Kuliah8 - StudiKasus Data Warwhouse-OLAP

7/21/2019 Kuliah8 - StudiKasus Data Warwhouse-OLAP

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18 Feb 2016 rev. 1

Data Warehousing

and OLAP

Sources:

Chapter 22, Database Systems Concepts, 4th ed., Siberschat!, "orth,

Sudarshan, #c$ra%&i, 2''2.

Chapter ((, #odern Database #anagement, )th ed., &o**er, Prescott,#c+adden, 2''.

Chapter -( and --, Database Systems, th ed., Connoy /egg, 2''4.



18 Feb 2016 rev. 2

Outline

Decision Support SystemsData Warehousing

Data Mart

OL!



18 Feb 2016 rev. "

Decision Support SystemsDecision#support systems are use$ to ma%e

business $ecisions o&ten base$ on $ata co''ecte$by on#'ine transaction#processing systems.

()amp'es o& business $ecisions*What items to stoc%+

What insurance premium to change+Who to sen$ a$vertisements to+

()amp'es o& $ata use$ &or ma%ing $ecisions ,etai' sa'es transaction $etai's

-ustomer pro'es /income age se) etc.



18 Feb 2016 rev.

Decision-Support Systems: Overview

Data analysis tas%s are simp'ie$ by specia'i3e$ too's

an$ S4L e)tensions Statistical analysis pac%ages /e.g. * S55 can be inter&ace$

ith $atabases Statistica' ana'ysis is a 'arge e'$ i'' not stu$y it here

Data mining see%s to $iscover %no'e$ge automatica''y in the

&orm o& statistica' ru'es an$ patterns &rom Large $atabases.

data warehouse archives in&ormation gathere$ &rom mu'tip'e

sources an$ stores it un$er a unie$ schema at a sing'e site. 7mportant &or 'arge businesses hich generate $ata &rom mu'tip'e $ivisions

possib'y at mu'tip'e sites

Data may a'so be purchase$ e)terna''y



18 Feb 2016 rev.

The Evolution of Data Warehousing Since 19:0s organi3ations gaine$ competitive a$vantage

through systems that automate business processes to o;er

more e<cient an$ cost#e;ective services to the customer. =roing amounts o& $ata in operationa' $atabases. Organi3ations no &ocus on ays to use operationa' $ata to

support $ecision#ma%ing as a means o& gaining competitivea$vantage.

>oever operationa' systems ere never $esigne$ to support

such business activities. Organi3ations nee$ to turn their archives o& $ata into a source

o& %no'e$ge so that a sing'e integrate$ ? conso'i$ate$ vieo& the organi3ation@s $ata is presente$ to the user.

$ata arehouse as $eeme$ the so'ution to meet thereAuirements o& a system capab'e o& supporting $ecision#

ma%ing receiving $ata &rom mu'tip'e operationa' $ata sources.



18 Feb 2016 rev. 6

A subject-oriented, integrated, time-variant, and non-volatile collection of data in support of management’sdecision-making process (nmon, !""#$%

Subject &riented' he warehouse is organi)ed around the major subjects of the enterprise

(e%g% customers, products, and sales$ rather than the major applicationareas (e%g% customer invoicing, stock control, and product sales$%

*eed to store decision support data rather than application-oriented data

ntegrated' he data warehouse integrates corporate application-oriented data from

di+erent source systems, which often includes data that is inconsistent%

ime-ariant' Data in the warehouse is only accurate and valid at some point in time or

over some time interval%

*on-volatile' Data in the warehouse is not updated in real-time but is refreshed from

operational systems on a regular basis%

Data Warehousing



Subject Oriented

or e.ample, to learn more about your company’s sales data ,

/0ho was our best customer for this item, in this

region last year1/

his ability to de2ne a data warehouse by subject matter,sales in this case, makes the data warehouse subject oriented%

Data is categori!ed and stored by business subject rather than

by application"

Operational SystemsOperational Systems

#egion

Time$ustomer

% r o d u c t$ustomer

&inancial

'nformation

Data WarehouseData WarehouseSubject (reaSubject (rea



'ntegrated

Data warehouses must put data from disparate sources intoa consistent format%



Time )ariant *time series+

Data is stored as a series of snapshots, each representing aperiod of time%

DataTime

,an./

&eb./

0ar./

Data for ,anuary

Data for &ebruary

Data for 0arch

DataData

WarehouseWarehouse



1on )olatile

Typically data in the data warehouse is not updated or deleted"

#ead#ead

2oad2oad

'1SE#T #ead'1SE#T #ead

3%D(TE3%D(TE

DE2ETEDE2ETE

Operational DatabasesOperational Databases Warehouse DatabaseWarehouse Database

1onvolatile means that4 once entered into the warehouse4 datashould not change "This is logical because the purpose of a

warehouse is to enable you to analy!e what has occurred"



18 Feb 2016 rev. 11

Data Warehousing



18 Feb 2016 rev. 12

Data Warehouse vs O2T%



18 Feb 2016 rev. 1"

Data 0art subset o& a $ata arehouse that supports the

reAuirements o& a particu'ar $epartment or business

&unction. -haracteristics inc'u$e

Focuses on on'y the reAuirements o& one $epartment or business &unction.

