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I
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Vo
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15
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3
4
5
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2346
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m
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s
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h
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ce
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th
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ap
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b
y
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o
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s
in
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m
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im
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tatio
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s
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a
m
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s
tem
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MA
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ased
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d
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in
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ts
(
FIPA)
s
tan
d
ar
d
s
f
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in
tellig
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ag
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ts
[
2
1
]
,
an
d
its
ap
p
licatio
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in
r
elatio
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al
to
b
ig
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ata
UM
L
m
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(
R
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ca
s
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y
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s
tr
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ess
in
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to
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atic
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atch
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h
e
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ap
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s
tr
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o
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ir
s
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ar
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m
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s
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2.
RE
L
AT
E
D
WO
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e
v
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e
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c
h
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q
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,
s
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c
h
a
s
S
I
B
[
4
]
–
[
6
]
,
S
I
G
[
7
]
–
[
9
]
,
S
I
M
[
1
0
]
–
[
1
5
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,
a
n
d
C
S
L
[
1
6
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–
[
2
0
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,
e
a
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s
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A
M
a
p
p
r
o
a
c
h
.
T
ab
le
1
.
SW
OT
an
aly
s
is
o
f
m
atch
in
g
ap
p
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ac
h
es
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t
a
t
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t
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f
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d
t
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c
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q
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(
S
I
B
)
C
h
a
r
a
c
t
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s
t
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c
s
U
ses
u
n
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q
u
e
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d
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t
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f
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r
s
(
U
U
I
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s)
t
o
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t
a
b
l
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sh
c
o
r
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d
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c
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w
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m
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me
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s
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a
s
t
a
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d
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o
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f
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b
u
t
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g
l
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s w
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t
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s m
o
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s.
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o
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t
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Q
u
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k
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m
p
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me
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t
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p
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e
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t
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o
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s,
p
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b
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t
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ma
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.
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g
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a
t
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a
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h
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n
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f
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p
r
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t
s)
f
o
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t
s.
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q
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t
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l
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t
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s
sc
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o
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t
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C
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m
p
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n
d
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t
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m
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R
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d
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st
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a
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s
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sp
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l
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o
m
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sp
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c
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n
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a
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t
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ms
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o
si
t
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v
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t
e
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r
a
t
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s
sema
n
t
i
c
s.
N
e
g
a
t
i
v
e
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M
a
n
u
a
l
a
l
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sp
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f
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x
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h
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r
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s
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i
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s
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
A
r
ch
itectu
r
e
o
f m
u
lti
-
a
g
en
t sy
s
tems fo
r
g
en
era
tive
a
u
to
ma
tic
ma
tch
in
g
…
(
Zo
u
h
a
ir
I
b
n
B
a
to
u
ta
)
2347
As
em
p
h
asized
in
th
e
SW
OT
an
aly
s
is
in
T
ab
le
1
,
all
e
x
is
tin
g
ap
p
r
o
ac
h
es
eith
er
r
eq
u
ir
e
m
an
u
al
m
atch
in
g
o
r
lack
th
e
ca
p
ab
ilit
y
f
o
r
au
to
m
atic
m
o
d
el
g
e
n
er
a
tio
n
.
Fo
r
in
s
tan
ce
,
SIB
is
f
ast
b
u
t
u
n
s
u
itab
le
f
o
r
h
eter
o
g
en
e
o
u
s
m
o
d
els
an
d
d
o
es
n
o
t
s
u
p
p
o
r
t
au
t
o
m
atic
g
e
n
er
atio
n
.
SIG
co
m
p
ar
es
in
d
e
p
en
d
en
t
m
o
d
els
b
u
t
r
eq
u
ir
es
u
s
er
-
d
e
f
in
ed
f
u
n
ctio
n
s
an
d
s
im
ilar
ly
lack
s
au
to
m
ati
c
g
en
er
atio
n
.
W
h
ile
SIM
is
ac
cu
r
ate,
it
r
elies
o
n
f
ix
ed
h
eu
r
is
tics
an
d
d
o
es
n
o
t
s
u
p
p
o
r
t
au
to
m
atic
g
e
n
er
atio
n
.
L
astl
y
,
C
SL
in
teg
r
ates
s
e
m
an
tics
b
u
t
r
eq
u
ir
es
m
an
u
al
s
p
ec
if
icatio
n
s
an
d
d
ep
en
d
s
o
n
f
ix
ed
h
e
u
r
is
tics
.
T
o
ad
d
r
ess
th
ese
lim
itatio
n
s
,
we
p
r
esen
t
a
n
ew
ap
p
r
o
ac
h
,
GAM
,
wh
ich
will b
e
d
etailed
in
th
e
n
ex
t sectio
n
.
3.
M
E
T
H
O
DO
L
O
G
Y
US
E
D
I
n
th
is
s
ec
tio
n
,
we
p
r
esen
t
th
e
m
eth
o
d
o
lo
g
y
em
p
lo
y
ed
in
th
e
d
esig
n
an
d
im
p
lem
en
tatio
n
o
f
o
u
r
n
ew
ap
p
r
o
ac
h
,
GAM
.
T
h
is
m
eth
o
d
o
lo
g
y
is
s
tr
u
ctu
r
ed
ar
o
u
n
d
th
e
f
o
llo
win
g
k
ey
co
m
p
o
n
en
ts
:
th
e
GAM
ar
ch
itectu
r
e,
wh
ich
d
ef
in
es
th
e
o
v
er
all
s
tr
u
ctu
r
e
o
f
th
e
ap
p
r
o
ac
h
;
th
e
g
en
er
ativ
e
m
atch
in
g
m
eta
-
m
o
d
el,
s
er
v
in
g
as
th
e
f
o
u
n
d
atio
n
al
f
r
am
ewo
r
k
f
o
r
m
atch
in
g
h
eter
o
g
en
eo
u
s
s
y
s
tem
s
;
an
d
th
e
GAM
p
r
o
ce
s
s
,
o
u
tlin
in
g
th
e
s
eq
u
en
tial
s
tep
s
f
o
r
ap
p
ly
in
g
th
e
ap
p
r
o
ac
h
.
Ad
d
itio
n
ally
,
th
e
m
eth
o
d
o
lo
g
y
in
co
r
p
o
r
ates
m
ath
em
atica
l
f
o
r
m
alis
m
to
p
r
o
v
id
e
a
r
ig
o
r
o
u
s
th
eo
r
etica
l
b
asis
an
d
a
m
u
lti
-
ag
en
t
s
y
s
tem
to
en
s
u
r
e
s
ca
lab
ilit
y
an
d
d
y
n
am
ic
in
ter
ac
tio
n
am
o
n
g
co
m
p
o
n
en
ts
.
Fin
ally
,
a
ca
s
e
s
tu
d
y
is
in
clu
d
ed
to
d
em
o
n
s
tr
ate
th
e
p
r
ac
tical
ap
p
licatio
n
an
d
v
alid
ate
th
e
ef
f
ec
tiv
en
ess
o
f
th
e
p
r
o
p
o
s
ed
ap
p
r
o
ac
h
.
3
.
1
.
G
AM
a
rc
hite
ct
ure
First,
we
d
esig
n
ed
th
e
ar
ch
itectu
r
e
o
f
o
u
r
ap
p
r
o
ac
h
.
