Knowledge a
nd Inte
lligent
Knowledge a
nd Inte
lligent
Tech
nology for Business
Tech
nology for Business
Pro
cess
es Optim
ization (SAP)
Pro
cess
es Optim
ization (SAP)
JASS 2
008
JASS 2
008
Stu
dent: C
onstantine
Stu
dent: C
onstantine S
onkin
Sonkin
Pro
fess
or: D
r.
Pro
fess
or: D
r. V
iach
eslav
Viach
eslav
P.
P. Shkodirev
Shkodirev
Saint
Saint --Pete
rsburg
Sta
te
Pete
rsburg
Sta
te P
olyte
chnical
Polyte
chnicalUniversity
University
Conte
nts
Conte
nts
��In
troduction
Introduction
��Data
Ware
housing
Data
Ware
housing
��Data
Mining
Data
Mining
��Business
Inte
lligence
Business
Inte
lligence
��SAP
SAP
Introduction
Introduction
��Pro
cess
fro
m d
ata
to
Pro
cess
fro
m d
ata
to
decisions involve:
decisions involve:
��data
base
tech
nology
data
base
tech
nology
��data
mining
data
mining
��business
inte
lligence
business
inte
lligence
��Main g
oal of
Main g
oal of advance
d d
ata
analysis
advance
d d
ata
analysisis to m
ake
is to m
ake
decisions
decisionsth
at lead d
irectly
that lead d
irectly to b
enefits
to b
enefits
Introduction.
Introduction.
Fro
m d
ata
to d
ecisions
Fro
m d
ata
to d
ecisions
��Phase
1Phase
1��
stru
cture
dstru
cture
ddata
data
store
sstore
sfilin
gfilin
gre
levant
relevantdata
data
;;
��decision support task
s decision support task
s were
perform
ed centrally
were
perform
ed centrally
��Phase
2Phase
2��
decision support
decision support
implem
ente
d o
fflin
eim
plem
ente
d o
fflin
e
��data
analysis te
chniques
data
analysis te
chniques
��Phase
3Phase
3��
diversity o
f th
e d
ata
diversity o
f th
e d
ata
store
s;
store
s;
��ware
house
fra
mework
s;ware
house
fra
mework
s;
��advance
d d
ata
analysis
advance
d d
ata
analysis
tech
niques
tech
niques
Introduction.
Introduction.
Definitions
Definitions
��Data Warehouse
Data Warehouse: is a
: is a
pro
cess
pro
cess
and
and a
rchitecture
architecture
that
that
requires ro
bust p
lanning to im
plem
ent a p
latform
for
requires ro
bust p
lanning to im
plem
ent a p
latform
for
decision
decision-- m
aking p
roce
sses and B
I.m
aking p
roce
sses and B
I.
Includes: selection, co
nversion, transform
ation,
Includes: selection, co
nversion, transform
ation,
conso
lidation, inte
gra
tion fro
m m
ultiple o
pera
tional data
co
nso
lidation, inte
gra
tion fro
m m
ultiple o
pera
tional data
so
urces to
a targ
et DBMS
sources to
a targ
et DBMS
��Data Mining
Data Mining: is the
: is the p
roce
sspro
cess
of
of disco
vering
disco
vering
meaningfu
l m
eaningfu
l new correlations and tre
nds by sifting thro
ugh larg
e
new correlations and tre
nds by sifting thro
ugh larg
e
am
ounts o
f data
sto
red in reposito
ries, u
sing p
attern
am
ounts o
f data
sto
red in reposito
ries, u
sing p
attern
re
cognition a
s well as statistical, m
ach
ine learn
ing, and
reco
gnition a
s well as statistical, m
ach
ine learn
ing, and
math
em
atica
l m
ath
em
atica
l te
chniques
tech
niques
��Business Intelligence
Business Intelligence: BI is the u
ser
: BI is the u
ser --ce
nte
red
cente
red p
roce
sspro
cess
of
of
exploring d
ata
, exploring d
ata
, data
relationsh
ips
data
relationsh
ipsand
and tre
nds
trends--th
ere
by
there
by
helping to im
pro
ve o
vera
llhelping to im
pro
ve o
vera
lldecision m
aking.
decision m
aking.
