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lt
co
m
p
u
tatio
n
f
u
n
ctio
n
s
s
u
c
h
as
m
a
tch
i
n
g
,
r
an
k
i
n
g
,
o
r
ag
g
r
eg
ati
n
g
r
es
u
lt
s
b
ased
o
n
f
u
zz
y
cr
iter
ia
[
5
]
.
A
cc
o
m
p
l
is
h
i
n
g
tas
k
s
b
y
cr
o
wd
w
o
r
k
er
s
m
i
g
h
t
in
c
u
r
a
m
o
n
e
tar
y
co
s
t,
laten
c
y
,
a
n
d
th
e
ac
cu
r
ac
y
o
f
t
h
e
q
u
er
ies
s
u
b
j
ec
t to
th
e
w
o
r
k
er
’
s
q
u
alit
y
[
2
]
.
I
n
m
o
s
t
o
f
t
h
e
co
n
te
m
p
o
r
ar
y
d
atab
ase
ap
p
licatio
n
s
,
th
e
i
s
s
u
e
o
f
m
is
s
in
g
d
ata
b
ec
o
m
e
a
f
r
eq
u
en
t
p
h
en
o
m
e
n
o
n
,
e
s
p
ec
iall
y
w
h
en
d
atab
ases
ar
e
g
e
n
er
ated
au
to
m
atica
ll
y
u
s
i
n
g
v
ar
io
u
s
in
f
o
r
m
atio
n
ex
tr
ac
tio
n
o
r
in
f
o
r
m
atio
n
i
n
teg
r
atio
n
ap
p
r
o
ac
h
es.
T
h
er
e
ar
e
m
a
n
y
r
ea
s
o
n
s
th
a
t
m
ak
e
th
e
d
atab
ase
i
m
p
r
ec
is
e,
ex
e
m
p
li
f
y
t
h
e
d
ata
in
teg
r
at
io
n
f
r
o
m
d
i
f
f
er
e
n
t
h
u
g
e
d
atab
ases
.
F
u
r
t
h
er
m
o
r
e,
u
s
er
s
m
ig
h
t
p
r
o
v
id
e
i
n
co
m
p
lete
in
p
u
t
b
y
ig
n
o
r
i
n
g
s
o
m
e
d
ata
w
h
et
h
er
in
ten
tio
n
all
y
o
r
u
n
i
n
ten
tio
n
all
y
w
h
e
n
w
o
r
k
i
n
g
o
n
t
h
e
d
atab
ase.
T
h
ese
ac
tiv
it
ie
s
in
f
lu
e
n
ce
n
eg
at
iv
el
y
o
n
th
e
d
atab
ase
co
n
ten
t
s
an
d
d
eter
io
r
ate
th
e
q
u
alit
y
o
f
th
e
d
atab
a
s
e
co
n
te
n
ts
.
T
h
ese
r
ea
s
o
n
s
i
m
p
ac
t o
n
th
e
co
m
p
let
en
es
s
an
d
th
e
co
r
r
ec
tn
e
s
s
o
f
t
h
e
q
u
er
y
r
esu
l
t [
3
]
,
[
8
]
-
[
1
0
]
.
I
n
th
i
s
r
eg
ar
d
,
a
s
k
y
lin
e
q
u
er
ies
h
a
v
e
g
ai
n
ed
co
n
s
id
er
ab
le
atten
tio
n
f
o
r
ass
i
s
ti
n
g
i
n
m
u
lti
-
cr
iter
ia
d
ec
is
io
n
m
a
k
i
n
g
an
d
d
ec
is
io
n
s
u
p
p
o
r
t
ap
p
licatio
n
s
[
4
]
,
[
23
]
,
[
24
]
,
[
27
]
,
[
2
8
]
.
Sk
y
li
n
es
ar
e
th
o
s
e
t
u
p
les
w
h
ic
h
ar
e
th
e
s
u
p
er
io
r
a
m
o
n
g
all
tu
p
les
in
t
h
e
d
atab
ase.
T
h
e
m
ai
n
i
s
s
u
e
co
n
ce
r
n
w
h
en
w
o
r
k
in
g
with
s
k
y
li
n
e
q
u
er
ies
is
r
ed
u
ci
n
g
th
e
s
ea
r
ch
s
p
ac
e
a
s
s
m
all
a
s
p
o
s
s
ib
le
a
n
d
a
v
o
id
th
e
u
n
n
ec
es
s
ar
y
e
x
h
au
s
ti
v
e
p
ai
r
w
i
s
e
co
m
p
ar
is
o
n
s
to
d
eter
m
i
n
i
n
g
t
h
e
s
k
y
l
in
e
s
.
