Online Analytical Processing by Hweichao Lu

8/7/2019 Online Analytical Processing by Hweichao Lu

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Online AnalyticalOnline Analytical

Processing (OLAP)Processing (OLAP)

Hweichao LuHweichao Lu

CS157BCS157B--02 Spring 200702 Spring 2007


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What is OLAPWhat is OLAP

Basic idea:Basic idea: converting data intoconverting data into

information that decision makers needinformation that decision makers need

Concept to analyze data by multipleConcept to analyze data by multiple

dimension in a structure called data cubedimension in a structure called data cube


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HistoryHistory

In 1993, E. F. Codd came up with theIn 1993, E. F. Codd came up with the

termterm online analytical processing (OLAP)online analytical processing (OLAP)

and proposed 12 criteria to define anand proposed 12 criteria to define anOLAP databaseOLAP database

the term OLAP seems perfect to describethe term OLAP seems perfect to describe

databases designed to facilitate decisiondatabases designed to facilitate decisionmaking (analysis) in an organizationmaking (analysis) in an organization


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Purpose of OLAPPurpose of OLAP

To derive summarized information fromTo derive summarized information from

large volume databaselarge volume database

To generate automated reports forTo generate automated reports for

human viewhuman view


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Why need OLAP overWhy need OLAP over

Relational Database IRelational Database I Consistently fast responseConsistently fast response

OLAP obtains a consistently fastOLAP obtains a consistently fast

response is by prestoring calculatedresponse is by prestoring calculated

valuesvalues


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Relational Database IIRelational Database II MetadataMetadata--based queriesbased queries

provide analysis functions that areprovide analysis functions that are

difficult or impossible to express in SQLdifficult or impossible to express in SQL

SQLSQL was developed primarily forwas developed primarily for

transaction systems, not for reportingtransaction systems, not for reporting

applicationsapplications


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Relational Database IIIRelational Database III SpreadsheetSpreadsheet--style formulasstyle formulas

design the data structure with users indesign the data structure with users in

mind.mind.

Spreadsheets areSpreadsheets are key components ofkey components of

business management because they arebusiness management because they are

intuitive to createintuitive to create


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Step IStep I

1.1. identify multidimensional dataidentify multidimensional data

measure attributemeasure attribute

(measure some value, can be(measure some value, can beaggregated upon)aggregated upon)

dimension attributedimension attribute(define the dimension and summary of(define the dimension and summary ofmeasure attribute)measure attribute)


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(Cont.)(Cont.)

Each dimension is typically expressed asEach dimension is typically expressed as

a hierarchya hierarchy

Hierarchy: Analyst is interested inHierarchy: Analyst is interested in

different level of detail of a dimensiondifferent level of detail of a dimension


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Step IIStep II

2.2. Analyze multidimensional data intoAnalyze multidimensional data into

crosscross--tabulationtabulation

row header: value for one attributerow header: value for one attribute

column header: value for another attr.column header: value for another attr.

individual cell: value aggregationindividual cell: value aggregation


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Step IIIStep III

3.3. Visualize nVisualize n--dimensional cubedimensional cube -- datadata

cubecube

the word CUBE describe what in thethe word CUBE describe what in the

relational world would be the integrationrelational world would be the integration

of the fact table with dimension tablesof the fact table with dimension tables


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Step IVStep IV

After you design the cube, you will useAfter you design the cube, you will use

the cube's structure to build a relationalthe cube's structure to build a relational

database (known as a star schema) todatabase (known as a star schema) tohouse the data for the cubehouse the data for the cube


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Step VStep V

Once you load data into the relationalOnce you load data into the relational

database, and then into the cube, you'lldatabase, and then into the cube, you'll

be able to see how attributes, dimensions,be able to see how attributes, dimensions,measures, and measure groups fitmeasures, and measure groups fit

together within a cube to create atogether within a cube to create a

powerful analytical tool.powerful analytical tool.


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StarSchemaStarSchema

Cubes are easily stored in relational databases,Cubes are easily stored in relational databases,

using a denormalized data structure called theusing a denormalized data structure called the

star schema, developed by Ralph Kimballstar schema, developed by Ralph Kimball starts with a central fact tablestarts with a central fact table

Each row in the central fact table containsEach row in the central fact table contains

some combination of keys that makes it unique.some combination of keys that makes it unique.

These keys are called dimensions.These keys are called dimensions.