GAM
is
b
u
ilt
o
n
two
f
u
n
d
am
en
tal
s
tep
s
,
as
s
h
o
wn
in
Fig
u
r
e
1:
−
Me
ta
-
m
o
d
el
m
atc
h
in
g
:
i
n
t
h
is
s
tep
,
h
eter
o
g
en
eo
u
s
m
et
a
-
m
o
d
els
ar
e
au
to
m
atica
lly
lin
k
ed
.
So
u
r
ce
m
eta
-
m
o
d
els
(
SMM
1
.
.
.
SMM
i)
ar
e
m
atch
e
d
with
tar
g
et
m
eta
-
m
o
d
els
(
TMM
1
...
TMM
j
)
,
g
en
e
r
atin
g
a
m
atch
in
g
m
o
d
el
(
MG
)
th
at
i
d
e
n
tifie
s
th
e
co
r
r
esp
o
n
d
en
ce
s
b
e
twee
n
elem
en
ts
.
−
Mo
d
el
g
e
n
er
atio
n
:
b
ased
o
n
t
h
e
m
atch
i
n
g
estab
lis
h
ed
in
th
e
f
ir
s
t
s
tep
,
th
e
s
o
u
r
ce
m
o
d
els
(
SM
1
.
.
.
SM
i
)
ar
e
au
to
m
atica
lly
tr
an
s
f
o
r
m
ed
in
to
tar
g
et
m
o
d
els
(
T
M
1
...
TM
j)
,
co
n
f
o
r
m
in
g
to
th
e
co
r
r
esp
o
n
d
in
g
tar
g
et
m
eta
-
m
o
d
els.
Fig
u
r
e
1
.
C
o
m
p
r
eh
en
s
iv
e
s
tr
u
ctu
r
e
o
f
t
h
e
g
en
e
r
ativ
e
m
atch
i
n
g
ap
p
r
o
ac
h
3
.
2
.
G
ener
a
t
iv
e
m
a
t
ching
met
a
-
mo
del
(
M
M
G
)
T
o
im
p
lem
e
n
t
th
e
GAM
a
p
p
r
o
ac
h
e
f
f
ec
tiv
ely
,
we
d
esig
n
ed
a
g
en
er
ativ
e
m
atch
in
g
m
eta
-
m
o
d
el
t
h
at
id
en
tifie
s
k
ey
co
n
ce
p
ts
,
in
clu
d
in
g
elem
en
ts
,
r
elatio
n
s
h
ip
s
,
v
er
s
io
n
m
an
a
g
em
en
t,
a
n
d
m
a
tch
in
g
h
is
to
r
y
.
T
h
is
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
15
,
No
.
2
,
Ap
r
il
20
25
:
2
3
4
5
-
2
3
5
5
2348
m
eta
-
m
o
d
el
h
a
n
d
les
th
e
id
en
tific
atio
n
o
f
co
r
r
esp
o
n
d
en
ce
s
b
etwe
en
m
eta
-
m
o
d
el
e
lem
en
ts
,
en
s
u
r
in
g
co
n
s
is
ten
cy
an
d
tr
ac
ea
b
ilit
y
th
r
o
u
g
h
o
u
t
th
e
m
atc
h
in
g
p
r
o
ce
s
s
.
Ad
d
itio
n
ally
,
it
p
r
o
v
id
es
a
s
tr
u
ctu
r
ed
r
ep
r
esen
tatio
n
to
ad
d
r
ess
th
e
d
y
n
am
ic
n
atu
r
e
o
f
s
y
s
tem
ch
an
g
es,
allo
win
g
f
o
r
iter
ativ
e
u
p
d
ates
an
d
r
ef
in
em
en
ts
.
T
h
e
d
esig
n
i
n
co
r
p
o
r
ates
m
ec
h
a
n
is
m
s
f
o
r
c
o
n
f
lict
r
eso
lu
tio
n
an
d
s
u
p
p
o
r
ts
m
u
ltip
le
v
er
s
io
n
s
to
ac
co
m
m
o
d
ate
ev
o
l
v
in
g
r
eq
u
ir
em
en
ts
.
B
y
lev
er
a
g
in
g
th
is
m
eta
-
m
o
d
el,
th
e
GAM
a
p
p
r
o
ac
h
ac
h
iev
es
a
r
o
b
u
s
t
an
d
s
ca
lab
le
f
r
am
ewo
r
k
f
o
r
m
an
ag
in
g
c
o
m
p
lex
s
y
s
tem
h
eter
o
g
en
eities.
3
.
3
.
G
AM
pro
ce
s
s
T
h
e
GAM
p
r
o
ce
s
s
co
m
p
r
is
es
two
p
r
im
ar
y
p
h
ases
:
au
to
m
ati
c
m
atch
in
g
an
d
au
to
m
atic
g
en
er
atio
n
.
I
n
th
e
m
atch
in
g
p
h
ase,
two
m
eta
-
m
o
d
els
(
s
o
u
r
ce
an
d
tar
g
et)
ar
e
u
s
ed
to
au
to
m
a
tically
g
en
er
ate
a
co
r
r
esp
o
n
d
en
ce
m
o
d
el
(
MG
)
,
wh
ich
d
ef
in
es
th
e
r
elatio
n
s
h
ip
s
b
etwe
en
th
eir
elem
e
n
ts
.
I
n
th
e
g
en
er
atio
n
p
h
ase,
a
s
o
u
r
ce
m
o
d
el
co
n
f
o
r
m
in
g
to
th
e
s
o
u
r
ce
m
eta
-
m
o
d
el
is
au
to
m
atica
lly
tr
an
s
f
o
r
m
e
d
in
to
an
eq
u
iv
alen
t
tar
g
et
m
o
d
el,
u
tili
zin
g
th
e
id
e
n
tifie
d
co
r
r
esp
o
n
d
en
ce
s
.
T
h
e
d
etailed
p
r
o
ce
s
s
in
clu
d
es
th
ese
f
o
u
r
k
ey
s
tep
s
,
as
s
h
o
wn
in
Fig
u
r
e
2:
−
Selectin
g
th
e
s
o
u
r
ce
an
d
tar
g
et
m
eta
-
m
o
d
els.
−
R
ef
in
in
g
th
e
co
r
e
g
e
n
er
ativ
e
MM
G
b
y
ad
d
in
g
o
r
m
o
d
if
y
in
g
r
elatio
n
s
h
ip
s
an
d
s
to
r
in
g
r
ef
i
n
ed
v
er
s
io
n
s
in
a
clo
u
d
r
e
p
o
s
ito
r
y
f
o
r
ea
s
y
ac
ce
s
s
.
−
R
ef
in
in
g
th
e
MG
m
o
d
el
th
r
o
u
g
h
iter
ativ
e
o
r
m
an
u
al
ad
ju
s
tm
en
ts
u
s
in
g
co
g
n
itiv
e
ag
en
ts
o
r
ex
p
er
t in
p
u
t.
−
Gen
er
atin
g
th
e
tar
g
et
m
o
d
el,
with
th
e
p
o
s
s
ib
ilit
y
o
f
f
u
r
t
h
er
r
ef
i
n
em
en
t
t
h
r
o
u
g
h
ex
p
e
r
t
v
alid
atio
n
o
r
ad
d
itio
n
al
iter
atio
n
s
.
Fig
u
r
e
2
.
