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ca
l
t
o
em
p
lo
y
th
e
s
ch
em
a
d
esig
n
in
f
o
r
m
atio
n
“
s
ch
em
a
attr
i
b
u
t
es”
to
d
et
er
m
in
e
th
e
c
o
r
r
es
p
o
n
d
en
ce
s
at
tr
ib
u
tes
w
h
en
d
if
f
er
en
t
ab
b
r
ev
ia
ti
o
n
s
o
f
at
tr
i
b
u
t
e
n
am
es
“
co
lu
m
n
’
s
n
am
es”
is
u
s
ed
t
o
r
e
p
r
esen
t
th
e
s
am
e
r
ea
l
w
o
r
l
d
en
titi
es
o
r
o
b
je
cts
[3
]
[
9
]
.
T
h
e
r
e
a
r
e
m
an
y
r
ea
l
lif
e
a
p
p
lic
ati
o
n
s
w
h
er
e
s
ch
e
m
a
in
f
o
r
m
atio
n
is
u
n
av
aila
b
l
e
o
r
av
a
ila
b
l
e
b
u
t
w
o
r
th
less
to
b
e
u
s
e
d
,
ex
am
p
le
s
in
clu
d
in
g
h
o
m
elan
d
s
e
cu
r
i
ty
,
cr
im
e
in
v
esti
g
ati
o
n
,
c
o
u
n
te
r
t
er
r
o
r
is
m
[
3
,
8
,
1
0
]
.
T
h
u
s
,
in
th
ese
ca
s
es
,
u
tili
zin
g
th
e
in
s
tan
ce
s
is
th
e
b
es
t
av
ai
lab
le
al
te
r
n
ativ
e
to
a
ch
iev
e
th
e
s
ch
em
a
m
atch
in
g
b
etw
ee
n
d
at
ab
ases
g
iv
es
a
p
r
ec
is
e
ch
ar
ac
t
e
r
iz
ati
o
n
o
f
th
e
r
ea
l
c
o
n
ten
ts
o
f
s
ch
em
a
attr
ib
u
tes
[
1
1
]
.
I
n
s
tan
c
e
-
b
as
ed
s
ch
em
a
m
atch
in
g
atte
m
p
ts
to
ex
tr
a
ct
th
e
s
em
an
tic
r
ela
ti
o
n
s
h
ip
b
etw
ee
n
tar
g
et
e
d
att
r
i
b
u
tes
v
ia
th
ei
r
v
alu
es “
in
s
tan
c
e”
.
T
w
o
d
if
f
e
r
en
t
cl
ass
es
f
o
r
m
atch
in
g
h
av
e
b
ee
n
p
r
o
p
o
s
ed
,
n
a
m
ely
:
s
y
n
tactic
an
d
s
em
an
tic
.
T
h
e
s
y
n
tacti
c
em
p
h
asizes
o
n
th
e
h
ete
r
o
g
en
eity
in
th
e
s
tr
u
ctu
r
e
o
f
th
e
tab
l
e
(
att
r
i
b
u
tes
)
t
o
d
ete
r
m
in
e
th
e
m
atch
.
W
h
ile
th
e
s
em
an
tic
class
f
o
cu
s
es
o
n
th
e
h
ete
r
o
g
en
ei
ty
in
th
e
m
ea
n
in
g
o
f
th
e
in
s
tan
ce
s
.
Ma
n
y
tech
n
iq
u
es
h
av
e
b
e
en
p
r
o
p
o
s
e
d
th
at
r
ely
o
n
s
y
n
tacti
c,
in
c
lu
d
in
g
N
-
g
r
am
,
an
d
r
eg
u
la
r
ex
p
r
ess
i
o
n
.
W
h
ile
th
e
m
o
s
t
ef
f
ec
tiv
e
tech
n
i
q
u
es
th
a
t
r
ely
o
n
s
em
an
tic
in
clu
d
in
g
,
L
a
ten
t
S
em
an
tic
an
aly
s
is
(
L
SA
)
,
W
o
r
d
Ne
t/
T
h
e
s
au
r
u
s
,
an
d
G
o
o
g
l
e
s
im
ilar
ity
.
B
y
ex
am
in
in
g
th
e
p
r
ev
i
o
u
s
w
o
r
k
s
,
w
e
n
o
tice
d
th
at
m
o
s
t
o
f
t
ec
h
n
i
q
u
es
c
o
u
l
d
n
o
t
ac
h
iev
ed
p
r
e
cise
m
atch
in
g
f
o
r
d
if
f
e
r
en
t
d
a
ta
ty
p
es
.
I
n
o
th
e
r
w
o
r
d
s
,
s
o
m
e
o
f
th
e
tech
n
i
q
u
es
t
r
e
at
n
u
m
er
ic
v
a
lu
es
as
s
t
r
in
g
s
.
T
h
is
n
eg
ativ
ely
in
f
lu
en
ce
s
o
n
d
is
co
v
er
in
g
th
e
m
atch
an
d
d
e
te
r
i
o
r
ates
th
e
q
u
a
lity
o
f
m
atch
r
esu
l
ts
.
Sim
ilar
ly
,
o
th
e
r
tech
n
i
q
u
es
tr
ea
t
t
ex
tu
al
in
s
tan
c
e,
as
n
u
m
er
ic,
an
d
als
o
im
p
a
ct
th
e
q
u
ali
ty
o
f
th
e
m
atch
r
esu
lts
.
I
n
th
is
p
ap
e
r
,
w
e
ex
am
in
e
t
w
o
s
t
r
at
eg
ies
u
tili
zin
g
G
o
o
g
le
Sim
ilar
ity
an
d
R
eg
u
la
r
ex
p
r
ess
i
o
n
tech
n
i
q
u
es
t
o
id
en
tif
y
th
e
s
e
m
an
tic
m
atch
b
e
tw
ee
n
d
ata
b
as
e
att
r
i
b
u
tes
u
s
in
g
th
e
av
ail
a
b
le
i
n
s
tan
ce
s
.
T
h
e
s
tu
d
y
s
h
o
u
ld
ca
r
r
y
o
u
t
ex
t
en
s
iv
e
e
x
p
e
r
im
en
ts
th
at
h
el
p
r
ese
ar
c
h
er
s
in
th
is
a
r
ea
o
f
r
es
ea
r
ch
to
u
n
d
er
s
tan
d
th
e
ca
p
a
b
il
iti
es
an
d
th
e
lim
itati
o
n
s
o
f
ea
ch
t
ec
h
n
i
q
u
e
.
T
h
e
r
est
o
f
th
e
p
ap
e
r
is
o
r
g
an
i
ze
d
as
f
o
ll
o
w
s
.
T
h
e
p
r
ev
io
u
s
r
elat
e
d
w
o
r
k
s
a
r
e
r
ev
iew
ed
an
d
r
e
p
o
r
te
d
i
n
s
ec
ti
o
n
2
.
T
h
e
d
e
tai
l
d
esc
r
i
p
t
io
n
o
f
th
e
p
r
o
p
o
s
e
d
a
p
p
r
o
ac
h
f
o
r
in
s
t
an
ce
-
b
as
ed
s
ch
em
a
m
atch
in
g
h
as
b
ee
n
ex
p
l
ain
e
d
in
s
ec
ti
o
n
3
.
T
h
e
f
o
llo
w
in
g
s
ec
ti
o
n
4
r
e
p
o
r
ts
th
e
r
esu
lts
o
f
th
e
ex
p
e
r
im
en
t.
T
h
e
ex
p
e
r
im
en
t
r
esu
lts
h
av
e
b
e
en
r
e
p
o
r
t
e
d
in
s
e
cti
o
n
5
.
T
h
e
co
n
clu
s
i
o
n
is
p
r
esen
te
d
in
s
ec
ti
o
n
6
.
2.
RE
L
AT
E
D
WO
RK
I
n
s
ta
n
ce
-
b
ased
s
c
h
e
m
a
m
atch
in
g
h
as
b
ee
n
i
n
v
e
s
ti
g
ated
b
y
n
u
m
er
o
u
s
s
t
u
d
ies
t
h
at
co
n
c
en
tr
ate
o
n
en
h
a
n
ci
n
g
th
e
ac
c
u
r
ac
y
o
f
t
h
e
s
c
h
e
m
a
m
atc
h
i
n
g
r
esu
l
t
[
3
,
6
-
7
,
1
2
-
18
]
.
Dif
f
er
e
n
t
ap
p
r
o
ac
h
es
h
a
v
e
b
ee
n
p
r
o
p
o
s
ed
,
ad
o
p
ted
v
ar
io
u
s
s
t
r
ateg
ies
f
o
r
p
r
e
cise
d
eter
m
i
n
atio
n
o
f
co
r
r
esp
o
n
d
en
ce
b
et
w
ee
n
at
tr
ib
u
tes
o
f
s
ch
e
m
as.
Mo
s
t
o
f
t
h
e
p
r
ev
i
o
u
s
w
o
r
k
s
r
elate
d
to
s
ch
e
m
a
m
atc
h
in
g
u
tili
ze
d
d
i
f
f
er
e
n
t
s
i
m
ilar
it
y
m
etr
ic
s
tech
n
iq
u
es
f
o
r
d
etec
tin
g
th
e
m
atch
es i
f
t
h
e
y
e
x
is
t.
Do
an
,
A
.
,
et
al.
i
n
[
1
5
]
p
r
o
p
o
s
ed
a
m
ac
h
in
e
lear
n
i
n
g
b
ased
s
y
s
te
m
ca
lled
,
L
ea
r
n
i
n
g
So
u
r
ce
Descr
ip
tio
n
s
(
L
SD)
t
h
at
lo
ca
t
es
attr
ib
u
te
s
m
a
tch
i
n
g
i
n
a
s
e
m
i
-
au
to
m
atic
m
a
n
n
er
.
L
S
D
n
e
ed
s
to
ex
ec
u
te
s
o
m
e
ex
a
m
p
le
s
o
f
s
e
m
a
n
tic
m
ap
p
i
n
g
s
f
r
o
m
th
e
u
s
er
b
ef
o
r
e
r
u
n
n
in
g
o
n
t
h
e
r
ea
l
d
atab
ase
to
tr
ain
ea
c
h
m
ac
h
in
e
l
ea
r
n
in
g
tech
n
iq
u
e.
T
h
e
u
s
er
n
ee
d
s
to
p
r
o
v
id
e
th
e
s
e
m
a
n
tic
m
ap
p
in
g
f
o
r
a
p
r
ed
eter
m
in
ed
s
et
o
f
d
ata
r
eso
u
r
ce
s
to
b
e
u
s
ed
to
g
eth
er
w
it
h
th
e
m
ap
p
in
g
to
tr
ain
a
s
et
o
f
lear
n
er
s
.
Ho
w
ev
er
,
L
SD
ac
h
iev
ed
a
li
m
ited
ac
cu
r
ac
y
d
u
e
to
th
e
m
i
s
m
atch
o
f
s
o
m
e
tag
s
,
a
n
d
also
s
o
m
e
ta
g
s
n
ee
d
d
if
f
er
e
n
t
t
y
p
e
s
o
f
lear
n
i
n
g
b
ec
au
s
e
th
e
y
ar
e
a
m
b
ig
u
o
u
s
.
T
h
e
w
o
r
k
i
n
[
1
6
]
h
ig
h
li
g
h
ted
th
e
i
s
s
u
e
o
f
s
ch
e
m
a
m
atc
h
i
n
g
f
o
r
a
r
elatio
n
al
d
atab
ase.