Data
Ware
housing
Data
Ware
housing
��A
A data warehouse
data warehouse
is a
physica
l data
set enablin
g a
n
is a
physica
l data
set enablin
g a
n
inte
gra
ted view o
f th
e u
nderlying
inte
gra
ted view o
f th
e u
nderlying D
ata
Sources
Data
Sources..
C
reat
ing
at Re
port
s C
reat
ing
Gra
phic
s C
alcu
latin
g T
able
s A
naly
sis
Met
hods
Ext
erna
l D
ata
Ext
erna
l D
ata
Ope
rati
ve
Dat
an O
pera
tive
D
ata
Dat
a W
areh
ouse
Dat
a C
olle
ctio
n 1.
Ori
ente
d by
sub
ject
2. In
tegr
ated
3.
Con
stan
t 4.
Tim
e re
fere
nced
DW in broad sense
DW in narrower sense
Data
Ware
housing
Data
Ware
housing
��DW
is an a
rchitecture
for
DW
is an a
rchitecture
for
five com
ponents:
five com
ponents:
••BI to
ols
BI to
ols
••DW
adm
inistration
DW
adm
inistration
••DW
Data
base
MS
DW
Data
base
MS
••Data
Data
coversion
coversion
and
and
extraction
extraction
••Opera
tional data
source
Opera
tional data
source
��Thre
eThre
e-- L
evel DW
Conce
pt:
Level DW
Conce
pt:
Data
Data
Sta
ging
Sta
ging
Data
Data
Sto
rage
Sto
rage
Info
rmation A
nalysis
Info
rmation A
nalysis
Data
Ware
housing
Data
Ware
housing
��Implementation difficulties
Implementation difficulties::
��DW
can’t b
e taken fro
m a
shelf
DW
can’t b
e taken fro
m a
shelf ––
has to
be
has to
be
com
pile
d u
sing a
variety
of co
mponents
com
pile
d u
sing a
variety
of co
mponents
��Clear idea o
f goals a
nd b
enefits is a n
ece
ssity
Clear idea o
f goals a
nd b
enefits is a n
ece
ssity
��DW
is an a
rchitecture
, not a p
roduct
DW
is an a
rchitecture
, not a p
roduct
��Can’t b
e b
ought
Can’t b
e b
ought ––
need to b
e b
uilt
need to b
e b
uilt
Data
Mining
Data
Mining
Data
Data
mining
mining
--th
eth
eextra
ction
extra
ction
of
ofhidden
hidden
pre
dictive
pre
dictive
info
rmation
info
rmation
from
from
larg
elarg
edata
base
sdata
base
s
Data
Data
mining
mining
tools
tools
pre
dict
pre
dictfu
ture
futu
retrends
trendsand
and
behaviors
behaviors
, ,
allo
wing
allo
wing
totom
ake
make
pro
active
pro
active, , knowledge
knowledge-- d
riven
driven
decisions
decisions. .
Thre
e m
ain reaso
ns fo
r data
mining:
Thre
e m
ain reaso
ns fo
r data
mining:
��Correcting d
ata
Correcting d
ata
––co
rrection o
f co
rrection o
f inco
mplete
ness
and contradicto
ry
inco
mplete
ness
and contradicto
ry
info
rmation
info
rmation
��Disco
vering knowledge
Disco
vering knowledge ––
to
to
dete
rmine h
idden relationsh
ips,
dete
rmine h
idden relationsh
ips,
pattern
s and correlations from
data
pattern
s and correlations from
data
��Visualiz
ing d
ata
Visualiz
ing d
ata
––to
hum
anize the
to h
um
anize the
mass
of data
, display the d
ata
mass
of data
, display the d
ata
Data
Data
correcting
correcting
Disco
vering
Disco
vering
knowledge
knowledge
Visualiz
ing d
ata
Visualiz
ing d
ata
Data
Mining
Data
Mining
Methodology
Methodology::
1.
1.