GAM
p
r
o
ce
s
s
d
escr
ip
tio
n
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
A
r
ch
itectu
r
e
o
f m
u
lti
-
a
g
en
t sy
s
tems fo
r
g
en
era
tive
a
u
to
ma
tic
ma
tch
in
g
…
(
Zo
u
h
a
ir
I
b
n
B
a
to
u
ta
)
2349
3
.
4
.
M
a
t
hema
t
ica
l
f
o
rma
lis
m
W
e
d
ev
elo
p
ed
a
m
ath
em
atica
l
f
o
r
m
alis
m
f
o
r
th
e
GAM
MA
S
ap
p
r
o
ac
h
,
g
r
o
u
n
d
ed
in
s
et
th
eo
r
y
,
wh
er
e
ea
ch
m
eta
-
m
o
d
el
(
MM
a)
is
r
ep
r
esen
ted
as
a
s
et
o
f
tr
ip
lets
.
T
h
ese
tr
ip
lets
co
m
p
r
is
e
elem
en
ts
f
r
o
m
th
e
r
ef
in
ed
g
en
er
ativ
e
m
atch
in
g
m
eta
-
m
o
d
el
(
MM
G)
an
d
th
e
r
elatio
n
s
h
ip
s
b
etwe
en
th
em
.
Fo
r
two
m
eta
-
m
o
d
els,
MM
a
(
s
o
u
r
ce
)
an
d
MM
b
(
tar
g
et)
,
th
e
m
atch
in
g
m
o
d
el
(
MG
)
ca
p
tu
r
es
th
e
co
r
r
esp
o
n
d
en
ce
s
b
etwe
en
th
eir
elem
en
ts
,
also
r
ep
r
esen
ted
as tr
ip
lets
.
T
h
e
tr
an
s
f
o
r
m
atio
n
p
r
o
ce
s
s
b
etwe
en
s
o
u
r
ce
an
d
tar
g
et
m
o
d
els lev
er
ag
es
th
ese
co
r
r
esp
o
n
d
en
ce
s
to
g
en
er
ate
eq
u
iv
alen
t m
o
d
els,
en
s
u
r
in
g
th
ey
co
n
f
o
r
m
to
th
eir
r
esp
ec
tiv
e
m
eta
-
m
o
d
els.
T
h
is
s
et
-
b
ased
f
o
r
m
alis
m
p
r
o
v
id
es
a
s
tr
u
ctu
r
ed
an
d
r
ig
o
r
o
u
s
r
ep
r
esen
tatio
n
o
f
m
o
d
els,
co
r
r
esp
o
n
d
en
ce
s
,
an
d
tr
an
s
f
o
r
m
atio
n
s
.
3
.
5
.
M
ulti
-
a
g
ent
s
y
s
t
e
m
T
h
e
co
n
ce
p
t
o
f
m
u
lti
-
ag
e
n
t
s
y
s
tem
(
MA
S)
s
tem
s
f
r
o
m
d
is
tr
i
b
u
ted
a
r
tific
ial
in
tellig
en
ce
(
D
AI
)
.
T
h
is
ap
p
r
o
ac
h
f
ac
ilit
ates
th
e
u
n
d
er
s
tan
d
in
g
,
m
o
d
elin
g
,
an
d
s
im
u
latio
n
o
f
co
m
p
lex
s
y
s
tem
s
co
m
p
o
s
ed
o
f
m
u
ltip
le
ag
en
ts
th
at
ex
h
ib
it
in
tellig
en
t
b
eh
av
io
r
an
d
in
te
r
ac
t
with
b
o
th
ea
ch
o
th
er
an
d
th
eir
e
x
ter
n
al
en
v
ir
o
n
m
en
t.
MA
S
is
p
ar
ticu
lar
ly
s
u
ited
f
o
r
s
o
lv
in
g
p
r
o
b
lem
s
in
a
d
is
tr
ib
u
ted
m
an
n
er
[
2
2
]
–
[
2
8
]
.
E
ac
h
ag
en
t
o
p
er
ates
lo
ca
lly
with
co
o
p
er
ati
v
e
b
eh
a
v
io
r
s
,
an
d
th
r
o
u
g
h
co
llectiv
e
s
elf
-
o
r
g
an
izatio
n
,
a
g
lo
b
al
s
o
lu
tio
n
em
er
g
es
f
r
o
m
th
e
in
d
iv
id
u
al
p
r
o
b
lem
-
s
o
lv
in
g
ef
f
o
r
ts
o
f
th
e
a
g
en
ts
.
3
.
6
.
Ca
s
e
s
t
ud
y
T
o
ev
alu
ate
o
u
r
a
p
p
r
o
ac
h
,
w
e
co
n
d
u
cted
th
e
R
B
DU
ca
s
e
s
tu
d
y
,
th
is
ca
s
e
s
tu
d
y
in
v
o
lv
ed
a
m
o
r
e
co
m
p
lex
h
eter
o
g
e
n
eo
u
s
d
ata
b
ase
s
y
s
tem
in
co
r
p
o
r
atin
g
th
e
UM
L
m
eta
-
m
o
d
el.
Fo
r
th
is
,
we
d
ev
elo
p
e
d
f
iv
e
m
eta
-
m
o
d
els
r
ep
r
esen
tin
g
v
ar
io
u
s
d
atab
ase
ty
p
es,
in
clu
d
in
g
r
elatio
n
al
d
atab
ases
an
d
th
r
ee
b
ig
d
ata
No
SQL
ty
p
es
(
k
e
y
-
v
alu
e
s
to
r
e,
d
o
c
u
m
en
t
s
to
r
e,
an
d
co
lu
m
n
ar
s
to
r
e
)
,
in
ad
d
itio
n
t
o
th
e
UM
L
m
eta
-
m
o
d
el.
T
h
ese
m
eta
-
m
o
d
els
illu
s
tr
ate
th
e
e
f
f
ec
tiv
en
ess
o
f
th
e
GAM
SMA
ap
p
r
o
ac
h
in
f
ac
ilit
atin
g
m
o
d
el
tr
an
s
f
o
r
m
atio
n
ac
r
o
s
s
d
iv
er
s
e
d
atab
ase
s
y
s
tem
s
.
4.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
4
.
1
.
G
ener
a
t
iv
e
m
a
t
ching
met
a
-
mo
del
T
h
e
g
en
er
ativ
e
m
atch
i
n
g
m
et
a
-
m
o
d
el
(
MM
G)
we
d
esig
n
e
d
ad
d
r
ess
es
th
e
m
an
ag
em
en
t
o
f
lex
ical,
s
tr
u
ctu
r
al,
an
d
s
em
an
tic
s
im
ilar
ities
to
ef
f
ec
tiv
ely
m
atch
h
eter
o
g
en
eo
u
s
m
eta
-
m
o
d
els.
I
t
s
k
ey
co
m
p
o
n
e
n
ts
en
ab
le
th
e
alig
n
m
e
n
t
an
d
tr
a
n
s
f
o
r
m
atio
n
o
f
elem
en
ts
ac
r
o
s
s
m
eta
-
m
o
d
els
b
y
em
p
l
o
y
in
g
v
ar
i
o
u
s
s
im
ilar
ity
m
ea
s
u
r
es.
T
h
is
s
tr
u
ctu
r
e
s
ig
n
if
ican
tly
im
p
r
o
v
es
th
e
o
v
er
all
ef
f
icien
cy
o
f
th
e
GAM
SMA
ap
p
r
o
ac
h
i
n
au
to
m
atin
g
m
o
d
el
g
en
e
r
atio
n
.