A
m
ac
h
in
e
lear
n
in
g
s
tr
ate
g
y
b
ased
ap
p
r
o
ac
h
n
a
m
ed
A
u
to
p
lex
is
p
r
o
p
o
s
ed
to
id
en
tify
t
h
e
m
atc
h
b
et
w
ee
n
s
c
h
e
m
a
attr
ib
u
tes
ex
p
lo
iti
n
g
d
ata
in
s
ta
n
ce
s
.
A
u
to
p
lex
b
en
ef
its
f
r
o
m
t
h
e
av
ailab
le
ch
ar
ac
ter
i
s
tics
o
f
d
atab
ase
in
s
ta
n
ce
s
to
d
eter
m
in
e
th
e
co
r
r
esp
o
n
d
en
ce
b
et
w
ee
n
a
s
o
u
r
ce
s
c
h
e
m
a
a
n
d
g
lo
b
al
s
ch
e
m
a.
Ho
wev
er
,
lear
n
er
s
n
ee
d
r
etr
ain
in
g
w
h
en
Au
to
p
lex
ap
p
l
ied
to
a
n
e
w
d
o
m
ai
n
.
A
C
o
n
ten
t
-
B
ased
Sc
h
e
m
a
Ma
t
ch
in
g
A
l
g
o
r
ith
m
(
C
B
SM
A
)
ad
o
p
t
n
eu
r
al
n
et
w
o
r
k
s
tr
ate
g
y
is
p
r
o
p
o
s
ed
in
[
1
9
]
.
C
B
SMA
r
elies
o
n
th
e
f
u
l
l
d
is
co
v
er
y
o
f
d
ata
co
n
t
en
t
to
id
en
ti
f
y
th
e
m
atc
h
b
y
an
al
y
z
in
g
t
h
e
d
ata
p
atter
n
,
w
h
ich
is
co
n
d
u
cted
b
y
tr
ain
in
g
a
s
et
o
f
n
e
u
r
al
n
et
w
o
r
k
s
.
Mo
r
eo
v
er
,
t
h
e
w
o
r
k
in
tr
o
d
u
ce
d
i
n
[
2
0
]
s
u
g
g
e
s
ted
a
n
i
n
s
tan
ce
-
b
ased
s
c
h
e
m
a
m
atch
in
g
ap
p
r
o
ac
h
b
ased
o
n
in
f
o
r
m
at
io
n
t
h
eo
r
etic
d
is
cr
ep
an
c
y
to
id
en
ti
f
y
th
e
co
r
r
esp
o
n
d
en
ce
s
b
et
w
ee
n
s
c
h
e
m
a
s
.
Ho
w
e
v
er
,
t
h
e
w
o
r
k
co
m
p
r
i
s
es a
tec
h
n
iq
u
e
th
at
f
i
n
d
s
s
e
m
an
tic
s
i
m
ilar
it
y
i
n
s
ta
n
ce
s
b
et
w
ee
n
co
m
p
ar
ed
attr
ib
u
tes
in
d
if
f
er
en
t
tab
le
s
.
T
h
e
tec
h
n
iq
u
e
b
e
g
in
s
w
it
h
e
x
tr
ac
tin
g
in
s
ta
n
ce
s
f
r
o
m
ea
c
h
attr
ib
u
te
w
h
ic
h
is
g
o
i
n
g
to
b
e
co
m
p
ar
ed
.
T
h
en
,
f
i
n
d
s
a
s
et
o
f
ch
ar
ac
t
er
is
tics
f
r
o
m
th
e
s
e
in
s
ta
n
ce
s
u
til
izin
g
N
-
g
r
a
m
a
n
d
f
i
n
all
y
,
co
m
p
ar
e
s
th
e
c
h
a
r
ac
ter
is
tics
f
o
r
ea
ch
attr
ib
u
te.
Ho
w
ev
er
,
N
-
g
r
a
m
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4752
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci,
Vo
l
.
10
,
No
.
3
,
J
u
n
e
2
0
1
8
:
1
2
6
6
–
1
2
7
7
1268
s
tr
ateg
y
h
a
s
w
ea
k
n
es
s
es,
b
ec
au
s
e
t
h
e
u
s
e
o
f
N
-
g
r
a
m
to
f
i
n
d
s
i
m
ilar
it
y
b
et
w
ee
n
d
ata
s
o
u
r
ce
s
s
o
m
eti
m
e
s
g
i
v
e
s
w
r
o
n
g
r
es
u
lt
s
o
r
e
v
en
n
o
t
h
i
n
g
,
esp
ec
iall
y
i
n
ca
s
es
w
h
er
e
t
h
e
i
n
s
ta
n
ce
s
d
o
n
o
t
h
a
v
e
a
n
y
o
v
er
lap
o
f
N
-
g
r
a
m
w
it
h
ea
c
h
o
th
er
[
3
]
.
J
i,
F.,
et
al.
,
[
2
1
]
p
r
o
p
o
s
ed
n
e
w
in
s
ta
n
ce
-
b
ased
s
c
h
e
m
a
m
atc
h
in
g
ap
p
r
o
ac
h
b
ased
o
n
m
ac
h
i
n
e
lear
n
in
g
s
tr
ate
g
y
.
An
o
p
ti
m
al
o
b
j
ec
tiv
e
f
u
n
ctio
n
is
co
n
s
tr
u
ct
ed
as
a
r
esu
lt
o
f
th
e
m
atc
h
i
n
g
w
h
ich
d
eter
m
i
n
es
all
eq
u
i
v
ale
n
t
attr
ib
u
tes.
E
x
p
e
r
i
m
en
tal
r
esu
lts
o
f
t
h
i
s
ap
p
r
o
ac
h
elab
o
r
ated
th
at
ac
cu
r
ac
y
r
eg
ar
d
in
g
p
r
ec
is
io
n
(
P
)
is
8
5
%.
Ho
w
ev
er
,
th
e
ap
p
r
o
ac
h
is
s
u
itab
le
o
n
l
y
f
o
r
n
u
m
er
ic
i
n
s
ta
n
ce
s
,
as
t
h
e
r
esu
lt
o
f
p
r
ec
is
io
n
(
P
)
d
r
o
p
p
ed
to
6
6
%
w
h
e
n
s
tr
i
n
g
in
s
ta
n
ce
s
ar
e
co
n
s
id
er
ed
[
3
]
.
Z
ais
s
,
K.
S.
[
2
2
]
in
tr
o
d
u
ce
d
t
w
o
in
s
ta
n
ce
-
b
ased
m
atc
h
in
g
m
et
h
o
d
s
u
tili
z
in
g
n
eu
r
al
n
et
w
o
r
k
s
tr
ateg
y
.
T
h
e
f
ir
s
t
m
et
h
o
d
r
elies
o
n
th
e
s
y
n
tactic
f
ac
t
s
o
f
th
e
d
atab
ase
s
ch
e
m
a
to
g
e
n
er
ate
r
eg
u
lar
ex
p
r
es
s
io
n
s
o
r
s
am
p
le
v
alu
e
s
th
at
r
es
u
lt
i
n
to
ch
ar
ac
ter
izi
n
g
t
h
e
co
n
ce
p
ts
o
f
o
n
to
lo
g
y
b
y
th
eir
i
n
s
ta
n
ce
s
ets.
T
h
e
s
ec
o
n
d
m
eth
o
d
u
s
es
t
h
e
in
s
ta
n
ce
s
ets
to
d
escr
ib
e
th
e
co
n
ten
t
s
o
f
ev
er
y
in
s
ta
n
ce
u
s
i
n
g
a
s
et
o
f
r
eg
u
lar
ex
p
r
ess
io
n
s
.
T
h
e
w
o
r
k
co
n
tr
ib
u
ted
b
y
[
2
3
]
h
as
also
h
i
g
h
l
ig
h
ted
t
h
e
i
s
s
u
e
o
f
s
y
n
tactic
an
d
s
e
m
a
n
t
ic
s
ch
e
m
a
m
atc
h
in
g
i
n
th
e
d
atab
ase.
T
h
e
y
h
a
v
e
in
tr
o
d
u
ce
d
an
in
f
o
r
m
atio
n
t
h
eo
r
etic
d
is
cr
ep
an
c
y
b
ased
ap
p
r
o
ac
h
th
at
ai
m
s
at
id
en
ti
f
y
i
n
g
t
h
e
s
e
m
a
n
tic
a
s
w
ell
as
s
y
n
tactic
co
r
r
esp
o
n
d
en
ce
s
attr
ib
u
te
v
ia
t
h
eir
in
s
tan
ce
s
s
et
s
.
Ho
w
e
v
er
,
th
e
ex
p
er
i
m
en
t
r
es
u
lt
d
ep
ic
ts
th
a
t
t
h
e
al
g
o
r
ith
m
u
s
e
s
N
-
g
r
a
m
s
,
is
u
n
ab
le
to
i
d
en
tify
th
e
m
atc
h
e
s
b
et
w
ee
n
attr
ib
u
tes
w
it
h
s
tr
in
g
t
y
p
es
co
r
r
ec
tl
y
co
m
p
ar
ed
to
th
e
s
ec
o
n
d
al
g
o
r
ith
m
u
ti
lizes
Go
o
g
le
s
i
m
ilar
it
y
d
is
tan
ce
w
h
ich
ac
h
iev
ed
a
b
etter
r
esu
lt
f
o
r
th
e
s
a
m
e
t
y
p
e
o
f
d
ata.
B
esid
es,
th
e
w
o
r
k
p
r
esen
ted
b
y
[
1
4
]
ad
d
r
ess
ed
th
e
is
s
u
e
o
f
in
s
ta
n
ce
b
ased
s
ch
e
m
a
m
a
tch
i
n
g
i
n
th
e
d
atab
ase.
T
h
e
y
h
a
v
e
p
r
o
p
o
s
ed
a
r
u
le
-
b
ased
s
ch
e
m
a
m
atc
h
i
n
g
ap
p
r
o
ac
h
wh
ich
u
tili
ze
s
a
p
r
ed
ef
i
n
ed
r
eg
u
lar
ex
p
r
ess
io
n
to
id
en
t
if
y
t
h
e
m
atc
h
in
g
p
atter
n
s
o
f
in
s
ta
n
ce
s
.
L
ast
l
y
,
t
h
e
w
o
r
k
co
n
tr
ib
u
ted
b
y
[
8
]
tack
led
th
e
i
s
s
u
e
o
f
s
c
h
e
m
a
m
atc
h
i
n
g
b
ased
o
n
d
ata
in
s
tan
ce
s
in
th
e
r
elatio
n
al
d
atab
ase.
He
p
r
o
p
o
s
ed
a
s
ch
e
m
a
m
atc
h
i
n
g
a
p
p
r
o
ac
h
to
id
en
tify
t
h
e
co
r
r
esp
o
n
d
en
ce
s
b
et
w
ee
n
attr
ib
u
tes
b
y
f
u
l
l
y
e
x
p
lo
itin
g
th
e
in
s
tan
ce
s
f
o
r
n
u
m
er
ic,
alp
h
ab
etic
an
d
m
i
x
d
ata
t
y
p
es.
T
h
e
p
r
o
p
o
s
ed
ap
p
r
o
ac
h
em
p
lo
y
s
th
e
co
n
ce
p
t
o
f
p
atter
n
r
ec
o
g
n
it
io
n
to
cr
ea
te
r
eg
u
lar
ex
p
r
es
s
io
n
b
ased
o
n
in
s
ta
n
ce
s
i
n
o
r
d
er
to
id
en
tify
attr
ib
u
te
s
m
atc
h
e
s
f
o
r
n
u
m
er
ic
an
d
m
ix
d
ata
t
y
p
e
s
.