Data
base
selection a
nd p
repara
tion
Data
base
selection a
nd p
repara
tion
••identifica
tion o
f data
base
s and factors to b
e
identifica
tion o
f data
base
s and factors to b
e
explore
dexplore
d
••pre
para
tion includes filling in m
issing values and
pre
para
tion includes filling in m
issing values and
rectifying e
rrors
rectifying e
rrors
2.
2.
Cluster and featu
re a
nalysis
Cluster and featu
re a
nalysis
••data
base
gro
ups are
divided u
sing clustering
data
base
gro
ups are
divided u
sing clustering
tech
niques
tech
niques
••m
ore
deta
iled featu
re a
nalysis to
find the m
ain
more
deta
iled featu
re a
nalysis to
find the m
ain
factors
factors
……
……
Data
Mining
Data
Mining
3.
3.
Tool se
lection
Tool se
lection
••How m
any e
xam
ples?
How m
uch
pro
cess
ing? W
hat
How m
any e
xam
ples?
How m
uch
pro
cess
ing? W
hat
are
outp
uts? Sim
plic
ity o
f update
?are
outp
uts? Sim
plic
ity o
f update
?
4.
4.
Hypoth
esis te
sting a
nd knowledge d
isco
very
Hypoth
esis te
sting a
nd knowledge d
isco
very
••hypoth
ese
s are
form
ed a
nd tested (to
p d
own)
hypoth
ese
s are
form
ed a
nd tested (to
p d
own)
••new relationsh
ips are
disco
vere
d (bottom
up)
new relationsh
ips are
disco
vere
d (bottom
up)
••what
what --if a
nalyse
s m
ay b
e p
erform
ed
if a
nalyse
s m
ay b
e p
erform
ed
5.
5.
Knowledge a
pplic
ation
Knowledge a
pplic
ation
••te
sted rules create
d fro
m the d
isco
very
pro
cess
te
sted rules create
d fro
m the d
isco
very
pro
cess
can b
e d
irectly a
dded to e
ither pro
cedura
l co
de o
r ca
n b
e d
irectly a
dded to e
ither pro
cedura
l co
de o
r
into
a knowledge
into
a knowledge-- b
ase
d system
.base
d system
.
Data
Mining
Data
Mining
��Data
mining p
roce
ss:
Data
mining p
roce
ss:
Data
Mining
Data
Mining
Techniques for DM
Techniques for DM
::
��Visualiz
ation
Visualiz
ation
��Sta
tistics
Sta
tistics
��clustering
clustering
��fa
ctor analysis
factor analysis
��pre
diction
pre
diction
��In
duction
Induction
��Decision tre
eDecision tre
e
��Neura
l Netw
ork
sNeura
l Netw
ork
s
••unsu
perv
ised
unsu
perv
ised
••su
perv
ised
superv
ised
��co
lore
d 2
colore
d 2
-- D
D ––
44-- D
D
repre
senta
tion
repre
senta
tion
��Choice for initial analysis
Choice for initial analysis
Identifica
tion p
redictive
Identifica
tion p
redictive
factors
factors
��Reaso
ning fro
m specific
Reaso
ning fro
m specific
facts to
reach
a h
ypoth
esis
facts to
reach
a h
ypoth
esis
��MMultila
yere
dultila
yere
dnetw
ork
netw
ork
architecture
sarchitecture
sth
at
thatlearn
learn
how
how
totoso
lve
solve
a
a p
roblem
pro
blem
Data
Mining
Data
Mining
��Data
mining p
roce
ss:
Data
mining p
roce
ss:
Business
Inte
lligence
Business
Inte
lligence
��Business
inte
lligence
(BI)
Business
inte
lligence
(BI) --
the p
roce
sses and
the p
roce
sses and
tech
nologies use
d for co
llecting, m
anaging, and
tech
nologies use
d for co
llecting, m
anaging, and
reporting d
ecision
reporting d
ecision-- o
riente
d d
ata
. oriente
d d
ata
.