T
h
e
c
o
r
e
o
f
th
e
MM
G
c
o
n
s
is
ts
o
f
s
ev
er
al
ess
en
tial
co
m
p
o
n
en
ts
,
as
illu
s
tr
ated
in
Fig
u
r
e
3
.
T
h
e
ef
f
ec
tiv
en
ess
o
f
o
u
r
g
en
e
r
ativ
e
m
atch
in
g
m
eta
-
m
o
d
el
l
ies
in
its
ex
ten
s
ib
ilit
y
,
wh
ich
allo
ws
f
o
r
f
lex
ib
le
ad
ap
tatio
n
to
v
ar
io
u
s
d
o
m
ain
s
an
d
ar
c
h
itectu
r
es.
At
its
co
r
e,
MM
G
co
n
s
is
ts
o
f
k
e
y
co
m
p
o
n
e
n
ts
th
at
m
an
ag
e
c
o
r
r
esp
o
n
d
en
ce
s
b
et
wee
n
h
eter
o
g
en
eo
u
s
m
eta
-
m
o
d
els,
en
s
u
r
in
g
th
at
all
ess
en
tial
elem
en
ts
ar
e
ad
d
r
ess
ed
d
u
r
i
n
g
th
e
m
atch
i
n
g
p
r
o
ce
s
s
.
T
h
e
E
leme
n
t
co
m
p
o
n
en
t
g
e
n
er
alize
s
o
th
er
elem
e
n
ts
with
attr
ib
u
tes
s
u
ch
as
n
am
e,
I
D,
an
d
d
escr
i
p
tio
n
,
wh
ile
th
e
Ma
tch
in
g
c
o
m
p
o
n
e
n
t
ef
f
icien
tly
m
an
a
g
es
r
elatio
n
s
b
etwe
en
s
o
u
r
ce
an
d
tar
g
et
m
eta
-
m
o
d
el
s
,
in
co
r
p
o
r
atin
g
v
er
s
io
n
co
n
t
r
o
l
an
d
r
ef
in
em
e
n
t.
S
o
u
r
ce
an
d
Ta
r
g
et
d
ef
in
e
th
e
r
esp
ec
tiv
e
m
eta
-
m
o
d
els,
an
d
t
h
e
A
g
e
n
tMeta
mo
d
elHa
n
d
ler
a
n
d
A
g
en
tEle
men
tHa
n
d
ler
f
ac
il
itate
th
e
n
a
v
ig
atio
n
an
d
m
a
n
ip
u
latio
n
o
f
m
eta
-
ele
m
en
ts
.
Ad
d
itio
n
ally
,
th
e
A
g
en
t
Tr
a
n
s
fo
r
mer
en
s
u
r
es
th
e
p
r
o
p
er
tr
an
s
f
o
r
m
atio
n
o
f
s
o
u
r
ce
elem
en
ts
in
to
tar
g
et
elem
en
ts
,
wh
ile
Lin
kA
lig
n
men
t
d
ef
in
es
r
elatio
n
s
h
ip
s
li
k
e
ag
g
r
eg
atio
n
an
d
s
im
ilar
ity
,
wh
ich
ar
e
es
s
en
tial
f
o
r
ef
f
ec
tiv
ely
alig
n
in
g
m
eta
-
e
lem
en
ts
.
T
h
e
Similar
ity
co
m
p
o
n
en
t
is
cr
u
cial,
as
it
ca
p
tu
r
es
v
ar
io
u
s
ty
p
es
o
f
co
r
r
esp
o
n
d
en
ce
s
—
lex
ical,
s
tr
u
ctu
r
al,
s
em
an
tic,
an
d
f
u
n
cti
o
n
al
—
f
o
r
m
in
g
th
e
f
o
u
n
d
atio
n
o
f
th
e
GAM
p
r
o
ce
s
s
an
d
en
ab
lin
g
p
r
ec
is
e
au
to
m
atic
m
o
d
el
g
en
e
r
atio
n
ac
r
o
s
s
d
iv
er
s
e
s
y
s
tem
s
.
4
.
2
.
M
ulti
-
a
g
ent
s
y
s
t
e
m
B
y
ass
ig
n
in
g
s
p
ec
ialized
r
o
l
es
to
d
if
f
e
r
en
t
a
g
en
ts
,
th
e
s
y
s
tem
ef
f
icien
tly
h
an
d
les
v
ar
io
u
s
task
s
.
Fig
u
r
e
4
illu
s
tr
ates
th
e
C
o
n
tr
ac
t
Net
p
r
o
to
co
l
,
wh
ich
o
u
tli
n
es
th
e
ag
en
t
s
o
cieties
an
d
th
eir
co
m
m
u
n
icatio
n
.
T
h
e
n
etwo
r
k
c
o
m
p
r
is
es
s
p
ec
ialized
ag
en
ts
,
ea
ch
r
esp
o
n
s
ib
le
f
o
r
d
is
tin
ct
f
u
n
ctio
n
s
with
i
n
th
e
GAM
MA
S
ap
p
r
o
ac
h
.
T
h
e
C
o
o
r
d
in
a
to
r
A
g
en
t
o
v
er
s
ee
s
co
o
r
d
in
atio
n
am
o
n
g
ag
e
n
ts
,
wh
ile
th
e
Gen
era
to
r
A
g
en
t
h
an
d
les
m
o
d
el
g
en
e
r
atio
n
b
ased
o
n
m
a
tch
in
g
r
esu
lts
.
T
h
e
R
efin
in
g
A
g
en
t
r
ef
in
es th
e
co
r
r
esp
o
n
d
en
ce
s
id
en
tifie
d
d
u
r
in
g
th
e
p
r
o
ce
s
s
,
an
d
th
e
Tr
a
n
s
fo
r
merAg
en
t
,
alo
n
g
with
th
e
MFTr
a
n
s
fo
r
me
r
A
g
en
t
an
d
F
MT
r
a
n
s
fo
r
me
r
A
g
en
t
,
co
n
v
er
ts
m
eta
-
m
o
d
els
in
to
m
ath
em
atica
l
f
o
r
m
alis
m
f
o
r
ad
ap
tab
ilit
y
.
Ag
e
n
ts
s
u
ch
as
th
e
Ma
tch
in
g
A
g
en
t
,
Mea
n
in
g
S
imila
r
ityA
g
en
t
,
Tr
a
n
s
la
tio
n
A
g
en
t
,
an
d
S
tr
u
ctu
r
ed
S
imila
r
ityA
g
en
t
ca
lcu
late
v
ar
io
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ile
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m
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atic
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er
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ess
o
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AM
MA
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o
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u
r
e
3
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h
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co
r
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e
GAM
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r
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u
r
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4
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o
n
tr
ac
t
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et
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l
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tic
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h
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ir
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n
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a
to
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ta
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2351
4
.
3
.
Ca
s
e
s
t
ud
y
a
nd
ma
t
ching
re
s
ults
I
n
th
is
s
ec
tio
n
,
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p
r
esen
t
t
h
e
m
eta
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m
o
d
els
d
ev
el
o
p
ed
f
o
r
th
e
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s
e
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tu
d
y
in
c
o
r
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o
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at
ed
in
o
u
r
r
esear
ch
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alo
n
g
with
th
e
co
r
r
esp
o
n
d
in
g
m
atch
i
n
g
r
esu
lts
.