B
esid
es,
f
o
r
th
e
alp
h
ab
etic
d
ata
ty
p
e,
t
h
e
ap
p
r
o
ac
h
in
v
o
lv
e
s
Go
o
g
le
s
i
m
ilar
it
y
to
co
m
p
u
te
t
h
e
s
e
m
an
tic
s
i
m
ilar
i
t
y
s
co
r
e
to
ca
p
tu
r
e
th
e
s
e
m
a
n
tic
r
elatio
n
s
h
ip
s
b
et
w
ee
n
i
n
s
tan
ce
s
.
3.
T
H
E
D
E
VE
L
O
P
E
D
F
R
AM
E
WO
RK
O
F
I
NS
T
ANC
E
-
B
ASE
D
S
CH
E
M
A
M
AT
CH
I
NG
.
T
h
is
s
ec
tio
n
d
is
c
u
s
s
es
t
h
e
d
et
ails
co
m
p
o
n
en
t
s
o
f
i
n
s
ta
n
ce
-
ba
s
ed
s
ch
e
m
a
m
a
tch
i
n
g
f
r
a
m
e
w
o
r
k
w
h
ic
h
h
as
b
ee
n
ad
o
p
ted
f
r
o
m
[
8
]
.
T
h
e
f
r
a
m
e
w
o
r
k
ai
m
s
to
d
etec
t
th
e
m
atch
e
s
b
et
w
ee
n
t
w
o
s
c
h
e
m
a
attr
ib
u
te
s
v
ia
th
eir
in
s
ta
n
ce
s
ets
w
h
ic
h
co
n
s
i
s
ts
o
f
f
i
v
e
m
ain
p
h
ases
a
s
d
e
m
o
n
s
tr
ated
in
Fi
g
u
r
e
1
.
T
h
ese
p
h
ases
ar
e
I
d
en
tify
i
n
g
A
ttri
b
u
te
s
,
C
las
s
if
y
in
g
Attr
ib
u
te
s
,
Ge
n
er
atin
g
t
h
e
Op
ti
m
al
Sa
m
p
le
Size
,
I
d
en
tify
I
n
s
ta
n
ce
Si
m
i
lar
it
y
a
n
d
Ma
tch
i
n
g
A
ttri
b
u
tes,
w
h
ich
ar
e
f
u
r
t
h
er
ex
p
lai
n
ed
in
t
h
e
f
o
llo
w
i
n
g
s
u
b
s
ec
tio
n
s
.
Fig
u
r
e
1
.
T
h
e
p
h
ases
o
f
th
e
i
n
s
tan
ce
-
b
ased
s
c
h
e
m
a
m
atc
h
i
n
g
f
r
a
m
e
w
o
r
k
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4752
A
n
E
mp
ir
ica
l
C
o
mp
a
r
a
tive
S
t
u
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f I
n
s
ta
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a
s
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ch
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)
1269
3
.
1
.
Id
ent
if
y
ing
At
t
ribute
s
T
h
is
p
h
ase
in
te
n
d
s
to
id
en
ti
f
y
th
e
d
ata
t
y
p
e
o
f
ea
ch
attr
ib
u
te
of
th
e
s
o
u
r
ce
a
n
d
th
e
tar
g
et
s
c
h
e
m
a
s
b
y
an
al
y
z
in
g
th
e
c
h
ar
ac
ter
s
o
f
s
o
m
e
r
a
n
d
o
m
l
y
s
elec
ted
in
s
ta
n
ce
s
f
r
o
m
ea
c
h
attr
ib
u
te.
T
h
r
ee
d
ata
ty
p
es
o
f
an
attr
ib
u
te
h
av
e
b
ee
n
d
ef
i
n
ed
,
n
a
m
el
y
:
alp
h
ab
etic,
n
u
m
er
ic,
a
n
d
m
ix
ed
(
s
tr
in
g
,
d
i
g
it
s
a
n
d
s
p
ec
ial
ch
ar
ac
ter
s
)
.
T
h
e
in
p
u
t
co
n
s
i
s
ts
o
f
a
s
et
o
f
r
an
d
o
m
l
y
g
e
n
er
ated
s
et
o
f
in
s
ta
n
ce
s
f
r
o
m
b
o
th
s
o
u
r
ce
a
n
d
tar
g
et
s
c
h
e
m
a
s
,
w
h
ile
th
e
o
u
tp
u
t
is
th
e
id
en
ti
f
ied
d
at
a
t
y
p
e
o
f
ea
c
h
attr
ib
u
te.
T
h
e
p
r
o
ce
s
s
s
tar
t
s
b
y
r
an
d
o
m
l
y
s
ele
ctin
g
a
n
d
s
ca
n
n
in
g
s
o
m
e
i
n
s
tan
ce
s
o
f
a
n
attr
ib
u
t
e
co
u
n
tin
g
th
e
n
u
m
b
er
o
f
c
h
ar
ac
ter
s
f
o
r
ea
c
h
d
ata
t
y
p
e.
T
h
en
,
co
m
p
ar
e
t
h
e
n
u
m
b
er
o
f
ch
ar
ac
ter
s
o
f
t
h
e
d
ata
t
y
p
e
w
it
h
t
h
e
n
u
m
b
er
o
f
c
h
ar
ac
ter
s
o
f
t
h
e
s
ca
n
n
ed
i
n
s
ta
n
ce
s
.
I
f
t
h
e
n
u
m
b
er
o
f
ch
ar
ac
ter
s
o
f
t
h
e
d
ata
t
y
p
e
eq
u
iv
a
len
t
to
th
e
le
n
g
t
h
o
f
th
e
i
n
s
tan
ce
(
ex
cl
u
d
in
g
w
h
i
te
-
s
p
ac
es
)
,
a
n
d
all
ch
ar
ac
ter
s
ar
e
alp
h
ab
etic.
T
h
en
,
w
e
id
en
t
if
y
t
h
e
d
ata
t
y
p
e
o
f
th
e
i
n
s
ta
n
ce
as a
lp
h
ab
etic.
Si
m
ilar
l
y
,
if
th
e
len
g
t
h
o
f
th
e
ch
ar
ac
ter
s
o
f
t
h
e
d
ata
t
y
p
e
eq
u
al
s
to
t
h
e
n
u
m
b
er
o
f
ch
ar
ac
ter
s
o
f
t
h
e
s
ca
n
n
ed
in
s
tan
ce
a
n
d
t
h
e
ch
ar
ac
ter
s
ar
e
n
u
m
er
ic,
th
e
n
,
id
en
ti
f
y
t
h
e
d
ata
t
y
p
e
as
n
u
m
er
ic.
Oth
er
w
is
e,
t
h
e
d
ata
t
y
p
e
o
f
th
e
in
s
ta
n
ce
s
i
s
id
en
ti
f
ied
as
a
m
i
x
.
Fi
n
all
y
,
th
e
p
r
o
ce
s
s
en
d
s
b
y
co
u
n
ti
n
g
t
h
e
n
u
m
b
er
o
f
alp
h
ab
etic
,
n
u
m
er
ic
an
d
m
ix
in
s
ta
n
ce
s
a
n
d
ac
co
r
d
in
g
l
y
as
s
i
g
n
s
an
attr
ib
u
te
to
a
p
ar
ticu
lar
d
ata
ty
p
e.
3
.
2
.
Cla
s
s
if
y
ing
At
t
ribute
s
T
h
e
m
ain
p
u
r
p
o
s
e
o
f
th
is
p
h
a
s
e
is
to
r
ed
u
ce
t
h
e
n
u
m
b
er
o
f
p
o
s
s
ib
le
co
m
p
ar
is
o
n
s
n
ee
d
ed
d
u
r
in
g
t
h
e
m
atc
h
in
g
p
r
o
ce
s
s
.
T
h
is
p
h
a
s
e
r
ec
eiv
e
d
th
e
n
u
m
b
er
o
f
d
ata
t
y
p
es
id
en
ti
f
ied
f
r
o
m
th
e
p
r
ev
io
u
s
p
h
ase
a
s
an
in
p
u
t
to
cla
s
s
i
f
y
t
h
e
m
i
n
to
d
i
f
f
er
en
t
clas
s
es
b
ased
o
n
th
e
s
a
m
e
d
er
iv
ed
d
ata
t
y
p
e.
T
h
e
m
a
x
i
m
u
m
n
u
m
b
er
o
f
class
es
t
h
at
m
i
g
h
t
b
e
i
n
tr
o
d
u
ce
d
in
th
i
s
p
h
ase
d
ep
en
d
s
m
a
in
l
y
o
n
t
h
e
n
u
m
b
er
o
f
d
ata
t
y
p
es
p
r
o
d
u
ce
d
f
r
o
m
id
en
ti
f
y
i
n
g
attr
ib
u
te
s
p
h
a
s
e.
E
ac
h
clas
s
w
ill
h
o
ld
s
e
v
er
al
attr
ib
u
tes
h
av
in
g
t
h
e
s
a
m
e
d
ata
t
y
p
e
o
r
d
o
m
ai
n
.
T
h
is
p
r
o
ce
s
s
h
elp
s
to
eli
m
i
n
ate
t
h
e
ir
r
elev
an
t
co
m
p
ar
i
s
o
n
s
b
etw
ee
n
s
c
h
e
m
a
attr
ib
u
te
s
,
w
h
er
e
attr
ib
u
tes
i
n
ea
c
h
class
w
il
l
o
n
l
y
b
e
co
m
p
ar
ed
to
ea
ch
o
th
er
.
T
h
is
s
tep
en
s
u
r
es
th
at
t
h
e
attr
ib
u
tes
w
i
th
t
h
e
s
a
m
e
d
ata
t
y
p
e
ar
e
co
m
b
i
n
ed
to
g
et
h
er
in
t
h
e
s
a
m
e
class
.
3
.
3
.
G
ener
a
t
ing
t
he
O
pti
m
a
l Sa
m
p
le
Size
T
h
is
p
h
ase
ai
m
s
at
e
x
tr
ac
ti
n
g
th
e
o
p
ti
m
al
r
an
d
o
m
s
a
m
p
le
s
ize
o
f
i
n
s
ta
n
ce
s
o
f
ea
c
h
attr
ib
u
te
o
f
th
e
id
en
ti
f
ied
class
es.
T
h
is
h
elp
s
in
r
ed
u
cin
g
th
e
p
r
o
ce
s
s
i
n
g
ti
m
e
o
f
th
e
m
atch
in
g
p
r
o
ce
s
s
b
y
r
el
y
i
n
g
o
n
a
s
m
all
p
o
r
tio
n
o
f
th
e
in
s
ta
n
ce
s
i
n
t
h
e
d
atab
ase
tab
le
to
b
e
u
s
e
d
in
o
r
d
er
to
d
eter
m
i
n
e
t
h
e
s
i
m
i
lar
it
y
b
et
w
ee
n
attr
ib
u
tes.
I
t
is
o
b
v
io
u
s
t
h
at
u
tili
zi
n
g
a
s
a
m
p
le
o
f
i
n
s
ta
n
c
es
in
s
tead
o
f
in
v
o
l
v
in
g
t
h
e
e
n
tire
in
s
ta
n
ce
s
w
il
l
s
ig
n
i
f
ica
n
tl
y
i
m
p
r
o
v
e
th
e
p
er
f
o
r
m
a
n
ce
o
f
th
e
m
atch
in
g
ap
p
r
o
ac
h
,
an
d
av
o
id
u
n
n
ec
es
s
ar
y
ac
ce
s
s
to
a
lar
g
e
p
o
r
tio
n
o
f
th
e
i
n
s
ta
n
ce
s
.