��The b
usiness
inte
lligence
architecture
is:
The b
usiness
inte
lligence
architecture
is:
��a subse
t of th
e o
vera
ll IT
architecture
a subse
t of th
e o
vera
ll IT
architecture
��an u
mbre
lla term
for an e
nte
rprise
an u
mbre
lla term
for an e
nte
rprise
-- wide set of
wide set of
system
s, a
pplic
ations, a
nd g
overn
ance
pro
cess
es
system
s, a
pplic
ations, a
nd g
overn
ance
pro
cess
es
��so
phistica
ted a
nalytics
, by a
llowing d
ata
and
sophistica
ted a
nalytics
, by a
llowing d
ata
and
analyse
s to
flow to those
who n
eed them
, when
analyse
s to
flow to those
who n
eed them
, when
they n
eed them
.th
ey n
eed them
.
Business
Inte
lligence
Business
Inte
lligence
��BI
BI architecture
architecture
ininsix
six
elem
ents
elem
ents::
1.
1.
Data
Data
managem
en
managem
entt
2.
2.
Tra
nsform
ation
Tra
nsform
ation
tools
tools
and
and
pro
cess
es
pro
cess
es
3.
3.
Reposito
ries
Reposito
riesof
ofdata
data
and
and
meta
data
meta
data
4.
4.
Applic
ations
Applic
ationsand
and
oth
er
oth
erso
ftware
software
tools
tools
for
foranalysis
analysis ..
5.
5.
Pre
senta
tion
Pre
senta
tion
tools
tools
and
and
applic
ations
applic
ations
6.
6.
Opera
tional
Opera
tionalpro
cess
es
pro
cess
es(( secu
rity
secu
rity
, , error
errorhandlin
ghandlin
g,,
archiving
archiving
and
and
privacy
privacy
))
Business
Inte
lligence
Business
Inte
lligence
��BI pro
cess
es:
BI pro
cess
es:
Business
Inte
lligence
Business
Inte
lligence
BI
BI pro
cess
es
pro
cess
esand
and
task
sta
sksca
nca
nbe
be
sum
marize
dsu
mm
arize
das
as ::
.. ��Understa
nd
Understa
nd the
the
business
business
pro
blem
pro
blem
totobe
be
addre
ssed
addre
ssed
��Design
Design
the
the
ware
house
ware
house
��Learn
Learn
how
how
totoextract
extract
source
source
data
data
and
and
transform
transform
itit
��Load
Load
the
the
ware
house
ware
house
��Connect
Connect
use
rsuse
rsand
and
pro
vide
pro
vide
them
them
with
with
tools
tools
and
and
way
way
totofind
find
the
the
data
data
of
ofinte
rest
inte
rest
ininth
eth
eware
house
ware
house
��Use
Use
the
the
data
data
totopro
vide
pro
vide
business
business
knowledge
knowledge
��Adm
iniste
rAdm
iniste
rall
allth
ese
these
pro
cess
es
pro
cess
es
��DDocu
ment
ocu
mentall
allth
isth
isinfo
rmation
info
rmation
ininm
eta
meta
-- data
data
SAP D
evelopm
ent
SAP D
evelopm
ent
1.
1.
SAP introduce
d a
s an E
RP vendor
SAP introduce
d a
s an E
RP vendor
2.
2.
SAP a
nd m
ultidim
ensional te
chnology
SAP a
nd m
ultidim
ensional te
chnology
��In
foCubes
Info
Cubes––
data
in form
suitable to a
nalysis
data
in form
suitable to a
nalysis
3.
3.
SAP a
nd D
ata
Ware
house
s:SAP a
nd D
ata
Ware
house
s:��
BW
1.2
b
BW
1.2
b --
introduction o
f introduction o
f In
foCubes
Info
Cubesand B
usiness
and B
usiness
Conte
nt
Conte
nt
��BW
2.0
b
BW
2.0
b --
introduction o
f ODS
introduction o
f ODS
��BW
2.1
c BW
2.1
c --analytica
l co
mponents
analytica
l co
mponents
��BW
3.0
BW
3.0
--enhance
ment of ODS into
DW
;
enhance
ment of ODS into
DW
;
creation o
f analytica
l applic
ations
creation o
f analytica
l applic
ations
SAP Info
rmation F
actory
SAP Info
rmation F
actory
SAP Info
rmation F
actory
SAP Info
rmation F
actory
1.