Do not norma''y contain $etai'e$ operationa' $ata un'i%e $ata arehouses.

More easi'y un$erstoo$ an$ navigate$.

,easons &or -reating Data Mart Bo give users access to the $ata they nee$ to ana'y3e most o&ten.

Bo improve en$#user response time $ue to the re$uction in the vo'ume o&

$ata to be accesse$.

Cui'$ing a $ata mart is simp'er compare$ ith estab'ishing a corporate $ataarehouse.

Bhe cost o& imp'ementing $ata marts is norma''y 'ess than that reAuire$ toestab'ish a $ata arehouse.



18 Feb 2016 rev. 1

Data Warehouse vs Data 0art

h ( hi



18 Feb 2016 rev. 1

Data Warehouse (rchitectures=eneric Bo#Leve' rchitecture

E

T

L

One4company-

wide

warehouse

%eriodic e5traction data is not completely current in warehouse

D t W h A hit t



18 Feb 2016 rev. 16

7n$epen$ent Data Mart

Data Warehouse Architectures

Data marts:Data marts:0ini-warehouses4 limited in scope

E

T

L

Separate ET2 for each

independent data mart

Data access comple5ity

due to multiple data marts

D W h 6 i



18 Feb 2016 rev. 1:

Data Warehouse 6ueries Bhe types o& Aueries that a DW is e)pecte$ to

anser ranges &rom the re'ative'y simp'e to the

high'y comp'e) an$ is $epen$ent on the type o&en$#user access too's use$.

()amp'es* What as the tota' revenue &or Scot'an$ in the thir$ Auarter o&

200+

What as the tota' revenue &or property sa'es &or each type o&property in 7n$onesia in 200"+

What are the three most popu'ar areas in each city &or therenting o& property in 200 an$ ho $oes this compare ith thegures &or the previous to years+

What is the re'ationship beteen the tota' annua' revenuegenerate$ by each branch o<ce an$ the tota' number o& sa'essta; assigne$ to each branch o<ce+



0odeling of Data Warehouses Bypica''y arehouse $ata is mu'ti$imensiona' ith very

'arge fact tables

()amp'es o& $imensions* item#i$ $ate?time o& sa'e store heresa'e as ma$e customer i$entier

()amp'es o& measures* number o& items so'$ price o& items

Mo$e'ing $ata arehouses* $imensions measures

Star schema* &act tab'e in the mi$$'e connecte$ to a set o&$imension tab'es

SnoEa%e schema* renement o& star schema here some

$imensiona' hierarchy is norma'i3e$ into a set o& sma''er $imension

tab'es &orming a shape simi'ar to snoEa%e

Fact conste''ations* Mu'tip'e &act tab'es share $imension tab'es

viee$ as a co''ection o& stars there&ore ca''e$ ga'a)y schema or

&act conste''ation18

'll i f S S h



'llustration of Star Schema

19

&actTable

Dimension

Table

Dimension

Table

Dimension

Table

Dimension

Table

Dimension

Table



20

E5ample of Star Schema

time78ey

day

day7of7the7wee8

month

9uarter year

time

location78ey

street

city province7or7street

country

location

Sales &act Table

time78ey

item78ey

branch78ey

location78ey

units7sold

dollars7sold

avg7sales

0easures

item78ey

item7name

brand

typesupplier7type

item

branch78ey

branch7name

branch7type

branch



'llustration of Snowfla8es Schema

21

&actTable



22

E5ample of Snowfla8e Schema

time78ey

day

day7of7the7wee8

month

9uarter

year

time

location78ey

street

city78ey

location

Sales &act Table

time78ey

item78ey

branch78ey

location78ey

units7sold

dollars7sold

avg7sales

0easures

item78eyitem7name

brand

type

supplier78ey

item

branch78ey

branch7name

branch7type

branch

supplier78ey

supplier7typ

supplier

city78ey

city province7or7street

country

city

'll i f & $ ll i



'llustration of &act $onstellations

-ontent Deve'opment =DLCatch 2

2"