I
n
t
h
is
w
o
r
k
,
w
e
s
et
u
p
th
e
o
p
ti
m
a
l
s
a
m
p
le
s
ize
to
b
e
u
p
to
5
0
%
o
f
th
e
ac
t
u
al
tab
le
s
ize
to
m
ai
n
tai
n
a
g
o
o
d
lev
el
o
f
ac
cu
r
ac
y
[
2
4
]
.
3
.
4
.
I
ns
t
a
nce
Si
m
ila
rit
y
I
dentif
ic
a
t
io
n P
ha
s
e
T
h
is
p
h
ase
f
o
cu
s
e
s
o
n
co
m
p
ar
in
g
attr
ib
u
tes
o
f
d
if
f
er
en
t
s
c
h
e
m
as
b
elo
n
g
s
to
t
h
e
s
a
m
e
clas
s
to
ch
ec
k
if
t
h
e
y
ar
e
r
ep
r
esen
ti
n
g
t
h
e
s
a
m
e
e
n
tit
y
o
r
n
o
t.
T
w
o
d
i
f
f
er
e
n
t
i
n
s
ta
n
ce
s
i
m
ilar
it
y
id
en
tific
atio
n
m
eth
o
d
s
h
a
v
e
b
ee
n
d
ev
elo
p
ed
u
n
d
er
t
h
is
p
h
ase,
n
a
m
el
y
:
(
1
)
R
eg
u
lar
e
x
p
r
ess
io
n
f
o
r
s
y
n
tactic
s
i
m
ilar
it
y
,
an
d
(
2
)
Go
o
g
le
f
o
r
s
e
m
a
n
tic
s
i
m
ilar
it
y
.
B
o
th
m
e
th
o
d
s
atte
m
p
t
to
id
en
tify
t
h
e
c
o
r
r
esp
o
n
d
en
ce
s
b
et
w
ee
n
attr
i
b
u
tes
i
n
ea
ch
clas
s
.
T
h
is
p
h
ase
co
n
s
id
er
s
th
e
m
o
s
t
s
ig
n
if
ican
t
p
h
a
s
e
in
t
h
e
in
s
tan
ce
-
b
ased
s
ch
e
m
a
m
a
tch
i
n
g
p
r
o
ce
s
s
w
h
ic
h
tr
ies
to
ex
tr
ac
t
s
i
m
ilar
it
ies
a
m
o
n
g
in
s
t
an
ce
s
t
h
r
o
u
g
h
p
air
w
i
s
e
co
m
p
a
r
is
o
n
s
b
et
w
ee
n
in
s
ta
n
ce
s
et
s
i
n
o
r
d
er
to
m
ea
s
u
r
e
t
h
e
m
atc
h
b
et
w
ee
n
t
h
eir
attr
i
b
u
tes.
E
ac
h
in
s
ta
n
ce
is
co
m
p
ar
ed
h
ea
d
-
to
-
h
ea
d
(
o
n
e
-
on
-
o
n
e)
w
it
h
ea
ch
o
f
th
e
o
th
er
in
s
ta
n
ce
s
.
I
n
th
i
s
p
h
ase,
w
e
h
a
v
e
i
m
p
le
m
en
ted
t
w
o
d
if
f
er
en
t
m
e
th
o
d
s
id
en
ti
f
y
t
h
e
s
i
m
ilar
itie
s
b
et
w
ee
n
in
s
ta
n
ce
s
s
et
s
.
T
h
e
f
ir
s
t
m
et
h
o
d
r
eg
u
lar
e
x
p
r
ess
io
n
r
el
ies
o
n
th
e
s
y
n
tactic
s
i
m
ilar
itie
s
b
et
w
ee
n
i
n
s
ta
n
ce
s
,
w
h
ile
t
h
e
s
ec
o
n
d
m
et
h
o
d
Go
o
g
le
s
i
m
ilar
it
y
e
m
p
lo
y
s
t
h
e
s
e
m
an
tic
s
i
m
ilar
it
ies
to
id
en
ti
f
y
th
e
co
r
r
esp
o
n
d
en
ce
s
b
et
w
ee
n
attr
ib
u
te
s
.
T
h
ese
m
et
h
o
d
s
ar
e
f
u
r
t
h
er
ex
p
lai
n
ed
in
t
h
e
f
o
llo
w
i
n
g
s
u
b
s
ec
tio
n
s
.
3
.
4
.
1
Reg
ula
r
E
x
pre
s
s
io
n (
Reg
e
x
e
s
)
R
eg
u
lar
ex
p
r
ess
io
n
m
et
h
o
d
h
elp
s
in
id
en
t
if
y
i
n
g
th
e
s
y
n
tactic
s
i
m
ilar
it
y
b
et
w
ee
n
t
wo
s
ets
o
f
in
s
ta
n
ce
s
f
r
o
m
t
w
o
d
i
f
f
er
en
t
s
ch
e
m
a
s
u
s
i
n
g
t
h
e
r
eg
u
lar
e
x
p
r
ess
io
n
o
f
th
e
i
n
s
tan
ce
s
.
A
r
eg
u
lar
ex
p
r
ess
io
n
is
a
s
tr
in
g
co
n
tai
n
in
g
a
co
m
b
i
n
ati
o
n
o
f
n
o
r
m
al
c
h
ar
ac
ter
s
a
n
d
s
p
ec
ial
c
h
ar
ac
ter
s
s
u
c
h
a
s
(
*
,
+,
%).
O
n
e
o
f
it
s
b
en
ef
it
s
is
an
i
n
ex
p
e
n
s
i
v
e
p
r
o
ce
s
s
as
it
d
o
es
n
o
t
n
ee
d
tr
ain
in
g
o
r
lear
n
in
g
p
r
o
ce
s
s
es.
Fu
r
t
h
er
m
o
r
e,
it
is
q
u
ic
k
an
d
co
n
cise
in
ca
p
tu
r
i
n
g
v
al
u
ab
le
u
s
er
k
n
o
w
led
g
e
ab
o
u
t
th
e
d
o
m
ai
n
[
3
,
7
-
8
]
.
Us
in
g
r
eg
u
lar
e
x
p
r
ess
io
n
s
u
g
g
e
s
ts
t
h
at
t
h
e
s
et
o
f
i
n
s
ta
n
ce
s
s
h
o
u
ld
b
e
r
ep
r
esen
ted
as
o
n
e
s
in
g
le
p
atter
n
i
n
o
r
d
er
to
p
r
o
v
id
e
an
ac
cu
r
ate
m
atc
h
in
g
r
es
u
lt
b
et
w
ee
n
in
s
ta
n
ce
s
.
R
eg
E
x
is
d
esi
g
n
ed
to
f
i
n
d
a
p
ar
ticu
lar
r
eg
u
lar
ex
p
r
ess
i
o
n
th
at
d
escr
ib
es
a
s
et
o
f
d
ata
v
alu
e
s
(
in
s
ta
n
ce
s
)
.
T
h
u
s
,
it
ca
n
b
e
p
o
s
s
ib
le
to
c
r
ea
te
a
r
eg
u
lar
ex
p
r
ess
io
n
t
h
at
f
its
t
h
e
m
aj
o
r
it
y
o
f
th
e
in
s
ta
n
ce
s
s
et
s
y
n
tactica
ll
y
(
f
o
r
m
a
ts
)
in
o
r
d
er
to
id
en
tif
y
t
h
e
s
i
m
ilar
i
t
y
b
et
w
ee
n
d
if
f
e
r
en
t
in
s
tan
ce
s
s
ets
.
T
h
e
p
r
o
ce
s
s
o
f
g
en
er
ati
n
g
a
r
eg
u
lar
e
x
p
r
ess
io
n
is
p
er
f
o
r
m
ed
in
t
w
o
w
a
y
s
r
eg
ar
d
in
g
t
h
e
d
ata
t
y
p
es
o
f
t
h
e
attr
ib
u
tes.
Fo
r
n
u
m
er
ic
attr
ib
u
tes,
th
e
p
r
o
ce
s
s
o
f
g
en
er
ati
n
g
attr
ib
u
tes
R
eg
E
x
is
s
ep
ar
atel
y
p
er
f
o
r
m
ed
d
u
e
to
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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h
ab
etica
l
a
n
d
m
i
x
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attr
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s
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t
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t
ing
Reg
E
x
f
o
r
Nu
m
e
ric
Da
t
a
T
y
pe
At
t
ribute
s
I
n
s
ta
n
ce
s
b
elo
n
g
to
n
u
m
er
ic
attr
ib
u
te
co
n
s
is
t
s
o
f
d
i
g
its
’
ch
ar
ac
ter
s
o
n
l
y
i
n
t
h
e
r
an
g
e
o
f
0
-
9
.
B
asicall
y
,
r
eg
e
x
es
m
et
h
o
d
n
e
ed
s
to
id
en
tify
th
e
m
i
n
i
m
u
m
an
d
m
a
x
i
m
u
m
v
alu
e
s
o
f
t
h
e
attr
ib
u
tes
to
g
e
n
er
ate
th
e
r
eg
u
lar
e
x
p
r
ess
io
n
f
o
r
a
n
u
m
er
ic
attr
ib
u
te.
T
h
e
m
i
n
i
m
u
m
a
n
d
m
a
x
i
m
u
m
v
a
lu
e
s
ar
e
a
s
s
i
g
n
ed
to
t
h
e
i
n
it
ial
v
alu
e
s
o
f
t
h
e
attr
ib
u
tes.
I
n
ad
d
itio
n
,
th
e
u
p
p
er
is
also
n
ee
d
ed
w
h
ic
h
is
g
r
ea
ter
t
h
a
n
t
h
e
v
a
lu
e
o
f
m
in
an
d
le
s
s
th
an
t
h
e
v
al
u
e
o
f
m
ax
.
T
h
r
ee
v
ar
iab
les
n
ee
d
to
b
e
id
en
ti
f
ied
,
n
a
m
el
y
:
m
i
n
,
m
ax
,
a
n
d
u
p
p
er
.
T
h
e
u
p
p
er
is
d
er
iv
ed
if
o
n
e
o
f
t
h
e
f
o
llo
w
i
n
g
co
n
d
itio
n
s
h
o
ld
s
:
i.
I
f
th
e
le
n
g
th
o
f
th
e
m
in
is
les
s
th
a
n
th
e
le
n
g
t
h
o
f
t
h
e
ma
x
,
th
en
t
h
e
u
p
p
er
is
th
e
ma
x
v
alu
e
b
ased
o
n
th
e
min
len
g
t
h
a
n
d
n
o
t
g
r
ea
ter
th
a
n
t
h
e
v
al
u
e
o
f
ma
x
.
Fo
r
e
x
a
m
p
le,
s
u
p
p
o
s
e
t
h
e
m
in
v
al
u
e
is
6
5
4
.
T
h
er
ef
o
r
e,
its
len
g
t
h
is
t
h
r
ee
,
th
e
p
o
s
s
ib
le
ma
x
v
alu
e
o
f
t
h
e
len
g
t
h
o
f
t
h
e
min
i
v
alu
e
i
s
9
9
9
.
T
h
er
ef
o
r
e,
9
9
9
is
s
aid
to
b
e
th
e
u
p
p
er
ma
x
i
m
u
m
o
f
t
h
e
min
i
v
alu
e
le
n
g
t
h
.
T
h
en
w
e
ch
ec
k
a
g
ai
n
if
t
h
e
u
p
p
er
is
g
r
ea
ter
th
an
t
h
e
ma
x
v
alu
e.