W
h
en
o
p
er
atin
g
o
n
h
u
g
e
d
ata
b
ase
s
u
c
h
as
cr
o
w
d
-
s
o
u
r
ci
n
g
d
atab
ase
th
e
ad
v
an
ce
d
q
u
er
y
o
p
er
ato
r
s
,
s
u
ch
as
s
k
y
li
n
e
q
u
er
ies
h
elp
to
f
ilter
u
n
n
ec
ess
ar
y
i
n
f
o
r
m
atio
n
e
f
f
icien
tl
y
b
ef
o
r
e
co
n
n
ec
tin
g
w
it
h
t
h
e
cr
o
w
d
.
Nev
er
th
e
less
,
in
co
m
p
lete
d
ata
h
as
a
n
e
g
ati
v
e
ef
f
ec
t
o
n
th
e
p
er
f
o
r
m
a
n
ce
o
f
th
e
s
k
y
l
in
e
q
u
er
y
p
r
o
ce
s
s
w
h
ic
h
m
ig
h
t
r
ai
s
e
s
o
m
e
is
s
u
es
i
n
cl
u
d
in
g
c
y
clic
d
o
m
in
a
n
ce
a
n
d
lo
s
i
n
g
t
h
e
tr
a
n
s
iti
v
it
y
p
r
o
p
er
ty
o
f
s
k
y
li
n
e
tech
n
iq
u
e
w
h
ic
h
is
h
e
ld
o
n
all
ex
is
ti
n
g
s
k
y
lin
e
tec
h
n
iq
u
e
s
.
B
esid
es,
in
ac
c
u
r
ate
d
ata
m
i
g
h
t
al
s
o
lead
d
ec
lin
e
th
e
q
u
a
lit
y
o
f
th
e
r
es
u
l
ts
p
r
o
d
u
ce
d
f
r
o
m
t
h
e
cr
o
w
d
-
s
o
u
r
ce
d
d
atab
ase
[
4
]
.
Ou
r
co
n
tr
ib
u
tio
n
in
t
h
i
s
p
ap
er
ca
n
b
e
s
u
m
m
ar
ized
as f
o
llo
w
s
:
a.
T
o
p
r
o
p
o
s
e
an
ap
p
r
o
ac
h
th
at
is
ab
le
to
r
etu
r
n
s
k
y
li
n
es
f
r
o
m
p
ar
tiall
y
co
m
p
lete
cr
o
w
d
-
s
o
u
r
cin
g
d
atab
as
e
w
it
h
t
h
e
i
n
te
n
tio
n
o
f
r
ed
u
ci
n
g
th
e
n
u
m
b
er
o
f
p
air
w
is
e
co
m
p
ar
is
o
n
s
b
y
e
li
m
in
a
tin
g
t
h
e
u
n
n
ec
ess
ar
y
t
u
p
le
s
b
ef
o
r
e
ap
p
ly
i
n
g
t
h
e
s
k
y
li
n
e
te
ch
n
iq
u
e.
b.
T
o
p
r
o
p
o
s
e
an
ap
p
r
o
ac
h
t
h
at
esti
m
ates
th
e
m
is
s
in
g
v
al
u
es
o
f
s
k
y
li
n
es
u
til
izin
g
cr
o
w
d
-
s
o
u
r
cin
g
d
atab
ase.
T
h
e
ap
p
r
o
ac
h
h
an
d
les
th
i
s
is
s
u
e
tak
in
g
in
to
ac
co
u
n
t
th
e
r
elativ
e
er
r
o
r
p
r
o
d
u
ce
d
w
h
en
g
en
er
ati
n
g
th
e
esti
m
ated
v
al
u
es,
th
e
m
o
n
etar
y
co
s
t
o
f
es
ti
m
atin
g
th
e
v
al
u
es,
r
ed
u
ce
t
h
e
ti
m
e
late
n
c
y
,
a
n
d
en
s
u
r
e
h
i
g
h
q
u
alit
y
o
f
th
e
s
k
y
li
n
e
r
es
u
lts
.
c.
T
o
p
r
o
p
o
s
e
a
m
o
d
el
o
f
s
k
y
li
n
e
q
u
er
ies f
o
r
in
co
m
p
le
te
cr
o
w
d
-
s
o
u
r
cin
g
d
atab
ase.