T
h
e
p
u
r
p
o
s
e
o
f
th
ese
ca
s
e
s
tu
d
y
is
to
test
o
u
r
ap
p
r
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h
o
n
d
if
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er
en
t
s
y
s
tem
s
an
d
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ate
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atch
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d
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o
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g
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e
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atio
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etwe
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h
e
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eta
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o
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els
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o
r
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h
e
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s
e
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tu
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y
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e
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o
wn
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Fig
u
r
e
5
.
T
h
r
o
u
g
h
th
is
ca
s
e
s
tu
d
y
,
we
aim
to
ass
ess
th
e
ef
f
icien
cy
o
f
th
e
GAM
MA
S a
p
p
r
o
ac
h
in
m
an
ag
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g
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iv
e
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s
e
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y
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tem
s
.
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u
r
e
5
.
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B
DU
ca
s
e
s
tu
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ies
T
h
e
r
esu
lts
o
f
ap
p
ly
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g
th
e
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ap
p
r
o
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e
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DU
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s
e
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tu
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e
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u
m
m
ar
ized
as
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o
llo
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.
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e
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r
esen
t
th
e
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atch
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g
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esu
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o
b
tain
ed
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r
o
m
ap
p
ly
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r
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o
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h
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e
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DU
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e
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y
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Fig
u
r
e
6
illu
s
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ates
th
e
m
atch
es
g
en
er
ated
b
etwe
en
th
e
SQL
an
d
k
ey
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alu
e
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e
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eta
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o
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ile
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ab
le
2
p
r
o
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id
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u
m
m
ar
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th
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atic
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atch
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g
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o
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ce
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o
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eta
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el.
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s
e
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em
o
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ated
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e
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en
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o
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e
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m
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eo
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s
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ase
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y
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e
m
eta
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o
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els,
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ep
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tin
g
r
elatio
n
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d
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ig
d
ata
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atab
ases
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e
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e,
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m
en
t
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e,
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d
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lu
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ar
s
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e)
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e
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atch
ed
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d
tr
an
s
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o
r
m
ed
with
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ig
h
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SS
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8
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8
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l.
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2
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r
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20
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atch
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ain
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atch
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r
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n
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alu
es
ap
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r
o
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h
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g
1
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n
th
e
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o
llo
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g
s
ec
tio
n
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will
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r
o
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e
a
d
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aly
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is
o
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th
e
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alu
atio
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e
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d
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g
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s
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esis
o
f
th
e
f
in
al
q
u
ality
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etr
ic
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lcu
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n
s
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o
r
th
e
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DU
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s
e
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tu
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y
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u
r
e
6
.
Ma
tch
i
n
g
b
etwe
en
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QL
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d
k
e
y
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v
al
u
e
s
to
r
e
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th
r
esh
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ld
:
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5
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ab
le
2
.
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m
m
a
r
y
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th
e
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atc
h
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g
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etwe
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d
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eta
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me
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ed
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etr
ics
co
m
m
o
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ly
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n
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ac
h
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n
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g
a
n
d
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r
tific
ial
in
tellig
en
ce
,
in
clu
d
in
g
r
ec
all,
o
v
er
al
l
ac
cu
r
ac
y
,
F
-
m
ea
s
u
r
e,
an
d
p
r
ec
is
io
n
[
2
9
]
–
[
3
3
]
.
T
ab
le
3
p
r
esen
ts
th
e
r
esu
lts
o
f
th
ese
m
etr
ics
af
ter
g
en
er
atin
g
co
r
r
esp
o
n
d
en
ce
s
u
s
in
g
th
e
G
AM
MA
S
ap
p
r
o
ac
h
in
th
e
R
B
DU
ca
s
e
s
tu
d
y
.
T
h
e
q
u
ality
m
etr
ics
ca
lcu
lated
f
o
r
b
o
th
ca
s
e
s
tu
d
ies
d
em
o
n
s
tr
ate
th
e
o
v
er
all
ef
f
ec
tiv
en
ess
o
f
th
e
GAM
MA
S
ap
p
r
o
ac
h
.
T
h
e
R
B
DU
ca
s
e
s
tu
d
y
ex
h
ib
ited
s
tr
o
n
g
p
er
f
o
r
m
a
n
ce
ac
r
o
s
s
m
u
ltip
le
b
ig
d
at
a
No
SQL
m
eta
-
m
o
d
els,
with
p
r
ec
is
io
n
co
n
s
is
ten
tly
r
ea
ch
in
g
1
.
T
h
ese
h
ig
h
-
q
u
ality
m
etr
ics
co
n
f
ir
m
th
at
th
e
GAM
SMA
ap
p
r
o
ac
h
is
ca
p
ab
le
o
f
h
an
d
li
n
g
b
o
t
h
s
im
p
le
an
d
co
m
p
lex
s
y
s
tem
s
,
d
eliv
er
in
g
r
eliab
le
an
d
ac
c
u
r
ate
r
esu
lts
.
T
ab
le
3
.
Qu
ality
m
ea
s
u
r
em
en
t
r
esu
lts
f
o
r
th
e
b
i
g
d
ata
R
B
DU
s
ec
tio
n
M
e
a
su
r
e
s
M
e
t
a
-
m
o
d
e
l
c
o
u
p
l
e
s
H
e
u
r
i
s
t
i
c
Reca
l
l
Prec
i
s
i
o
n
F
-
Mea
s
u
re
O
v
era
l
l
(
S
Q
L,
K
e
y
-
V
a
l
u
e
)
N
a
me
M
a
t
c
h
i
n
g
0
.
6
1
0
.
7
5
0
.
6
N
e
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g
h
b
o
u
r
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t
r
u
c
t
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r
a
l
0
.
5
1
0
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6
6
6
6
6
6
6
6
7
0
.
5
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l
o
o
d
i
n
g
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t
r
u
c
t
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r
a
l
1
1
1
1
(
S
Q
L,
D
o
c
u
m
e
n
t
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t
o
r
e
)
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a
me
M
a
t
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h
i
n
g
0
.
8
1
0
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8
8
8
8
8
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8
9
0
.
8
N
e
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g
h
b
o
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r
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t
r
u
c
t
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r
a
l
0
.
6
1
0
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7
5
0
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6
F
l
o
o
d
i
n
g
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t
r
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c
t
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r
a
l
0
.
8
1
0
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8
8
8
8
8
8
8
9
0
.
8
(
S
Q
L,
C
o
l
u
m
n
a
r
)
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a
me
M
a
t
c
h
i
n
g
0
.
8
7
5
1
0
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3
3
3
3
3
3
0
.
8
7
5
N
e
i
g
h
b
o
u
r
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t
r
u
c
t
u
r
a
l
1
0
.
8
0
.
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4
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
A
r
ch
itectu
r
e
o
f m
u
lti
-
a
g
en
t sy
s
tems fo
r
g
en
era
tive
a
u
to
ma
tic
ma
tch
in
g
…
(
Zo
u
h
a
ir
I
b
n
B
a
to
u
ta
)
2353
T
h
e
m
etr
ic
r
esu
lts
o
b
tain
ed
af
ter
g
en
er
atin
g
m
atch
es
u
s
in
g
th
e
GAM
SMA
ap
p
r
o
ac
h
f
o
r
t
h
e
b
ig
d
at
a
m
eta
-
m
o
d
els
in
t
h
e
R
B
DU
ca
s
e
s
tu
d
y
.