T
h
er
ef
o
r
e,
th
e
f
ir
s
t
d
ig
it
o
f
th
e
u
p
p
er
is
r
ep
lace
d
b
y
t
h
e
f
ir
s
t
d
ig
i
t
o
f
th
e
m
in
v
a
lu
e
(
i.e
.
:
6
9
9
)
.
I
f
t
h
is
n
e
w
u
p
p
er
is
s
t
ill
g
r
ea
te
r
th
a
n
t
h
e
ma
x
v
al
u
e,
t
h
e
s
ec
o
n
d
d
ig
i
t o
f
u
p
p
er
r
ep
lace
s
t
h
e
s
ec
o
n
d
d
ig
i
t o
f
th
e
min
v
al
u
e
(
i.e
.
:
6
5
9
)
.
T
h
is
iter
atio
n
w
il
l
s
u
b
s
eq
u
en
tl
y
p
er
f
o
r
m
u
n
til
it
m
ee
ts
t
h
e
ab
o
v
e
co
n
d
itio
n
o
f
u
p
p
er
.
Ho
w
e
v
er
,
in
t
h
e
ca
s
e
w
h
er
e
u
p
p
er
iter
atio
n
r
esu
lt
s
t
o
b
e
eq
u
al
to
th
e
min
v
alu
e,
th
er
ef
o
r
e
t
h
e
ma
x
v
alu
e
i
s
d
en
o
ted
as th
e
u
p
p
er
.
ii.
W
h
en
t
h
e
d
ig
its
’
le
n
g
th
o
f
min
is
eq
u
al
to
t
h
e
d
ig
its
’
le
n
g
th
o
f
ma
x
an
d
min
h
as a
t
least o
n
e
ze
r
o
d
ig
its
o
n
th
e
r
ig
h
t,
t
h
e
u
p
p
er
is
d
er
iv
ed
u
s
i
n
g
th
e
f
o
r
m
u
la
g
iv
e
n
b
elo
w
.
Up
p
er
= (
ma
x
-
(
min
MO
D
s
u
mz
*
1
0
)
B.
G
ener
a
t
ing
Reg
E
x
f
o
r
Alph
a
bet
ic
a
nd
M
ix
Da
t
a
T
y
pe
At
t
ribute
s
T
h
is
s
ec
tio
n
ex
p
lai
n
s
t
h
e
d
e
tail
s
tep
s
o
f
g
e
n
er
atin
g
th
e
r
eg
u
lar
e
x
p
r
ess
io
n
f
o
r
attr
ib
u
tes
w
it
h
alp
h
ab
etic
a
n
d
m
ix
d
ata
u
s
i
n
g
r
eg
u
lar
e
x
p
r
ess
io
n
tec
h
n
iq
u
e.
T
h
e
id
ea
o
f
g
e
n
er
ati
n
g
a
r
e
g
u
lar
e
x
p
r
ess
io
n
f
o
r
alp
h
ab
etic
an
d
m
ix
d
ata
t
y
p
e
s
r
elies
o
n
d
i
v
id
in
g
a
n
i
n
s
tan
c
e
in
to
a
s
et
o
f
s
u
b
-
to
k
en
s
.
T
h
i
s
co
n
ce
p
t
h
as
b
ee
n
ap
p
lied
in
r
eg
u
lar
ex
p
r
ess
io
n
ap
p
r
o
ac
h
to
co
n
s
tr
u
cti
n
g
a
r
eg
u
lar
ex
p
r
es
s
io
n
f
o
r
attr
ib
u
tes
w
ith
m
i
x
an
d
alp
h
ab
etic
d
ata
t
y
p
e
s
.
T
h
e
d
er
iv
ed
s
u
b
-
to
k
e
n
s
co
n
tai
n
a
s
et
o
f
ch
ar
ac
ter
s
o
f
a
p
ar
ticu
lar
d
ata
t
y
p
e
t
h
at
w
ill
b
e
p
r
o
ce
s
s
ed
s
ep
ar
atel
y
to
g
e
n
er
ate
th
e
r
e
g
u
lar
e
x
p
r
ess
io
n
s
o
f
th
e
i
n
s
tan
ce
.
E
v
en
tu
al
l
y
,
t
h
e
co
n
s
tr
u
cted
r
e
g
u
lar
ex
p
r
ess
io
n
s
o
f
t
h
e
s
u
b
-
to
k
en
s
ar
e
co
m
b
i
n
ed
to
g
eth
er
to
f
o
r
m
th
e
r
eg
u
lar
ex
p
r
ess
io
n
o
f
t
h
e
i
n
s
ta
n
ce
.
W
h
er
e
s
u
mz
r
e
f
er
s
to
t
h
e
n
u
m
b
er
o
f
ze
r
o
’
s
i
n
t
h
e
min
i
.
I
f
th
e
v
al
u
e
r
etu
r
n
s
f
r
o
m
th
e
ab
o
v
e
eq
u
atio
n
less
th
a
n
ma
x
an
d
g
r
ea
ter
th
an
min
,
th
en
ass
i
g
n
ed
th
e
v
alu
e
t
o
u
p
p
er
.
Oth
er
w
i
s
e,
ap
p
ly
t
h
e
s
tep
s
in
co
n
d
itio
n
(
i)
[
3
]
.
T
o
g
en
er
ate
a
r
eg
u
lar
e
x
p
r
ess
io
n
f
o
r
n
u
m
er
ic
d
ata
t
y
p
e
attr
ib
u
te,
a
n
i
n
ter
v
al
n
ee
d
s
to
b
e
d
er
iv
ed
b
ased
o
n
min
len
g
t
h
a
n
d
its
v
al
u
e,
a
n
d
th
e
v
al
u
e
o
f
u
p
p
er
.
T
h
e
p
r
o
ce
s
s
o
f
d
er
iv
in
g
i
n
ter
v
al
an
d
cr
ea
tin
g
a
r
eg
u
lar
ex
p
r
ess
io
n
f
o
r
th
at
p
ar
ticu
lar
i
n
ter
v
al
co
n
tin
u
es
u
n
til
u
p
p
er
=
ma
x
.
L
astl
y
,
t
h
e
cr
ea
ted
r
eg
u
lar
ex
p
r
ess
io
n
s
o
f
th
ese
d
er
iv
ed
i
n
ter
v
a
ls
ar
e
m
e
r
g
ed
to
g
eth
er
i
n
o
n
e
s
in
g
le
r
e
g
u
lar
e
x
p
r
ess
io
n
u
s
in
g
|
o
p
er
ato
r
to
in
d
icate
th
e
r
eg
u
lar
ex
p
r
es
s
io
n
o
f
th
e
attr
ib
u
te
[
3
]
.
3
.
4
.
2
G
o
o
g
le
Si
m
ila
rit
y
Dis
t
a
nce
Go
o
g
le
s
i
m
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it
y
tech
n
iq
u
e
e
x
p
lo
its
t
h
e
lar
g
es
t
d
atab
ase
wh
ich
is
a
W
o
r
ld
W
id
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eb
as
a
s
o
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r
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f
s
ea
r
ch
an
d
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m
p
lo
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Go
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a
s
ea
r
ch
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n
g
i
n
e
f
o
r
th
i
s
d
atab
ase.
T
h
e
b
elo
w
eq
u
a
tio
n
d
escr
ib
es
h
o
w
th
e
Go
o
g
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s
i
m
i
lar
it
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tec
h
n
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s
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Go
o
g
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p
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co
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n
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id
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t
h
e
s
i
m
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it
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o
f
w
o
r
d
s
an
d
p
h
r
ases
f
r
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m
W
o
r
ld
W
id
e
W
e
b
[
3
,
2
5
]
:
(
)
*
(
)
(
)
+
(
)
*
(
)
(
)
+
(
1
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W
h
er
e:
f
(
x
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: r
ef
er
s
to
th
e
n
u
m
b
er
o
f
G
o
o
g
le
h
its
f
o
r
th
e
s
ea
r
c
h
ter
m
x
.
f
(
y
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: r
ef
er
s
to
th
e
n
u
m
b
er
o
f
G
o
o
g
le
h
its
f
o
r
th
e
s
ea
r
c
h
ter
m
y
.
f
(
x
,
y
)
: r
ep
r
ese
n
ts
t
h
e
n
u
m
b
er
o
f
Go
o
g
le
h
it
s
f
o
r
b
o
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ter
m
s
x
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d
y
to
g
eth
er
.
M:
in
d
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t
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u
m
b
er
o
f
w
e
b
p
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es in
d
ex
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Go
o
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le.
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ip
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w
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n
tar
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d
s
u
b
j
ec
ts
[
3
,
8
,
2
5
]
.
I
n
co
n
tr
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to
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th
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s
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m
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a
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u
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t
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m
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n
g
t
w
o
d
if
f
er
en
t
i
n
s
ta
n
ce
s
f
o
r
d
if
f
er
en
t
attr
ib
u
tes.
W
e
f
ir
s
t
s
tar
t
s
ea
r
ch
in
g
i
n
Go
o
g
le
w
eb
p
ag
es
f
o
r
ea
ch
ter
m
s
ep
ar
atel
y
to
f
in
d
th
e
n
u
m
b
er
o
f
o
cc
u
r
r
en
ce
s
o
f
t
h
e
s
e
ter
m
s
in
Go
o
g
le
w
eb
p
ag
e
s
.
T
h
en
,
w
e
co
n
tin
u
e
t
h
e
s
ea
r
c
h
f
o
r
th
o
s
e
p
ag
es
co
n
tai
n
b
o
th
ter
m
s
“
d
o
cto
r
”
an
d
“
p
r
o
f
e
s
s
o
r
”
to
g
eth
er
to
r
etr
iev
e
t
h
e
to
t
al
n
u
m
b
er
o
f
p
a
g
es
w
h
er
e
th
ese
t
w
o
ar
e
f
o
u
n
d
.
E
v
en
t
u
all
y
,
w
e
w
ill
h
a
v
e
t
h
e
n
u
m
b
er
o
f
h
i
ts
f
o
r
b
o
th
f
o
u
n
d
ed
ter
m
s
,
a
n
d
th
e
n
u
m
b
er
o
f
h
it
s
f
o
r
ea
ch
ter
m
f
o
u
n
d
s
ep
ar
atel
y
.
Fu
r
t
h
er
m
o
r
e
,
w
e
al
s
o
in
v
o
lv
e
th
e
c
u
r
r
en
t
t
o
tal
n
u
m
b
er
o
f
p
ag
e
s
i
n
d
ex
ed
b
y
Go
o
g
le
en
g
i
n
e
in
W
W
W
d
atab
ase
w
h
ich
is
3
0
0
0
,
0
0
0
,
0
0
0
a
p
p
r
o
x
i
m
atel
y
.
T
h
en
s
u
b
s
tit
u
te
t
h
e
o
b
tain
ed
v
a
l
u
es
i
n
t
h
e
eq
u
atio
n
(
1
)
to
p
r
o
d
u
ce
th
e
s
i
m
ilar
it
y
d
eg
r
ee
GSD
b
et
w
ee
n
t
h
e
t
w
o
te
r
m
s
“
d
o
cto
r
”
an
d
“
p
r
o
f
ess
o
r
”.
W
h
en
th
e
v
alu
e
o
f
GSD
is
clo
s
e
to
ze
r
o
,
in
d
icate
s
th
at
n
o
s
e
m
a
n
tic
r
elatio
n
s
h
ip
b
et
w
ee
n
t
h
e
t
w
o
ter
m
s
is
d
ete
cted
.
