © 2005 by Prentice Hall 1 Chapter 11: Data Warehousing Modern Database Management 7 th Edition Jeffrey A. Hoffer, Mary B. Prescott, Fred R. McFadden.

2424Chapter 11 © 2005 by Prentice Hall© 2005 by Prentice Hall

Figure 11-14: Star schema example

Fact table provides statistics for sales broken down by product, period and store dimensions

Page 25: © 2005 by Prentice Hall 1 Chapter 11: Data Warehousing Modern Database Management 7 th Edition Jeffrey A. Hoffer, Mary B. Prescott, Fred R. McFadden.

2525Chapter 11 © 2005 by Prentice Hall© 2005 by Prentice Hall

Page 26: © 2005 by Prentice Hall 1 Chapter 11: Data Warehousing Modern Database Management 7 th Edition Jeffrey A. Hoffer, Mary B. Prescott, Fred R. McFadden.

2626Chapter 11 © 2005 by Prentice Hall© 2005 by Prentice Hall

Issues Regarding Star Issues Regarding Star SchemaSchema

Dimension table keys must be Dimension table keys must be surrogatesurrogate (non- (non-intelligent and non-business related), because:intelligent and non-business related), because: Keys may change over timeKeys may change over time Length/format consistencyLength/format consistency

Granularity of Fact Table – what level of detail do Granularity of Fact Table – what level of detail do you want? you want? Transactional grain – finest levelTransactional grain – finest level Aggregated grain – more summarizedAggregated grain – more summarized Finer grains Finer grains better better market basket analysismarket basket analysis capability capability Finer grain Finer grain more dimension tables, more rows in fact table more dimension tables, more rows in fact table

Duration of the database – how much history Duration of the database – how much history should be kept?should be kept? Natural duration – 13 months or 5 quartersNatural duration – 13 months or 5 quarters Financial institutions may need longer durationFinancial institutions may need longer duration Older data is more difficult to source and cleanseOlder data is more difficult to source and cleanse

Page 27: © 2005 by Prentice Hall 1 Chapter 11: Data Warehousing Modern Database Management 7 th Edition Jeffrey A. Hoffer, Mary B. Prescott, Fred R. McFadden.

2727Chapter 11 © 2005 by Prentice Hall© 2005 by Prentice Hall

Figure 11-16: Modeling dates

Fact tables contain time-period data Date dimensions are important

Page 28: © 2005 by Prentice Hall 1 Chapter 11: Data Warehousing Modern Database Management 7 th Edition Jeffrey A. Hoffer, Mary B. Prescott, Fred R. McFadden.

2828Chapter 11 © 2005 by Prentice Hall© 2005 by Prentice Hall

Helper table simplifies representation of hierarchies in data warehouses

Page 29: © 2005 by Prentice Hall 1 Chapter 11: Data Warehousing Modern Database Management 7 th Edition Jeffrey A. Hoffer, Mary B. Prescott, Fred R. McFadden.

2929Chapter 11 © 2005 by Prentice Hall© 2005 by Prentice Hall

The User InterfaceThe User InterfaceMetadata (data catalog)Metadata (data catalog)

Identify subjects of the data martIdentify subjects of the data mart Identify dimensions and factsIdentify dimensions and facts Indicate how data is derived from enterprise Indicate how data is derived from enterprise

data warehouses, including derivation rulesdata warehouses, including derivation rules Indicate how data is derived from operational Indicate how data is derived from operational

data store, including derivation rulesdata store, including derivation rules Identify available reports and predefined queriesIdentify available reports and predefined queries Identify data analysis techniques (e.g. drill-down)Identify data analysis techniques (e.g. drill-down) Identify responsible peopleIdentify responsible people

Page 30: © 2005 by Prentice Hall 1 Chapter 11: Data Warehousing Modern Database Management 7 th Edition Jeffrey A. Hoffer, Mary B. Prescott, Fred R. McFadden.

3030Chapter 11 © 2005 by Prentice Hall© 2005 by Prentice Hall

On-Line Analytical Processing On-Line Analytical Processing (OLAP)(OLAP)

The use of a set of graphical tools that provides The use of a set of graphical tools that provides users with multidimensional views of their data users with multidimensional views of their data and allows them to analyze the data using and allows them to analyze the data using simple windowing techniquessimple windowing techniques

Relational OLAP (ROLAP)Relational OLAP (ROLAP) Traditional relational representationTraditional relational representation

Multidimensional OLAP (MOLAP)Multidimensional OLAP (MOLAP) CubeCube structure structure

OLAP OperationsOLAP Operations Cube slicingCube slicing – come up with 2-D view of data – come up with 2-D view of data Drill-downDrill-down – going from summary to more detailed – going from summary to more detailed

viewsviews

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3131Chapter 11 © 2005 by Prentice Hall© 2005 by Prentice Hall

Figure 11-22: Slicing a data cube

Page 32: © 2005 by Prentice Hall 1 Chapter 11: Data Warehousing Modern Database Management 7 th Edition Jeffrey A. Hoffer, Mary B. Prescott, Fred R. McFadden.

3232Chapter 11 © 2005 by Prentice Hall© 2005 by Prentice Hall

Figure 11-23: Example of drill-down

Summary report

Drill-down with color added

Starting with summary data, users can obtain details for particular cells

Page 33: © 2005 by Prentice Hall 1 Chapter 11: Data Warehousing Modern Database Management 7 th Edition Jeffrey A. Hoffer, Mary B. Prescott, Fred R. McFadden.

3333Chapter 11 © 2005 by Prentice Hall© 2005 by Prentice Hall

Data Mining and Data Mining and VisualizationVisualization

Knowledge discovery using a blend of statistical, AI, and computer Knowledge discovery using a blend of statistical, AI, and computer graphics techniquesgraphics techniques

Goals:Goals: Explain observed events or conditionsExplain observed events or conditions Confirm hypothesesConfirm hypotheses Explore data for new or unexpected relationshipsExplore data for new or unexpected relationships

TechniquesTechniques Statistical regressionStatistical regression Decision tree inductionDecision tree induction Clustering and signal processingClustering and signal processing AffinityAffinity Sequence associationSequence association Case-based reasoningCase-based reasoning Rule discoveryRule discovery Neural netsNeural nets FractalsFractals

Data visualization – representing data in graphical/multimedia Data visualization – representing data in graphical/multimedia formats for analysisformats for analysis


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