W
e
o
p
ted
to
s
ep
ar
at
e
th
e
ev
alu
atio
n
r
esu
lts
f
o
r
b
i
g
d
ata
m
eta
-
m
o
d
els
f
r
o
m
t
h
o
s
e
o
f
th
e
SQL/UM
L
p
air
,
s
h
o
wn
in
T
ab
le
4
,
to
an
a
ly
ze
th
e
im
p
ac
t
o
f
m
eta
-
m
o
d
e
l
d
o
m
ain
s
im
ilar
ity
o
n
th
e
g
en
er
atio
n
r
esu
lts
.
T
h
e
f
in
al
m
etr
ics
f
o
r
th
e
R
B
DU
c
ase
s
tu
d
y
ar
e
s
u
m
m
ar
ized
in
T
ab
le
5
,
wh
er
e
th
e
weig
h
ted
s
u
m
m
eth
o
d
was a
p
p
lied
,
ass
ig
n
in
g
a
weig
h
t o
f
1
to
ea
ch
p
air
t
o
ca
lcu
late
th
e
o
v
er
all
r
esu
lts
.
T
ab
le
5
d
is
p
lay
th
e
f
in
al
m
etr
i
c
r
esu
lts
,
r
ef
lectin
g
th
e
av
er
ag
e
v
alu
es c
alcu
lated
b
y
th
e
co
r
r
esp
o
n
d
in
g
f
u
n
ctio
n
s
.
I
t
is
n
o
tewo
r
th
y
th
at
all
m
etr
ics
ar
e
clo
s
e
to
1
,
u
n
d
er
s
co
r
in
g
th
e
h
ig
h
q
u
ality
a
n
d
ac
cu
r
ac
y
o
f
th
e
r
esu
lts
o
b
tain
ed
.
Ou
r
ev
alu
a
tio
n
d
em
o
n
s
tr
ates
th
at
t
h
e
GAM
SMA
ap
p
r
o
ac
h
s
ig
n
if
ican
tly
o
u
t
p
er
f
o
r
m
s
ex
is
tin
g
m
eth
o
d
s
s
u
ch
as
SIB,
SIG
,
SIM
,
an
d
C
L
S,
as
h
ig
h
l
ig
h
ted
in
th
e
SW
OT
an
aly
s
is
in
T
ab
le
1
.
Un
lik
e
th
ese
m
eth
o
d
s
,
wh
ich
eith
er
lack
au
to
m
atic
m
o
d
el
g
en
e
r
atio
n
o
r
r
ely
o
n
f
ix
ed
h
eu
r
is
tics
,
GAM
MA
S
in
teg
r
ates b
o
th
m
atch
in
g
an
d
g
en
er
atio
n
p
r
o
ce
s
s
es,
m
ak
in
g
it h
ig
h
ly
ad
ap
ta
b
le
to
a
wid
e
r
an
g
e
o
f
s
y
s
tem
s
an
d
ar
ch
itectu
r
es.
T
h
e
u
s
e
o
f
a
MA
S
en
h
an
ce
s
s
ca
lab
ilit
y
an
d
f
lex
ib
ilit
y
,
allo
win
g
f
o
r
ef
f
icie
n
t
m
an
ag
em
en
t
o
f
d
iv
er
s
e
m
eta
-
m
o
d
els.
T
h
e
ca
s
e
s
tu
d
y
co
n
f
ir
m
e
d
GAM
MA
S's
ef
f
ec
tiv
en
ess
in
ad
d
r
ess
in
g
th
e
c
h
allen
g
es
o
f
au
to
m
atic
m
atch
in
g
a
n
d
m
o
d
el
g
en
e
r
atio
n
b
etwe
en
h
e
ter
o
g
en
eo
u
s
m
eta
-
m
o
d
els.
All
q
u
ality
m
etr
ics
ap
p
r
o
ac
h
ed
v
alu
es
clo
s
e
to
1
,
h
ig
h
lig
h
tin
g
th
e
h
i
g
h
p
r
ec
i
s
io
n
an
d
r
eliab
ilit
y
o
f
th
e
co
r
r
esp
o
n
d
e
n
ce
s
an
d
tr
an
s
f
o
r
m
atio
n
s
ac
h
ie
v
ed
.
T
ab
le
4
.
Qu
ality
m
ea
s
u
r
em
en
t
r
esu
lts
f
o
r
SQL/UM
L
p
air
M
e
a
su
r
e
s
M
e
t
a
-
m
o
d
e
l
c
o
u
p
l
e
s
H
e
u
r
i
s
t
i
c
R
e
c
a
l
l
P
r
e
c
i
s
i
o
n
F
-
mea
su
r
e
O
v
e
r
a
l
l
(
S
Q
L,
U
M
L)
M
a
x
i
m
u
m
S
i
m
i
l
a
r
i
t
y
0
.
6
6
6
6
6
6
6
6
7
1
0
.
8
0
.
6
6
6
6
6
6
6
6
7
T
ab
le
5
.
Fin
al
q
u
ality
m
etr
ics
GAM
SMA
M
e
a
su
r
e
s
C
a
se
st
u
d
y
F
u
n
c
t
i
o
n
R
e
c
a
l
l
P
r
e
c
i
s
i
o
n
F
-
M
e
a
s
u
r
e
O
v
e
r
a
l
l
R
B
D
U
F
i
n
a
l
S
i
mi
l
a
r
i
t
y
0
.
8
0
9
5
2
3
8
1
1
7
5
1
0
.
8
9
2
3
0
7
6
9
0
.
8
0
9
5
2
3
8
1
1
7
5
4
.
5
.
L
im
it
a
t
io
ns
T
h
e
GAM
MA
S
ap
p
r
o
ac
h
p
r
o
v
id
es
a
r
o
b
u
s
t
s
o
lu
tio
n
b
y
i
n
te
g
r
atin
g
au
t
o
m
atic
m
eta
-
m
o
d
el
m
atch
in
g
with
m
o
d
el
g
en
er
atio
n
,
ef
f
ec
t
iv
ely
ad
d
r
ess
in
g
th
e
co
m
p
lex
i
ties
o
f
h
eter
o
g
en
eo
u
s
s
y
s
tem
s
an
d
tec
h
n
o
lo
g
ies.
T
h
is
s
ig
n
if
ican
tly
im
p
r
o
v
es
p
r
ec
is
io
n
an
d
r
ec
all
m
etr
ics,
with
s
u
cc
ess
f
u
l
im
p
lem
en
tatio
n
d
em
o
n
s
tr
atin
g
im
p
o
r
tan
t
im
p
licatio
n
s
f
o
r
m
a
n
ag
in
g
h
eter
o
g
en
eity
ac
r
o
s
s
d
if
f
er
en
t d
ev
elo
p
m
en
t
s
y
s
tem
s
an
d
ar
c
h
itectu
r
es.
I
t
en
h
an
ce
s
th
e
ef
f
icien
cy
an
d
a
cc
u
r
ac
y
o
f
cr
ea
tin
g
in
ter
o
p
er
a
b
le
s
y
s
tem
s
,
esp
ec
ially
in
co
m
p
lex
e
n
v
ir
o
n
m
en
ts
with
d
iv
er
s
e
s
y
s
tem
s
an
d
m
eta
-
m
o
d
els.