Oth
er
w
is
e,
if
th
e
s
co
r
e
v
al
u
e
is
c
lo
s
e
to
1
,
t
h
en
it
is
a
s
s
u
m
ed
t
h
at
t
h
e
t
w
o
ter
m
s
ar
e
s
e
m
a
n
ticall
y
r
elate
d
,
an
d
t
h
e
t
w
o
v
a
lu
e
s
r
ep
r
esen
t a
p
r
o
p
er
ty
o
f
th
e
s
a
m
e
en
t
it
y
[
2
6
]
.
A.
F
ind
Si
m
i
la
rit
y
Sco
re
f
o
r
At
t
ribute
s
Go
o
g
le
s
i
m
i
lar
it
y
i
s
th
e
s
ec
o
n
d
ap
p
r
o
ac
h
th
at
h
as
b
ee
n
co
n
s
id
er
ed
in
th
i
s
th
e
s
is
to
d
eter
m
in
e
t
h
e
co
r
r
esp
o
n
d
en
ce
b
et
w
ee
n
attr
i
b
u
tes.
I
t
is
u
s
ed
to
id
en
tify
t
h
e
m
atc
h
b
et
w
ee
n
alp
h
ab
e
tic,
n
u
m
er
ic,
a
n
d
m
i
x
ed
d
ata
t
y
p
e
attr
ib
u
te
s
.
T
h
e
id
ea
o
f
Go
o
g
le
s
i
m
ilar
it
y
ap
p
r
o
ac
h
is
r
el
y
in
g
o
n
co
m
p
u
t
in
g
t
h
e
s
e
m
an
tic
s
i
m
i
lar
it
y
s
co
r
e
b
et
w
ee
n
i
n
s
ta
n
ce
s
to
d
is
co
v
er
t
h
e
s
e
m
a
n
tic
r
elatio
n
s
h
ip
b
et
w
ee
n
attr
ib
u
tes
o
f
th
e
s
o
u
r
ce
an
d
tar
g
et
s
ch
e
m
as.
I
t
is
i
n
co
n
tr
as
t
to
r
eg
u
lar
e
x
p
r
ess
io
n
ap
p
r
o
ac
h
th
a
t
u
tili
ze
s
th
e
s
ch
e
m
a
i
n
f
o
r
m
at
io
n
w
ith
o
u
t
ta
k
in
g
in
to
ac
co
u
n
t
th
e
i
m
p
licit
s
e
m
a
n
tic
r
elatio
n
s
h
ip
b
et
w
ee
n
attr
i
b
u
tes.
3
.
5
.
At
t
ribute
M
a
t
ching
P
ha
s
e
A
ttrib
u
te
m
atc
h
i
n
g
is
t
h
e
last
s
tag
e
in
th
e
p
r
o
ce
s
s
o
f
in
s
ta
n
c
e
-
b
ased
s
c
h
e
m
a
m
atc
h
in
g
.
I
n
t
h
is
p
h
ase,
w
e
a
tte
m
p
t
to
id
en
tify
th
e
co
r
r
ec
t
m
atc
h
b
et
w
ee
n
t
h
e
attr
i
b
u
tes
t
h
at
s
h
ar
ed
s
a
m
e
d
ata
t
y
p
e
an
d
e
v
e
n
tu
a
ll
y
m
ap
p
in
g
th
e
m
.
T
h
e
p
r
o
ce
s
s
is
ca
r
r
ied
o
u
t
af
ter
p
er
f
o
r
m
i
n
g
t
h
e
tas
k
o
f
s
y
n
tactic
an
d
s
e
m
a
n
tic
m
atch
in
g
in
t
h
e
p
r
ev
io
u
s
p
h
a
s
e.
I
n
th
i
s
p
h
a
s
e,
a
d
ec
is
io
n
n
ee
d
s
to
b
e
m
ad
e
w
h
et
h
er
t
w
o
d
if
f
er
e
n
t
attr
ib
u
tes
ar
e
co
n
s
id
er
ed
s
i
m
ilar
o
r
n
o
t.
D
u
e
to
co
n
s
i
d
er
in
g
t
w
o
d
i
f
f
er
en
t
tec
h
n
iq
u
es
w
h
ic
h
ar
e
a
r
e
g
u
lar
e
x
p
r
ess
io
n
a
n
d
Go
o
g
le
s
i
m
ilar
it
y
to
id
en
ti
f
y
th
e
m
at
ch
b
et
w
ee
n
attr
ib
u
te
s
;
co
n
s
eq
u
en
t
l
y
,
i
n
th
i
s
p
h
ase,
t
w
o
m
a
tch
i
n
g
m
ec
h
a
n
i
s
m
s
h
av
e
b
ee
n
i
m
p
le
m
e
n
ted
to
h
a
n
d
le
t
h
e
m
ap
p
in
g
tas
k
,
n
a
m
el
y
:
r
e
g
u
lar
e
x
p
r
ess
io
n
-
b
ased
at
tr
ib
u
te
m
atch
in
g
a
n
d
Go
o
g
le
s
i
m
ilar
it
y
b
a
s
ed
attr
ib
u
te
m
a
tch
i
n
g
.
4.
E
XP
E
R
I
M
E
NT
R
E
SU
L
T
S
T
o
f
air
ly
e
v
alu
a
te
t
h
e
i
n
s
tan
ce
-
b
ased
s
c
h
e
m
a
m
atc
h
i
n
g
te
ch
n
iq
u
es
co
n
s
id
er
ed
in
th
i
s
p
ap
er
,
t
w
o
d
if
f
er
e
n
t
t
y
p
es
o
f
t
h
e
d
ata
s
et
s
h
a
v
e
b
ee
n
u
s
ed
i
n
t
h
e
e
x
p
er
i
m
e
n
t
s
tu
d
y
,
n
a
m
el
y
:
s
y
n
t
h
eti
c
an
d
r
ea
l
d
ata
s
e
ts
.
Fo
r
s
y
n
t
h
etic
d
ata
s
et,
a
n
o
n
l
in
e
d
ata
g
e
n
er
ato
r
n
a
m
ed
B
E
T
A
h
a
s
b
ee
n
u
s
ed
.
I
n
t
h
i
s
t
y
p
e
o
f
d
ata
s
et,
th
e
attr
ib
u
tes
ar
e
g
e
n
er
ated
b
y
s
e
ttin
g
o
u
t
t
h
eir
ap
p
r
o
p
r
iate
n
a
m
es,
d
ata
t
y
p
e
s
,
d
ata
r
a
n
g
e
s
(
if
n
ee
d
ed
)
,
an
d
t
h
e
s
ize
o
f
th
e
d
ata.
W
e
h
a
v
e
d
e
v
elo
p
ed
a
u
n
iv
er
s
it
y
d
atab
ase
th
at
co
n
s
i
s
ts
o
f
a
s
et
o
f
attr
i
b
u
tes
w
i
th
d
if
f
er
en
t
t
y
p
es o
f
d
ata
a
n
d
v
ar
y
i
n
g
r
an
g
e
o
f
v
al
u
es.
T
h
e
m
ai
n
r
ea
s
o
n
b
eh
in
d
s
elec
ti
n
g
t
h
is
t
y
p
e
o
f
d
at
a
s
et
i
s
to
o
b
tai
n
a
d
ee
p
in
s
ig
h
t
a
n
d
b
etter
u
n
d
er
s
tan
d
in
g
o
f
t
h
e
ef
f
ec
t
o
f
d
ata
ch
ar
ac
ter
is
tic
s
o
n
t
h
e
b
eh
av
io
r
an
d
t
h
e
p
er
f
o
r
m
a
n
ce
o
f
th
e
d
e
v
elo
p
ed
u
n
d
er
co
m
p
ar
is
o
n
.
Fu
r
t
h
er
m
o
r
e,
t
w
o
r
ea
l
d
ata
s
e
ts
(
R
e
s
tau
r
an
t
a
n
d
C
e
n
s
u
s
)
h
av
e
b
ee
n
u
s
ed
in
th
e
ex
p
er
i
m
en
ts
to
ex
a
m
i
n
e
f
air
l
y
t
h
e
a
p
p
r
o
ac
h
es
co
n
s
id
er
ed
in
th
is
t
h
esi
s
.
T
h
ese
r
ea
l
d
ata
s
ets
h
av
e
b
ee
n
u
s
ed
in
m
o
s
t
p
r
ev
io
u
s
w
o
r
k
s
r
elate
d
to
th
e
ar
ea
o
f
s
c
h
e
m
a
m
atc
h
in
g
i
n
d
atab
ase,
an
d
p
ar
ticu
lar
l
y
f
o
r
in
s
ta
n
ce
-
b
ased
s
ch
e
m
a
m
atc
h
i
n
g
[
8
-
9
,
1
4
,
2
7
-
2
8
]
.
B
o
th
R
esta
u
r
an
t
a
n
d
ce
n
s
u
s
d
ata
s
et
s
ar
e
av
ailab
le
o
n
li
n
e.
I
n
th
e
e
x
p
er
i
m
e
n
t
t
w
o
s
u
b
-
tab
les
h
a
v
e
b
ee
n
d
er
iv
ed
f
r
o
m
t
h
e
o
r
ig
in
al
tab
le
s
o
f
t
h
e
d
ata
s
ets.
T
h
ese
t
w
o
s
u
b
-
tab
les
r
ep
r
esen
t
t
h
e
s
o
u
r
ce
s
ch
e
m
a
a
n
d
tar
g
et
s
c
h
e
m
a
i
n
t
h
e
ex
p
er
i
m
en
ts
.
T
h
e
s
et
o
f
attr
ib
u
te
s
b
elo
n
g
s
to
t
h
e
s
o
u
r
ce
an
d
tar
g
et
s
c
h
e
m
a
h
as
b
ee
n
g
e
n
er
ate
d
r
an
d
o
m
l
y
an
d
t
h
e
n
u
m
b
er
o
f
attr
ib
u
te
s
in
ea
c
h
s
u
b
-
tab
le
is
eq
u
i
v
ale
n
t
to
t
h
e
n
u
m
b
er
o
f
attr
ib
u
te
s
o
f
t
h
e
o
r
ig
i
n
al
tab
le.
Fo
r
ea
ch
s
u
b
-
tab
l
e,
a
s
et
o
f
r
an
d
o
m
d
if
f
er
e
n
t
i
n
s
tan
ce
s
i
s
i
n
s
er
ted
r
ef
er
r
in
g
to
th
e
o
r
ig
i
n
al
tab
l
e
o
f
t
h
e
d
ata
s
et
[
8
,
2
9
]
.
T
w
o
an
al
y
s
e
s
t
h
at
h
a
v
e
b
ee
n
co
n
d
u
cted
,
th
e
f
ir
s
t
a
n
al
y
s
i
s
e
m
p
h
asize
s
o
n
id
en
ti
f
y
i
n
g
th
e
o
p
ti
m
al
s
a
m
p
le
s
ize
o
f
i
n
s
ta
n
ce
s
to
ac
h
iev
e
ac
ce
p
tab
le
ac
cu
r
ac
y
r
es
u
lt
s
f
o
r
t
h
e
m
atc
h
in
g
p
r
o
ce
s
s
.
T
h
e
s
ec
o
n
d
a
n
al
y
s
is
i
n
ten
d
s
to
co
m
p
ar
e
t
h
e
p
er
f
o
r
m
a
n
ce
o
f
b
o
th
tec
h
n
iq
u
es
in
ter
m
s
o
f
p
r
ec
is
io
n
(
P
)
an
d
r
ec
all
(
R
)
an
d
F
-
m
ea
s
u
r
e
(
F
).