&act

Table

&actTable

&act

Table

E le f & t $ tell ti



Data Warehouse2

E5ample of &act $onstellation

time78ey

day

day7of7the7wee8 month

9uarter

year

time

location78ey

street

city

province7or7street

country

location

Sales &act Table

time78ey

item78ey

branch78ey location78ey

units7sold

dollars7sold

avg7sales0easure

s

item78ey

item7name

brand

type

supplier7type

item

branch78ey

branch7name

branch7type

branch

Shipping &act Tabl

time78ey

item78ey

shipper78ey from7location

to7location

dollars7cost

units7shipped

shipper78ey

shipper7name

location78ey

shipper7type

shipper

Star Schema Example



18 Feb 2016 rev. 2

Star Schema Example

usiness 'ntelligence Technologies



18 Feb 2016 rev. 26

usiness 'ntelligence Technologies

ccompanying the groth in $ata arehousing is an ever#increasing $eman$ by users &or more poer&u' access

too's that provi$e a$vance$ ana'ytica' capabi'ities. Bhere are to main types o& access too's avai'ab'e to meet

this $eman$ name'y On'ine na'ytica' !rocessing /OL!an$ $ata mining.

OL! an$ Data Mining $i;er in hat they o;er the useran$ because o& this they are comp'ementary techno'ogies.

n environment that inc'u$es a $ata arehouse /or morecommon'y one or more $ata marts together ith too'ssuch as OL! an$ ?or $ata mining are co''ective'y re&erre$

to as Business Intelligence (BI) technologies.

Data (nalysis and O2(%



18 Feb 2016 rev. 2:

ggregate &unctions summari3e 'arge vo'umes o& $ata

On'ine na'ytica' !rocessing /OL! 7nteractive ana'ysis o& $ata a''oing $ata to be summari3e$ an$ viee$ in

$i;erent ays in an on'ine &ashion /ith neg'igib'e $e'ay

OL! enab'es users to gain a $eeper un$erstan$ing an$ %no'e$ge

about various aspects o& their corporate $ata through &ast consistent

interactive access to a i$e variety o& possib'e vies o& the $ata.

Bypes o& ana'ysis ranges &rom basic navigation an$ brosing /s'icing

an$ $icing to ca'cu'ations to more comp'e) ana'yses such as timeseries an$ comp'e) mo$e'ing.

Data (nalysis and O2(%

#epresentation of 0ulti dimensional Data



18 Feb 2016 rev. 28

#epresentation of 0ulti-dimensional Data ()amp'e o& to#$imensiona' Auery.

What is the tota' revenue generate$ by property sa'es in each city ineach Auarter o& 200+@

-hoice o& representation is base$ on types o& Aueries en$#user may as%.

-ompare representation # "#e'$ re'ationa' tab'e vs 2#$imensiona' matri).

#epresentation of 0ulti-dimensional Data



18 Feb 2016 rev. 29

()amp'e o& three#$imensiona' Auery. GWhat is the tota' revenue generate$ by property sa'es &or each

type o& property /F'at or >ouse in each city in each Auarter o&200+@

-ompare representation # &our#e'$ re'ationa'tab'e versus three#$imensiona' cube.

#epresentation of 0ulti dimensional Data

$ross Tabulation of sales by item-name and color



18 Feb 2016 rev. "0

$ross Tabulation of sales by item name and color

Bhe tab'e above is an e)amp'e o& a cross-tabulation /cross-tab a'so re&erre$ to as a pivot-table.

cross#tab is a tab'e here Ha'ues &or one o& the $imension attributes &orm the ro

hea$ers va'ues &or another $imension attribute &orm theco'umn hea$ers Other $imension attributes are 'iste$ on top

Ha'ues in in$ivi$ua' ce''s are /aggregates o& the va'ues o& the

$imension attributes that speci&y the ce''.

#elational #epresentation of $rosstabs



18 Feb 2016 rev. "1

#elational #epresentation of $rosstabs

Crosstabs can be

represented as relationsThe valueall is used torepresent aggregates

The SQL:1999 standardactually uses null valuesin place ofall

Three-Dimensional Data $ube



18 Feb 2016 rev. "2

Three Dimensional Data $ube Adata cube is a multidimensional generalization of a crosstab

Cannot view a three-dimensional object in its entirety

but crosstabs can be used as views on a data cube

Online (nalytical %rocessing *O2(%+



18 Feb 2016 rev. ""

Online (nalytical %rocessing *O2(%+ Bhe operation o& changing the $imensions use$ in a

cross#tab is ca''e$ pivoting

Suppose an ana'yst ishes to see a cross#tab on item-name an$ color &or a )e$ va'ue o& size &or e)amp'e'arge instea$ o& the sum across a'' si3es. Such an operation is re&erre$ to as slicing.