T
h
e
h
ig
h
q
u
ality
o
f
r
esu
lts
,
r
ef
lecte
d
in
m
etr
ics
n
ea
r
in
g
1
,
h
ig
h
lig
h
ts
th
e
r
eliab
ilit
y
an
d
ef
f
ec
tiv
en
e
s
s
o
f
th
e
ap
p
r
o
ac
h
in
p
r
o
d
u
cin
g
ac
cu
r
ate
co
r
r
esp
o
n
d
en
ce
s
.
Ho
wev
er
,
s
ev
er
al
ar
ea
s
f
o
r
f
u
tu
r
e
r
esear
ch
r
em
ain
.
First,
im
p
r
o
v
in
g
th
e
au
to
m
atic
m
atch
in
g
h
eu
r
is
tics
is
a
k
ey
p
r
io
r
ity
.
Fo
r
in
s
tan
ce
,
weig
h
t
an
d
t
h
r
esh
o
l
d
ca
lcu
latio
n
s
co
u
ld
b
e
r
e
f
in
e
d
th
r
o
u
g
h
a
d
v
an
ce
d
tech
n
i
q
u
e
s
,
s
u
ch
as
th
e
R
o
ck
m
eth
o
d
f
o
r
weig
h
t
d
eter
m
in
a
tio
n
o
r
f
u
zz
y
lo
g
ic.
I
n
teg
r
atin
g
n
ew
h
eu
r
is
tics
an
d
test
in
g
t
h
em
co
u
ld
f
u
r
th
er
o
p
tim
ize
th
e
m
atch
in
g
ac
cu
r
a
cy
.
An
o
th
e
r
cr
itical
d
ir
ec
tio
n
is
ap
p
ly
in
g
th
e
g
en
e
r
ativ
e
a
u
to
m
atic
m
atch
in
g
ap
p
r
o
ac
h
to
a
d
d
r
ess
d
ata
la
y
er
in
ter
o
p
er
ab
ilit
y
c
h
allen
g
es
[
3
4
]
,
p
a
r
ticu
lar
ly
b
etwe
en
b
i
g
d
ata
No
SQL
a
n
d
r
elatio
n
al
d
atab
ases
.
T
h
e
v
e
r
s
atility
an
d
a
d
ap
tab
ilit
y
o
f
G
AM
MA
S
o
f
f
er
n
u
m
e
r
o
u
s
r
e
s
ea
r
ch
o
p
p
o
r
tu
n
ities
ac
r
o
s
s
a
wid
e
r
an
g
e
o
f
d
o
m
ai
n
s
.
Fu
tu
r
e
wo
r
k
c
o
u
ld
ex
te
n
d
its
ap
p
licatio
n
to
ar
ea
s
s
u
ch
as
I
T
g
o
v
er
n
a
n
ce
,
e
-
lear
n
in
g
,
e
-
h
ea
lth
ca
r
e,
I
o
T
,
s
ea
r
ch
en
g
in
es,
an
d
th
e
Sem
an
t
ic
W
eb
.
T
h
ese
ex
p
an
s
io
n
s
wo
u
ld
n
o
t o
n
ly
en
r
ich
th
e
k
n
o
wled
g
e
b
ase
b
u
t
also
en
h
an
ce
th
e
ca
p
ab
ilit
ies
o
f
th
e
ag
en
ts
,
b
r
o
a
d
en
in
g
th
e
s
co
p
e
o
f
th
e
r
esear
ch
.
Ad
d
itio
n
ally
,
we
p
lan
to
d
ev
el
o
p
a
c
o
m
p
r
e
h
en
s
iv
e
GUI
a
p
p
l
icatio
n
o
n
th
e
.
NE
T
p
latf
o
r
m
t
o
f
ac
ilit
ate
b
r
o
a
d
er
ad
o
p
tio
n
a
n
d
p
r
a
ctica
l
u
s
e
o
f
GAM
SMA,
en
s
u
r
in
g
it
r
em
ain
s
a
f
lex
ib
le
an
d
ef
f
ec
tiv
e
to
o
l
f
o
r
tack
lin
g
f
u
tu
r
e
ch
allen
g
es in
au
to
m
atic
m
atch
in
g
an
d
s
y
s
tem
in
ter
o
p
er
a
b
ilit
y
.
5.
CO
NCLU
SI
O
N
T
h
is
p
ap
er
p
r
esen
ted
th
e
im
p
lem
en
tatio
n
o
f
th
e
GAM
a
p
p
r
o
ac
h
u
s
in
g
a
MA
S
ar
c
h
itectu
r
e.
GAM
MA
S
r
ep
r
esen
ts
a
n
o
v
el
p
ar
a
d
ig
m
th
at
co
m
b
in
es
au
to
m
atic
m
atch
in
g
an
d
m
o
d
el
g
en
er
a
tio
n
to
ad
d
r
ess
th
e
h
eter
o
g
en
eity
o
f
m
eta
-
m
o
d
els.
Ou
r
ap
p
r
o
ac
h
f
ac
ilit
ates
th
e
g
en
er
atio
n
o
f
m
o
d
els
th
r
o
u
g
h
a
u
to
m
atic
m
atch
in
g
b
etwe
en
v
ar
io
u
s
h
eter
o
g
e
n
eo
u
s
s
y
s
tem
s
.
T
h
e
ev
alu
atio
n
u
s
in
g
q
u
ality
m
etr
ics
an
d
ca
s
e
s
tu
d
ies
d
em
o
n
s
tr
ated
th
e
v
alid
it
y
an
d
ef
f
ec
tiv
en
ess
o
f
o
u
r
ap
p
r
o
ac
h
.
Sp
ec
if
ically
,
th
e
R
B
D
U
ca
s
e
s
tu
d
y
h
ig
h
lig
h
t
ed
th
e
r
o
b
u
s
tn
ess
an
d
ad
ap
tab
ilit
y
o
f
GAM
SMA.
T
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f
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p
r
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v
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a
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atch
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Evaluation Warning : The document was created with Spire.PDF for Python.
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Vo
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15
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2354
g
en
er
atio
n
,
c
o
n
tr
ib
u
tin
g
to
th
e
f
ield
o
f
m
o
d
el
-
d
r
iv
en
en
g
in
ee
r
in
g
.
C
o
m
p
ar
e
d
to
ex
is
tin
g
m
eth
o
d
s
,
GAM
SMA
o
f
f
er
s
s
ev
er
al
ad
v
an
tag
es:
T
h
e
G
AM
a
p
p
r
o
a
c
h
i
n
t
r
o
d
u
ce
s
s
ig
n
i
f
i
ca
n
t
i
n
n
o
v
a
ti
o
n
s
c
o
m
p
ar
ed
t
o
e
x
is
ti
n
g
m
et
h
o
d
s
.
U
n
l
ik
e
tr
a
d
i
ti
o
n
al
ap
p
r
o
ac
h
es
t
h
at
o
f
te
n
r
e
ly
o
n
m
a
n
u
al
m
a
tc
h
i
n
g
a
n
d
la
ck
m
ec
h
a
n
is
m
s
f
o
r
a
u
t
o
m
ati
c
m
o
d
el
g
e
n
e
r
a
ti
o
n
,
GAM
s
ea
m
l
ess
l
y
i
n
t
e
g
r
ates
b
o
t
h
p
r
o
ce
s
s
es,
en
s
u
r
i
n
g
g
r
ea
t
e
r
e
f
f
ic
ie
n
c
y
an
d
c
o
n
s
is
te
n
cy
.