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4752
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci,
Vo
l
.
10
,
No
.
3
,
J
u
n
e
2
0
1
8
:
1
2
6
6
–
1
2
7
7
1272
4
.
1
E
x
peri
m
e
nt
1
T
h
is
an
al
y
s
i
s
h
i
g
h
l
ig
h
ts
th
e
e
x
p
er
i
m
e
n
t
o
f
s
elec
t
in
g
t
h
e
o
p
ti
m
al
s
a
m
p
le
s
ize
o
f
t
u
p
les
t
o
b
e
u
s
ed
d
u
r
in
g
s
ch
e
m
a
m
a
tch
i
n
g
p
r
o
ce
s
s
.
T
h
e
p
r
o
ce
s
s
o
f
s
a
m
p
le
s
ize
s
elec
tio
n
is
p
er
f
o
r
m
ed
b
y
g
en
era
tin
g
th
e
o
p
tima
l
s
a
mp
le
s
iz
e
p
h
ase
o
f
in
s
tan
ce
-
b
ased
s
c
h
e
m
a
m
atc
h
in
g
.
I
n
t
h
is
a
n
al
y
s
i
s
,
w
e
att
e
m
p
t
to
s
t
u
d
y
th
e
i
m
p
ac
t
o
f
t
h
e
s
a
m
p
le
s
ize
o
f
t
h
e
t
u
p
les
o
n
t
h
e
q
u
alit
y
o
f
t
h
e
m
atc
h
i
n
g
r
es
u
lt
in
ter
m
s
o
f
p
r
ec
is
io
n
(
P
)
,
r
ec
all
(
R
)
,
an
d
F
-
m
ea
s
u
r
e
(
F
)
f
o
r
b
o
th
s
tr
ateg
ie
s
.
T
h
e
s
a
m
p
le
s
ize
is
a
m
o
n
g
t
h
e
i
m
p
o
r
ta
n
t
p
ar
a
m
eter
s
th
at
i
n
f
l
u
e
n
ce
th
e
q
u
alit
y
an
d
t
h
e
p
er
f
o
r
m
an
ce
o
f
th
e
m
atc
h
i
n
g
p
r
o
ce
s
s
[
3
,
8
,
2
4
]
.
T
h
er
ef
o
r
e,
d
is
co
v
er
in
g
t
h
e
b
est
s
a
m
p
le
s
ize
o
f
in
s
ta
n
ce
s
is
ex
tr
e
m
el
y
n
ee
d
ed
in
o
r
d
er
to
m
ea
s
u
r
e
th
e
ac
cu
r
ac
y
o
f
th
e
co
n
s
id
er
ed
tech
n
iq
u
es
.
W
e
s
tar
t
f
r
o
m
1
0
%,
a
n
d
t
h
e
s
a
m
p
le
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iz
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g
r
ad
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all
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ed
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th
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b
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eq
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m
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t
s
u
p
to
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0
%
o
f
th
e
ac
tu
al
tab
le
s
ize.
T
h
is
i
n
cr
e
m
en
t
h
elp
s
to
d
i
s
co
v
er
w
h
et
h
er
th
e
ap
p
r
o
ac
h
es
t
h
at
h
a
v
e
b
ee
n
co
n
s
id
er
ed
r
eq
u
ir
e
a
lar
g
e
n
u
m
b
er
o
f
i
n
s
tan
ce
s
i
n
o
r
d
er
to
ac
h
iev
e
an
ac
c
u
r
ate
m
atc
h
b
et
w
ee
n
s
c
h
e
m
a
s
.
Fro
m
t
h
is
an
al
y
s
is
,
it
h
a
s
b
ee
n
ex
p
lo
r
ed
t
h
at
i
n
cr
ea
s
i
n
g
th
e
s
a
m
p
le
s
ize
lead
s
to
a
b
etter
r
esu
lt
o
f
P
r
ec
is
io
n
(
P
)
,
R
ec
all
(
R
)
,
a
n
d
F
-
m
ea
s
u
r
e
(
F
)
f
o
r
b
o
th
ap
p
r
o
ac
h
es.
T
ab
le
1
d
em
o
n
s
tr
ate
s
th
e
s
a
m
p
le
s
ize
co
n
s
id
er
ed
in
ea
ch
ex
p
er
i
m
en
t.
A
l
l
th
ese
e
x
p
er
i
m
e
n
t
s
u
s
ed
th
e
s
a
m
e
d
ata
s
et
a
n
d
en
d
ed
u
p
w
h
e
n
s
a
m
p
le
s
ize
r
ea
ch
ed
5
0
%.
E
ac
h
ex
p
er
i
m
en
t
h
a
s
b
ee
n
ex
ec
u
ted
f
i
v
e
ti
m
es
m
ea
s
u
r
in
g
t
h
e
P
,
R
,
an
d
F
an
d
av
er
ag
ed
th
ese
r
es
u
lt
s
.
T
ab
le
1
.
Sam
p
le
s
ize
f
o
r
ea
ch
ex
p
er
i
m
e
n
t
Ex
p
e
r
i
me
n
t
S
i
z
e
o
f
S
a
mp
l
e
s
Ex
p
e
r
i
me
n
t
1
-
1
1
0
%
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p
e
r
i
me
n
t
1
-
2
2
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p
e
r
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me
n
t
1
-
3
3
0
%
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x
p
e
r
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me
n
t
1
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4
4
0
%
Ex
p
e
r
i
me
n
t
1
-
5
5
0
%
4
.
1
.
1
Resul
t
o
f
E
x
peri
m
ent
1
T
h
is
s
u
b
-
s
ec
tio
n
p
r
esen
ts
th
e
d
etail
r
esu
lts
o
f
An
al
y
s
is
1
.
I
n
th
is
an
al
y
s
is
,
v
ar
io
u
s
ex
p
er
i
m
en
ts
h
a
v
e
b
ee
n
co
n
d
u
cted
o
n
t
w
o
r
ea
l
-
wo
r
ld
d
ata
s
ets
(
i)
R
esta
u
r
an
t
d
ata
s
et
a
n
d
(
ii)
C
en
s
u
s
d
ata
s
e
t,
an
d
o
n
e
s
y
n
th
et
ic
d
ata
s
et
(
i)
Un
iv
er
s
it
y
d
ata
s
et
to
id
en
tify
t
h
e
o
p
ti
m
al
s
a
m
p
le
s
ize
f
o
r
th
e
b
est
m
atc
h
i
n
g
r
es
u
lt.
4
.
1
.
1
.
1
Resul
t
o
f
E
x
peri
m
ent
1
Rela
t
ed
t
o
Rest
a
ura
nt
Da
t
a
s
et
I
n
th
is
a
n
al
y
s
is
,
a
r
ea
l
w
o
r
ld
d
ata
s
et
r
elate
d
to
R
estau
r
an
t
d
o
m
a
i
n
is
u
s
ed
to
d
eter
m
i
n
e
th
e
o
p
ti
m
al
s
a
m
p
le
s
ize
to
b
e
u
s
ed
in
b
o
t
h
ap
p
r
o
ac
h
es
(
R
eg
u
lar
ex
p
r
es
s
io
n
an
d
Go
o
g
le
s
i
m
ilar
it
y
)
.
R
esta
u
r
an
t
d
ata
s
et
co
n
s
is
ts
o
f
a
lis
t
o
f
r
esta
u
r
an
t
s
in
t
w
o
p
o
p
u
lar
w
eb
s
i
tes,
n
a
m
el
y
:
Z
a
g
at
a
n
d
Feo
d
o
r
.
T
h
e
d
ata
s
et
co
m
p
r
is
es
o
f
f
i
v
e
attr
ib
u
te
s
co
n
tai
n
i
n
s
ta
n
ce
s
r
ep
r
esen
ti
n
g
t
w
o
d
i
f
f
er
en
t
d
ata
t
y
p
e
s
a
lp
h
a
b
etic
an
d
s
p
ec
ia
l
ch
a
r
a
cters
(
mixe
d
)
.
Selecti
n
g
th
e
o
p
ti
m
al
s
a
m
p
le
s
ize
h
a
s
a
s
ig
n
i
f
ica
n
t
i
m
p
ac
t
o
n
r
ed
u
c
in
g
t
h
e
n
u
m
b
er
o
f
co
m
p
ar
is
o
n
s
b
et
w
ee
n
i
n
s
ta
n
ce
s
,
w
h
ic
h
f
u
r
t
h
er
r
ed
u
ce
th
e
p
r
o
ce
s
s
in
g
ti
m
e
o
f
th
e
m
atc
h
i
n
g
p
r
o
ce
s
s
.
Fi
g
u
r
e
2
(
a)
an
d
2
(
b
)
d
em
o
n
s
tr
ate
th
e
r
es
u
lts
o
f
P
r
e
cisi
o
n
(
P
)
,
R
ec
all
(
R
)
a
n
d
F
-
m
ea
s
u
r
e
(
F
)
f
o
r
t
h
e
e
x
p
er
i
m
e
n
ts
o
f
a
n
al
y
s
i
s
1
f
o
r
b
o
th
m
e
th
o
d
s
R
e
g
u
lar
ex
p
r
ess
io
n
an
d
Go
o
g
le
s
i
m
ilar
it
y
r
es
p
ec
tiv
el
y
.
I
t
is
v
er
y
clea
r
th
a
t
th
e
ac
cu
r
ac
y
o
f
t
h
e
m
atc
h
in
g
r
e
s
u
l
t
u
s
i
n
g
r
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u
lar
ex
p
r
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io
n
s
tr
ate
g
y
i
n
cr
ea
s
es
w
h
e
n
t
h
e
s
a
m
p
le
s
ize
i
n
cr
ea
s
e
as
s
h
o
w
n
i
n
Fi
g
u
r
e
2
.
No
tice
th
at
w
h
e
n
th
e
s
a
m
p
l
e
s
ize
is
5
0
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th
e
p
er
ce
n
tag
es
ar
e
6
0
%
an
d
8
1
%
f
o
r
p
r
ec
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io
n
(
P
)
an
d
r
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all
(
R
)
r
esp
ec
tiv
el
y
.
Ho
w
e
v
er
,
in
Fi
g
u
r
e
2
(
b
)
f
o
r
Go
o
g
le
s
i
m
ilar
it
y
tech
n
iq
u
e,
th
e
p
er
ce
n
ta
g
es
o
f
p
r
ec
is
io
n
(
P
)
an
d
r
ec
all
(
R
)
h
as in
cr
ea
s
ed
u
p
to
8
2
% a
n
d
7
7
% r
esp
ec
tiv
el
y
.
0%
20%
40%
60%
80%
100%
10%
20%
30%
40%
50%
P
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iz
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P
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P
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R
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c
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R
)
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20%
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60%
80%
100%
10%
20%
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P
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c
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P
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R
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c
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(
R
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(
a)
R
eg
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E
x
p
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b
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Go
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Si
m
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ig
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.
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m
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1273
4
.
1
.
1
.