1.
SAP support o
f ERP
SAP support o
f ERP
��SAP w
as th
e w
orlds pioneer in this a
rena
SAP w
as th
e w
orlds pioneer in this a
rena
��ERP foundation g
ives SAP a
basis fo
r gath
ering
ERP foundation g
ives SAP a
basis fo
r gath
ering
and m
anaging d
ata
and m
anaging d
ata
2.
2.
SAP support o
f data
ware
house
SAP support o
f data
ware
house
��first fo
ray w
as with the h
elp o
f an O
pera
tional
first fo
ray w
as with the h
elp o
f an O
pera
tional
Data
Sto
reData
Sto
re
��ODS conta
ins gra
nular data
ODS serv
es as a
ODS conta
ins gra
nular data
ODS serv
es as a
ware
house
and o
pera
tes in a
mode o
f openness
ware
house
and o
pera
tes in a
mode o
f openness
3.
3.
SAP support for data
marts
SAP support for data
marts
��SAP h
as
SAP h
as In
foCubes
Info
Cubes
��In
foCubes
Info
Cubesplays a robust role
plays a robust role
SAP Info
rmation Factory
SAP Info
rmation Factory
4.
4.
SAP's support o
f th
e w
eb e
nvironm
ent
SAP's support o
f th
e w
eb e
nvironm
ent
��m
ySAP.com
mySAP.com
has inte
rface
s from
the w
eb to a
nd
has inte
rface
s from
the w
eb to a
nd
from
the corp
ora
te infrastru
cture
from
the corp
ora
te infrastru
cture
5.
5.
SAP support for DSS a
pplic
ations
SAP support for DSS a
pplic
ations
��th
e conce
pt of th
e "co
ckpit"
the conce
pt of th
e "co
ckpit" --fo
r m
anagem
ent with
for m
anagem
ent with
the n
eed for up to d
ate
and a
wide variety o
f th
e n
eed for up to d
ate
and a
wide variety o
f info
rmation.
info
rmation.
��SAP's o
ffering o
f SEM
SAP's o
ffering o
f SEM ––
Strate
gic E
nte
rprise
s Strate
gic E
nte
rprise
s Managem
ent includes:
Managem
ent includes:
••SEM
SEM-- B
IC
BIC
--business
info
rmation colle
ction,
business
info
rmation colle
ction,
••SEM
SEM-- B
PS
BPS --
planning a
nd sim
ulation,
planning a
nd sim
ulation,
••SEM
SEM-- B
CS
BCS --
business
conso
lidation,
business
conso
lidation,
••SEM
SEM-- C
PM
CPM --
corp
ora
te p
erform
ance
monitoring,
corp
ora
te p
erform
ance
monitoring,
••SEM
SEM-- S
RM
SRM --
stakeholder re
lationsh
ip m
anagem
ent
stakeholder re
lationsh
ip m
anagem
ent
SAP
SAP
SAP
SAP
SAP
SAP
Refe
rence
sRefe
rence
s
��Basics
Basics
--Reporting &
Analysis
Reporting &
Analysis --Modelin
g &
Sta
ging
Modelin
g &
Sta
ging --
Data
Mining,
Data
Mining,
Helm
ut
Helm
ut Krcm
ar
Krcm
arSAP U
CC a
t SAP U
CC a
t Tech
nisch
eTech
nisch
eUniversität
UniversitätMünch
en
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ith
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Kurt
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Thearling
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ew S
cience
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inning H
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ard
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ool Pre
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ew S
cience
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arv
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Sch
ool Pre
ss, March 2
007
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��The A
rchitecture
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lligence
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Jeanne G
. Harris
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. Harris
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Inte
lligence
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on S
/390,
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Inte
lligence
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/390, ibm
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Conclusion
Conclusion
Thank you for your attention
Thank you for your attention
May I answ
er your questions?
May I answ
er your questions?
