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$ra%&i, 2''2.
Chapter ((, #odern Database #anagement, )th ed., &o**er, Prescott,#c+adden, 2''.
Chapter -( and --, Database Systems, th ed., Connoy /egg, 2''4.
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Outline
Decision Support SystemsData Warehousing
Data Mart
OL!
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Decision Support SystemsDecision#support systems are use$ 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 $etai's
-ustomer pro'es /income age se) etc.
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Decision-Support Systems: Overview
Data analysis tas%s are simp'ie$ 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
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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' $ata sources.
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A subject-oriented, integrated, time-variant, and non-volatile collection of data in support of management’sdecision-making process (nmon, !""#$%
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$%
*eed to store decision support data rather than application-oriented data
ntegrated' he data warehouse integrates corporate application-oriented data from
di+erent source systems, which often includes data that is inconsistent%
ime-ariant' Data in the warehouse is only accurate and valid at some point in time or
over some time interval%
*on-volatile' Data in the warehouse is not updated in real-time but is refreshed from
operational systems on a regular basis%
Data Warehousing
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Subject Oriented
or e.ample, to learn more about your company’s sales data ,
/0ho was our best customer for this item, in this
region last year1/
his ability to de2ne a data warehouse by subject matter,sales in this case, makes the data warehouse subject oriented%
Data is categori!ed and stored by business subject rather than
by application"
Operational SystemsOperational Systems
#egion
Time$ustomer
% r o d u c t$ustomer
&inancial
'nformation
Data WarehouseData WarehouseSubject (reaSubject (rea
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'ntegrated
Data warehouses must put data from disparate sources intoa consistent format%
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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
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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"
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Data Warehousing
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Data Warehouse vs O2T%
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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 $ata arehouse.
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Data Warehouse vs Data 0art
h ( hi
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Data Warehouse (rchitectures=eneric Bo#Leve' rchitecture
E
T
L
One4company-
wide
warehouse
%eriodic e5traction data is not completely current in warehouse
D t W h A hit t
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7n$epen$ent Data Mart
Data Warehouse Architectures
Data marts:Data marts:0ini-warehouses4 limited in scope
E
T
L
Separate ET2 for each
independent data mart
Data access comple5ity
due to multiple data marts
D W h 6 i
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Data Warehouse 6ueries Bhe types o& Aueries that a DW is e)pecte$ 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+
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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
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'llustration of Star Schema
19
&actTable
Dimension
Table
Dimension
Table
Dimension
Table
Dimension
Table
Dimension
Table
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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
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'llustration of Snowfla8es Schema
21
&actTable
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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
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'llustration of &act $onstellations
-ontent Deve'opment =DLCatch 2
2"
&act
Table
&actTable
&act
Table
E le f & t $ tell ti
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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
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Star Schema Example
usiness 'ntelligence Technologies
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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(%
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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
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#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
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()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
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$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
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#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
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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(%+
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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.
Online (nalytical %rocessing *O2(%+
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S'icing a Data -ube
y g * +
Online (nalytical %rocessing *O2(%+
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()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
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;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
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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:<=== > $3E
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gg g 6 Bhe cube operation computes union o& group by@s on
every subset o& the specie$ 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"+
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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"+
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E5tended (ggregation > #O223% *$ont"+
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Bhe rollup construct generates union on every pre) o&specie$ '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'.
E5tended (ggregation > #O223% *$ont"+
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18 Feb 2016 rev. 2
Show the totals for sales of ;ats or houses bybranch oces located in Aberdeen, dinburgh,
or !lasgow for the months of August andSeptember of "##$%
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
E5tended (ggregation > #O223% *$ont"+
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18 Feb 2016 rev. "
Elementary O2(% Operators
7/21/2019 Kuliah8 - StudiKasus Data Warwhouse-OLAP
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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*%