2
Resul
t
o
f
E
x
peri
m
ent
1
Rela
t
ed
t
o
Censu
s
Da
t
a
s
et
T
h
e
C
en
s
u
s
r
ea
l
d
ata
s
et
co
n
t
ain
s
w
ei
g
h
ted
ce
n
s
u
s
d
ata
ex
t
r
ac
ted
b
y
B
ar
r
y
B
ec
k
er
i
n
1
9
9
4
f
r
o
m
th
e
C
en
s
u
s
d
atab
ase,
to
d
eter
m
i
n
e
t
h
e
o
p
ti
m
al
s
a
m
p
le
s
ize
th
at
w
o
u
ld
r
es
u
lt
i
n
r
ed
u
ci
n
g
th
e
n
u
m
b
er
o
f
co
m
p
ar
is
o
n
s
b
et
w
ee
n
i
n
s
tan
ce
s
to
id
en
ti
f
y
t
h
e
i
n
s
ta
n
ce
s
i
m
i
lar
it
y
,
w
h
ic
h
f
u
r
t
h
er
r
ed
u
ce
s
t
h
e
p
r
o
ce
s
s
i
n
g
ti
m
e
o
f
th
e
m
atch
in
g
p
r
o
ce
s
s
.
T
h
e
in
s
ta
n
ce
s
et
s
o
f
t
h
is
d
ata
s
et
i
n
v
o
l
v
e
th
e
t
h
r
ee
d
ata
t
y
p
es,
wh
ich
ar
e
a
n
u
m
er
ic,
alp
h
ab
etic
an
d
s
p
ec
ial
c
h
ar
ac
t
er
.
Fig
u
r
e
3
(
a)
an
d
3
(
b
)
d
e
m
o
n
s
tr
ate
th
e
r
es
u
lts
o
f
p
r
ec
is
io
n
(
P
)
,
r
ec
all
(
R
)
an
d
F
-
m
ea
s
u
r
e
(
F
)
f
o
r
th
is
a
n
al
y
s
i
s
o
n
C
en
s
u
s
d
ata
s
et
u
s
i
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g
R
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g
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lar
e
x
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Go
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le
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ilar
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ch
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atch
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tech
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a
m
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ip
b
et
w
ee
n
t
h
e
in
s
ta
n
ce
s
e
ts
.
Nev
er
t
h
eles
s
,
it
is
i
n
ap
p
r
o
p
r
iate
to
b
e
u
tili
ze
d
f
o
r
s
c
h
e
m
a
attr
ib
u
tes
co
n
tai
n
m
i
x
a
n
d
n
u
m
er
ic
d
ata.
W
e
als
o
co
n
clu
d
e
th
at
r
eg
u
lar
e
x
p
r
ess
io
n
r
elies
m
ain
l
y
o
n
a
s
a
m
p
le
s
ize
o
f
i
n
s
ta
n
ce
s
to
ac
h
ie
v
e
h
ig
h
ac
c
u
r
ac
y
.
T
h
e
ac
cu
r
ac
y
o
f
t
h
e
m
atc
h
i
n
g
r
es
u
lt
i
n
cr
ea
s
ed
w
h
e
n
th
e
s
a
m
p
le
s
ize
i
s
lar
g
e.
RE
F
E
R
E
NC
E
S
[1
]
L
e
n
z
e
rin
i,
M
.
,
“
Da
ta
in
teg
ra
ti
o
n
:
A
th
e
o
re
ti
c
a
l
p
e
rsp
e
c
ti
v
e
”
.
In
:
P
ro
c
e
e
d
in
g
s
o
f
th
e
2
1
st
ACM
S
IG
M
OD
-
S
IGACT
-
S
IGART
S
y
mp
o
si
u
m o
n
Pri
n
c
ip
le
s o
f
Da
t
a
b
a
se
S
y
ste
ms
.
2
–
6
-
Ju
n
e
-
2
0
0
2
,
M
a
d
iso
n
,
W
isc
o
n
si
n
,
US
A
,
2
3
3
-
2
4
6
.
[2
]
Do
,
H.
,
“
S
c
h
e
m
a
M
a
tch
in
g
a
n
d
M
a
p
p
i
n
g
-
b
a
se
d
Da
ta In
teg
ra
ti
o
n
:
A
rc
h
it
e
c
tu
re
,
A
p
p
ro
a
c
h
e
s,
a
n
d
Ev
a
lu
a
ti
o
n
”
.
V
DM
V
e
rlag
S
a
a
rb
rü
c
k
e
n
,
G
e
r
m
a
n
y
,
2
0
0
7
.
[3
]
Os
a
m
a
,
A
.
M
e
h
d
i
.
,
“
A
N
e
w
a
p
p
ro
a
c
h
f
o
r
In
sta
n
c
e
b
a
se
d
-
sc
h
e
m
a
m
a
t
c
h
in
g
”
.
Un
p
u
b
li
sh
e
d
M
a
ste
r
Diss
e
rtatio
n
.
Un
iv
e
rsit
i
P
u
tra M
a
lay
sia
,
Ku
a
la L
u
m
p
u
r,
M
a
lay
sia
,
2
0
1
4
.
[4
]
Be
rn
ste
in
,
P
.
A
.
,
M
a
d
h
a
v
a
n
,
J.,
Ra
h
m
,
E.
“
G
e
n
e
ric
sc
h
e
m
a
m
a
t
c
h
in
g
,
ten
y
e
a
rs
late
r
”
.
In
:
Pro
c
e
e
d
in
g
s
o
f
t
h
e
3
7
th
In
ter
n
a
t
io
n
a
l
Co
n
fer
e
n
c
e
o
n
Ver
y
L
a
rg
e
Da
ta
Ba
se
s
,
A
u
g
u
st
2
9
th
-
S
e
p
tem
b
e
r
3
rd
2
0
1
1
,
S
e
a
tt
le,
W
a
s
h
in
g
to
n
,
USA
,
695
-
7
0
1
.
[5
]
T
ian
,
A
.
,
Ke
jri
w
a
l,
M
.
,
M
iran
k
e
r,
D.
P
.
,
“
S
c
h
e
m
a
m
a
tch
in
g
o
v
e
r
re
latio
n
s,
a
tt
r
ib
u
tes
,
a
n
d
d
a
t
a
v
a
lu
e
s
”
.
In
:
Pro
c
e
e
d
in
g
s
o
f
th
e
2
6
th
I
n
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
S
c
ien
ti
fi
c
a
n
d
S
ta
ti
st
ica
l
Da
ta
b
a
se
M
a
n
a
g
e
me
n
t
.
3
0
-
Ju
n
e
–
2
Ju
ly
–
2
0
1
4
,
A
a
lb
o
rg
,
De
n
m
a
rk
.
[6
]
G
o
z
u
d
e
li
,
Y.,
Ka
ra
c
a
n
,
H.,
Yi
ld
iz
,
O.,
Ba
k
e
r,
M
.
,
M
in
n
e
t,
A
.
,
Ka
len
d
e
r,
M
.
,
A
k
c
a
y
o
l,
M
.
,
“
A
Ne
w
m
e
th
o
d
b
a
se
d
o
n
tree
si
m
p
li
f
ica
ti
o
n
a
n
d
sc
h
e
m
a
m
a
tch
in
g
f
o
r
a
u
to
m
a
ti
c
we
b
re
su
lt
e
x
trac
ti
o
n
a
n
d
m
a
tch
in
g
”
.
In
:
Pro
c
e
e
d
in
g
s
o
f
th
e
In
ter
n
a
t
io
n
a
l
M
u
lt
i
Co
n
fer
e
n
c
e
o
f
En
g
i
n
e
e
rs
a
n
d
Co
mp
u
ter
S
c
ien
ti
sts
.
1
8
-
20
-
M
a
rc
h
–
2
0
1
5
,
Ho
n
g
Ko
n
g
,
Ch
in
a
,
1
-
5.
[7
]
Ja
in
,
S
.
,
T
a
n
w
a
n
i,
S
.
,
“
S
c
h
e
m
a
m
a
tch
in
g
tec
h
n
iq
u
e
f
o
r
a
h
e
tero
g
e
n
e
o
u
s
w
e
b
d
a
tab
a
se
”
.
In
:
Pro
c
e
e
d
in
g
s
o
f
th
e
4
th
In
ter
n
a
t
io
n
a
l
Co
n
fer
e
n
c
e
o
n
t
h
e
Relia
b
i
li
ty,
In
f
o
c
o
m
T
e
c
h
n
o
l
o
g
ies
a
n
d
O
p
ti
miza
ti
o
n
(
ICRIT
O)
(
T
re
n
d
s
a
n
d
F
u
tu
re
Dire
c
ti
o
n
s)
.
2
–
4
-
S
e
p
tem
b
e
r
-
2
0
1
5
,
N
o
id
a
,
In
d
ia,
1
-
6.
[8
]
Os
a
m
a
,
A
.
M
.
,
Ha
m
id
a
h
,
I
.,
Li
ll
y
S
.
A
.
,
“
A
n
A
p
p
ro
a
c
h
f
o
r
In
sta
n
c
e
Ba
se
d
S
c
h
e
m
a
M
a
tch
in
g
w
it
h
G
o
o
g
le
S
im
il
a
rit
y
a
n
d
Re
g
u
lar E
x
p
re
ss
io
n
”
.
T
h
e
I
n
t.
Ara
b
J
.
o
f
In
fo
.
T
e
c
h
.
,
2
0
1
7
.
No
.
5
.
[9
]
M
u
n
ir
,
S
.
,
Kh
a
n
,
F
.
,
Riaz
,
M
.
A
.
“
A
n
in
sta
n
c
e
-
b
a
se
d
sc
h
e
m
a
m
a
tch
in
g
b
e
tw
e
e
n
o
p
a
q
u
e
d
a
tab
a
se
sc
h
e
m
a
s
”
.
I
n:
Pro
c
e
e
d
in
g
s
o
f
t
h
e
4
th
I
n
ter
n
a
t
io
n
a
l
C
o
n
fer
e
n
c
e
o
n
En
g
i
n
e
e
rin
g
T
e
c
h
n
o
l
o
g
y
a
n
d
T
e
c
h
n
o
p
re
n
e
u
sh
ip
(
ICE2
T
)
.
2
7
-
29
-
A
u
g
u
st
-
2
0
1
4
,
K
u
a
la L
u
m
p
u
r,
M
a
lay
sia
,
1
7
7
-
1
8
2
.
[1
0
]
De
Ca
rv
a
lh
o
,
M
.
G
.
,
L
a
e
n
d
e
r,
A
.
H.,
G
o
n
ç
A
lv
e
s,
M
.
A
.
,
Da
S
il
v
a
,
A
.
S
.
,
“
A
n
e
v
o
lu
ti
o
n
a
ry
a
p
p
ro
a
c
h
t
o
c
o
m
p
lex
sc
h
e
m
a
m
a
tch
in
g
”
.
J
.
o
f
In
f
o
.
S
y
s
,
2
0
1
3
,
3
8
(3
)
,
3
0
2
-
3
1
6
.
[1
1
]
Os
a
m
a
A
.
M
e
h
d
i,
Ha
m
id
a
h
,
I.
,
L
i
ll
y
S
.
A
.
,
“
In
sta
n
c
e
b
a
s
e
d
m
a
tch
in
g
u
sin
g
re
g
u
lar
e
x
p
re
ss
io
n
”
.
Pro
c
e
d
ia
Co
m.
S
c
i
.
,
2
0
1
2
,
1
0
,
6
8
8
-
6
9
5
.
[1
2
]
Zh
a
o
,
H.,
Ra
m
,
S
.
,
“
Co
m
b
in
in
g
s
c
h
e
m
a
a
n
d
in
sta
n
c
e
in
f
o
r
m
a
ti
o
n
fo
r
in
teg
ra
ti
n
g
h
e
tero
g
e
n
e
o
u
s
d
a
ta
so
u
rc
e
s
”
.
J
.
o
f
Da
ta
&
Kn
o
w.
En
g
.
,
2
0
0
7
,
6
1
(
2
),
2
8
1
-
3
0
3
.
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