Fu
r
th
e
r
m
o
r
e
,
GAM
d
e
m
o
n
s
tr
ates
e
x
ce
p
ti
o
n
al
a
d
a
p
ta
b
i
lit
y
b
y
h
an
d
l
in
g
a
w
id
e
r
a
n
g
e
o
f
m
et
a
-
m
o
d
els
a
n
d
tec
h
n
o
lo
g
i
es,
ef
f
ec
t
iv
el
y
a
d
d
r
e
s
s
in
g
t
h
e
l
im
ita
ti
o
n
s
o
f
m
et
h
o
d
s
s
u
c
h
as
s
t
ati
c
i
d
e
n
ti
f
i
er
-
b
as
ed
t
ec
h
n
i
q
u
e
(
SIB
)
,
SIG
,
SI
M,
a
n
d
C
S
L
t
ec
h
n
i
q
u
e
,
as
h
i
g
h
li
g
h
t
ed
in
t
h
e
S
W
OT
a
n
al
y
s
is
.
I
n
a
d
d
iti
o
n
,
GAM
e
n
h
a
n
c
es
k
e
y
p
e
r
f
o
r
m
a
n
c
e
m
e
tr
ics
,
o
f
f
e
r
i
n
g
i
m
p
r
o
v
e
d
p
r
ec
is
io
n
a
n
d
r
ec
a
ll,
w
h
i
ch
u
n
d
e
r
s
c
o
r
es
its
p
o
t
en
t
ial
to
t
r
an
s
f
o
r
m
s
o
f
twa
r
e
d
e
v
e
lo
p
m
e
n
t
p
r
ac
ti
ce
s
b
y
d
eli
v
er
i
n
g
m
o
r
e
ac
c
u
r
ate
an
d
r
eli
ab
le
r
esu
lts
.
Fu
tu
r
e
r
esear
ch
will
f
o
cu
s
o
n
en
h
an
cin
g
th
e
h
eu
r
is
tics
f
o
r
a
u
to
m
atic
m
atc
h
in
g
an
d
i
n
teg
r
atin
g
ad
d
itio
n
al
ca
s
e
s
tu
d
ies
to
f
u
r
th
er
v
alid
ate
th
e
ap
p
r
o
ac
h
.
W
e
also
p
lan
to
d
ev
elo
p
a
c
o
m
p
r
eh
e
n
s
iv
e
GUI
ap
p
licatio
n
u
s
in
g
th
e
.
NE
T
p
l
atf
o
r
m
to
f
ac
ilit
ate
b
r
o
ad
e
r
ad
o
p
tio
n
a
n
d
p
r
ac
tical
ap
p
licat
io
n
o
f
GAM
SMA,
wh
ich
will
f
u
r
th
er
e
n
h
an
ce
a
u
to
m
atic
m
atch
in
g
an
d
m
o
d
e
l
g
en
er
atio
n
i
n
v
ar
i
o
u
s
d
o
m
a
in
s
s
u
ch
as
AI
,
I
T
g
o
v
er
n
an
ce
,
E
-
lear
n
in
g
,
E
-
h
e
alth
ca
r
e,
in
ter
n
et
o
f
th
in
g
s
(
I
o
T
)
,
s
ea
r
ch
en
g
in
es,
ch
atb
o
ts
,
an
d
th
e
Sem
an
tic
W
eb
.
An
o
th
er
s
ig
n
i
f
ican
t
ar
ea
f
o
r
f
u
tu
r
e
r
esear
ch
lies
in
ap
p
ly
in
g
g
en
er
ativ
e
au
to
m
atic
m
atch
in
g
to
ad
d
r
ess
d
ata
lay
er
in
ter
o
p
er
ab
ilit
y
a
n
d
m
ig
r
atio
n
is
s
u
es.
Ou
r
team
is
cu
r
r
en
tly
wo
r
k
in
g
in
t
h
is
f
ield
,
aim
in
g
to
o
v
er
co
m
e
in
ter
o
p
er
a
b
ilit
y
p
r
o
b
lem
s
b
etwe
en
b
ig
d
ata
No
S
QL
an
d
r
elatio
n
al
d
atab
ases
.
GAM
MA
S
ca
n
h
elp
au
to
m
ate
s
y
s
tem
m
atch
in
g
s
tr
u
ctu
r
e
g
e
n
er
atio
n
a
n
d
e
x
p
lo
r
e
d
ata
lay
er
tr
an
s
f
o
r
m
atio
n
.
RE
F
E
R
E
NC
E
S
[
1
]
Z.
I
.
B
a
t
o
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t
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,
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.
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e
h
b
i
,
M
.
T
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l
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n
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.
O
mar,
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o
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rn
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4
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2
]
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I
.
B
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.
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a
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p
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,
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o
u
r
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a
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o
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h
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c
a
l
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d
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p
p
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3
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p
p
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6
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0
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5
.
[
3
]
Z.
I
.
B
a
t
o
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t
a
,
R
.
D
e
h
b
i
,
M
.
T
a
l
e
a
,
a
n
d
O
.
H
a
j
o
u
i
,
“
A
u
t
o
mat
i
o
n
i
n
c
o
d
e
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e
n
e
r
a
t
i
o
n
:
Te
r
t
i
a
r
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d
s
y
st
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t
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c
map
p
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r
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v
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w
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o
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o
q
u
i
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m
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n
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f
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rm
a
t
i
o
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c
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2
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[
4
]
X
.
Y
a
o
z
o
n
g
,
S
.
X
u
e
b
i
n
,
Z.
S
h
u
h
u
a
,
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.
Q
i
u
j
u
n
,
a
n
d
J.
W
e
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n
,
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t
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c
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l
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si
s M
e
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h
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f
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o
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l
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c
k
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n
d
D
e
f
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P
a
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M
a
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,
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n
2
0
2
3
I
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5
t
h
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r
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Po
w
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C
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p
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[
5
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F
.
A
.
S
o
m
o
g
y
i
a
n
d
M
.
A
sz
t
a
l
o
s
,
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S
y
s
t
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ma
t
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c
r
e
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w
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ma
t
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m
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-
d
r
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v
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me
t
h
o
d
o
l
o
g
i
e
s,”
S
o
f
t
w
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r
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a
n
d
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y
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m
s
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e
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6
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J.
R
e
n
e
t
a
l
.
,
“
M
a
t
c
h
i
n
g
a
l
g
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t
h
ms
:
f
u
n
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a
m
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a
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ra
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Em
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7
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T.
L
y
o
n
s
a
n
d
A
.
D
.
M
c
Le
o
d
,
“
S
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g
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a
t
u
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m
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t
h
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1
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,
2
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.
[
8
]
C
.
C
u
c
h
i
e
r
o
,
G
.
G
a
z
z
a
n
i
,
a
n
d
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.
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v
a
l
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t
o
-
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e
r
r
o
,
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S
i
g
n
a
t
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r
e
-
b
a
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d
m
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e
l
s:
t
h
e
o
r
y
a
n
d
c
a
l
i
b
r
a
t
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o
n
,
”
S
I
AM
J
o
u
r
n
a
l
o
n
Fi
n
a
n
c
i
a
l
Ma
t
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m
a
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o
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:
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.
1
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2
3
3
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.
[
9
]
M
.
T.
S
h
a
f
i
q
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Evaluation Warning : The document was created with Spire.PDF for Python.