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Slicing & DicingSlicing & Dicing

Additional Functionality that can beAdditional Functionality that can be

thought of as viewing a slice of the datathought of as viewing a slice of the data

cube, particularly when values forcube, particularly when values formultiple dimensions are fixed.multiple dimensions are fixed.

Slicing/Dicing simply consists of selectingSlicing/Dicing simply consists of selecting

specific values for these attributes, whichspecific values for these attributes, whichare then displayed on top of the crossare then displayed on top of the cross--tabtab


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Rollup & DrillRollup & Drill--downdown

OLAP permit users to view data at ayOLAP permit users to view data at ay

desired level of granularity.desired level of granularity.

Rollup: moving from finerRollup: moving from finer--granularity datagranularity data

to coarser granularityto coarser granularity

DrillDrill--down: opposite to Rollupdown: opposite to Rollup


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OLAP InplementationOLAP Inplementation

Multidimensional OLAP (MOLAP)Multidimensional OLAP (MOLAP)

Relational OLAP (ROLAP)Relational OLAP (ROLAP)

Hybrid OLAP (HOLAP)Hybrid OLAP (HOLAP)


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MOLAPMOLAP

The database is stored in a special, usuallyThe database is stored in a special, usually

proprietary, structure that is optimized forproprietary, structure that is optimized for

multidimensional analysis.multidimensional analysis. + : very fast query response time because data+ : very fast query response time because data

is mostly preis mostly pre--calculatedcalculated

--:: practical limit on the size becausepractical limit on the size because the timethe time

taken to calculate the database and the spacetaken to calculate the database and the spacerequired to holdrequired to hold these prethese pre--calculated valuescalculated values


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ROLAPROLAP

The database is a standard relational databaseThe database is a standard relational database

and the database model is a multidimensionaland the database model is a multidimensional

model, often referred to as a star or snowflakemodel, often referred to as a star or snowflakemodel or schema.model or schema.

+: more scalable solution+: more scalable solution

--:: performance of the queries will be largelyperformance of the queries will be largely

governed by the complexity of the SQL and thegoverned by the complexity of the SQL and thenumber and size of thenumber and size of the tables being joined intables being joined in

the querythe query


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HOLAPHOLAP

a hybrid of ROLAPa hybrid of ROLAP and MOLAPand MOLAP

can be thought of as a virtual databasecan be thought of as a virtual database

whereby the higher levels of thewhereby the higher levels of the

database are implemented as MOLAPdatabase are implemented as MOLAP

and the lower levels of theand the lower levels of the database asdatabase as

ROLAPROLAP


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DOLAPDOLAP

The previous terms are used to refer toThe previous terms are used to refer to

server based OLAP technologiesserver based OLAP technologies

DOLAP (Desktop OLAP)DOLAP (Desktop OLAP)

DOLAP enablesDOLAP enables users to quickly pullusers to quickly pull

together small cubes that run on theirtogether small cubes that run on their

desktops or laptopsdesktops or laptops


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ConclusionConclusion

OLAP is a significant improvement overOLAP is a significant improvement over

query systemsquery systems

OLAP is an interactive system to showOLAP is an interactive system to showdifferent summaries of multidimensionaldifferent summaries of multidimensional

data by interactively selecting thedata by interactively selecting the

attributes in a multidimensional data cubeattributes in a multidimensional data cube


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ReferencesReferences

IBM Redbooks.IBM Redbooks. DB2 Cube Views: A Primer.DB2 Cube Views: A Primer. Durham, NC,Durham, NC,USA: IBM, 2003. ebrary collections. San Jose StateUSA: IBM, 2003. ebrary collections. San Jose StateUniversity.University.

Jacobson, Reed,Jacobson, Reed, Microsoft SQL Server 2005 AnalysisMicrosoft SQL Server 2005 AnalysisServices Step by StepServices Step by Step. Microsoft Press.. Microsoft Press.

Berry, Michael J. A.Berry, Michael J. A. Data Mining Techniques : ForData Mining Techniques : For

Marketing, Sales, and Customer Relationship Management.Marketing, Sales, and Customer Relationship Management.Hoboken, NJ, USA: John Wiley & Sons, Incorporated, 2004.Hoboken, NJ, USA: John Wiley & Sons, Incorporated, 2004.ebrary collections. San Jose State University.ebrary collections. San Jose State University..

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Online Analytical Processing by Hweichao Lu