Bhe operation is sometimes ca''e$ dicing particu'ar'y hen va'ues &or

mu'tip'e $imensions are )e$. Bhe operation o& moving &rom ner#granu'arity $ata to a

coarser granu'arity is ca''e$ a rollup.

Bhe opposite operation # that o& moving &rom coarser#granu'arity $ata to ner#granu'arity $ata I is ca''e$ a drilldown.




18 Feb 2016 rev. "

S'icing a Data -ube

y g * +




18 Feb 2016 rev. "

()amp'e o&Dri'' Don

y g * +Summary report

Drill-down with

color added

Starting ithsummary $atausers can obtain$etai's &or particu'arce''s

;ierarchies on Dimensions



18 Feb 2016 rev. "6

;ierarchies on DimensionsHierarchy on dimension attributes: lets dimensions to be viewedat different levels of detail

E.g. the dimension DateTime can be used to aggregate by hour of day,date, day of week, month, quarter or year

E5tended (ggregation



18 Feb 2016 rev. ":

te ded gg egat o

$vantages o& S4L inc'u$e that it is easy to 'earnnon#proce$ura' &ree#&ormat DCMS#in$epen$ent an$

that it is a recogni3e$ internationa' stan$ar$. >oever maJor 'imitation o& S4L /S4L#92 is the

inabi'ity to anser routine'y as%e$ business Aueriessuch as computing the percentage change in va'ues

beteen this month an$ a year ago or to computemoving averages cumu'ative sums an$ otherstatistica' &unctions.

S4L*1999 OL! e)tensions provi$e a variety o&

aggregation &unctions to a$$ress some 'imitations Supporte$ by severa' $atabases inc'u$ing Orac'e an$ 7CM

DC2

E5tended (ggregation in S62:<=== > $3E



18 Feb 2016 rev. "8

gg g 6 Bhe cube operation computes union o& group by@s on

every subset o& the specie$ attributes

(.g. consi$er the Aueryselect item-name, color, size, sum/number from salesgroup by cube/item-name, color, size

Bhis computes the union o& eight $i;erent groupings o& the sales

re'ation*

K /item-name, color, size /item-name, color

/item-name, size /color, size/item-name /color

/size /

here / $enotes an empty group by 'ist. For each grouping the resu't contains the nu'' va'ue &or

attributes not present in the grouping.

E5tended (ggregation > $3E *$ont"+



18 Feb 2016 rev. "9

Show all possible subtotals for sales of propertiesby branches oces in Aberdeen, dinburgh, and

!lasgow for the months of August and Septemberof "##$%

S&' propertyype, yearonth, city, S*(saleAmount) AS

sales+ Branch, .roperty+or Sale, .ropertySale

/0 Branch%branch1o 2 .ropertySale%branch1o

A13 .roperty+orSale%property1o 2 .ropertySale%property1o

A13 .ropertySale%yearonth I1 (4"##$-#54, 4"##$-#64)

A13 Branch%city I1 (7Aberdeen8, 7dinburgh8, 7!lasgow8)

!*. B9 '*B(propertyype, yearonth, city):

E5tended (ggregation > $3E *$ont"+



18 Feb 2016 rev. 0

E5tended (ggregation > #O223% *$ont"+



18 Feb 2016 rev. 1

Bhe rollup construct generates union on every pre) o&specie$ 'ist o& attributes

(.g.select item-name color size sum/number from salesgroup by rollup/item-name, color, size

=enerates union o& &our groupings*

K /item-name, color, size /item-name, color /item-name /

,OLL! supports ca'cu'ations using aggregations such asSM -OB MN M7 an$ H= at increasing 'eve's o&

aggregation &rom the most $etai'e$ up to a gran$ tota'.




18 Feb 2016 rev. 2

Show the totals for sales of ;ats or houses bybranch oces located in Aberdeen, dinburgh,

or !lasgow for the months of August andSeptember of "##$%

S(L(-B propertyBype yearMonth city SM/sa'emount S

sa'es

F,OM Cranch !ropertyFor Sa'e !ropertySa'eW>(,( Cranch.brancho !ropertySa'e.brancho

D !ropertyForSa'e.propertyo !ropertySa'e.propertyo

D !ropertySa'e.yearMonth 7 /P200#08P P200#09P

D Cranch.city 7 /Gber$een@ G($inburgh@ G='asgo@

=,O! CQ ,OLL!/propertyBype yearMonth cityR




18 Feb 2016 rev. "

Elementary O2(% Operators



18 Feb 2016 rev.

Supports a variety of operations suchas rankings and window calculations%

0indowing allows the calculation ofcumulative and moving aggregationsusing functions such as S34, A5,

4*, and 6&3*%

Documents

Kuliah8 - StudiKasus Data Warwhouse-OLAP