T
h
e
r
est o
f
th
i
s
p
ap
er
is
o
r
g
an
i
ze
d
as
f
o
llo
w
s
.
So
m
e
n
ec
es
s
ar
y
b
asic
d
ef
i
n
itio
n
s
an
d
n
o
tatio
n
s
,
w
h
ic
h
ar
e
u
s
ed
t
h
r
o
u
g
h
o
u
t
th
e
p
ap
e
r
,
ar
e
r
ep
o
r
ted
in
s
ec
tio
n
2
.
T
h
e
p
r
ev
io
u
s
w
o
r
k
s
r
elate
d
t
o
th
is
r
esear
c
h
ar
e
p
r
es
en
ted
an
d
ex
a
m
i
n
ed
in
s
e
ctio
n
3
.
Mo
r
e
o
v
er
,
th
e
d
etail
s
tep
s
o
f
th
e
p
r
o
p
o
s
ed
tech
n
iq
u
e
ar
e
ex
p
lain
ed
in
s
ec
tio
n
4
.
Sectio
n
5
d
r
a
w
s
th
e
co
n
clu
s
io
n
o
f
t
h
i
s
p
ap
er
.
2.
P
RE
L
I
M
I
NARIE
S
T
h
is
s
ec
tio
n
g
i
v
es
s
o
m
e
n
ec
es
s
ar
y
d
ef
i
n
itio
n
s
an
d
an
n
o
tatio
n
s
t
h
at
ar
e
r
elate
d
to
s
k
y
li
n
es
q
u
er
ies
i
n
p
ar
tiall
y
co
m
p
lete
cr
o
w
d
-
s
o
u
r
cin
g
d
atab
ase
s
.
T
h
ese
d
ef
i
n
i
tio
n
s
a
n
d
n
o
tatio
n
s
ar
e
i
m
p
o
r
tan
t
to
e
x
p
lain
t
h
e
d
etails
o
f
o
u
r
p
r
o
p
o
s
ed
m
o
d
el.
W
e
also
d
escr
ib
e
th
e
co
n
ce
p
ts
r
elate
d
to
cr
o
w
d
-
s
o
u
r
ci
n
g
d
atab
ase,
w
h
ile
co
n
ce
n
tr
ati
n
g
o
n
ch
alle
n
g
e
s
o
f
in
co
m
p
lete
d
ata
o
n
d
atab
ase.
T
h
is
in
cl
u
d
e
ex
p
lai
n
in
g
t
h
e
r
ea
s
o
n
s
th
a
t
m
a
k
e
th
e
d
atab
ase
to
b
e
in
co
m
p
lete
an
d
th
e
m
eth
o
d
s
u
s
ed
to
m
a
n
i
p
u
late
th
e
m
i
s
s
i
n
g
d
ata.
2
.
1
.
Cr
o
w
d
-
So
urcing
Da
t
a
ba
s
e
C
r
o
w
d
-
s
o
u
r
ci
n
g
d
atab
ase
is
a
h
y
b
r
id
d
atab
ase
s
y
s
te
m
th
a
t
au
to
m
a
ticall
y
u
s
es
cr
o
w
d
-
s
o
u
r
cin
g
to
in
te
g
r
ate
h
u
m
an
i
n
p
u
t
f
o
r
p
r
o
ce
s
s
i
n
g
q
u
er
ies
t
h
at
a
n
o
r
m
al
d
atab
ase
s
y
s
te
m
ca
n
n
o
t
an
s
w
er
[
5
]
.
T
h
e
cr
o
w
d
-
s
o
u
r
cin
g
s
y
s
te
m
co
m
p
r
i
s
es
o
f
th
r
ee
co
m
p
o
n
e
n
t
s
w
o
r
k
to
p
r
o
v
id
e
s
er
v
ices
to
t
h
e
u
s
er
s
an
d
en
s
u
r
e
r
etu
r
n
i
n
g
a
h
ig
h
-
q
u
al
i
t
y
r
es
u
lt
to
t
h
e
u
s
er
.
T
h
e
co
m
p
o
n
en
t
s
ar
e:
(
i)
r
eq
u
ester
,
w
h
o
s
u
b
m
it
t
h
e
q
u
er
ie
s
to
th
e
cr
o
w
d
an
d
w
ait
s
f
o
r
t
h
e
a
n
s
w
er
s
,
(
ii)
p
lat
f
o
r
m
(
q
u
er
y
e
x
ec
u
tio
n
e
n
g
i
n
e)
,
w
h
ic
h
i
s
r
esp
o
n
s
ib
le
to
m
a
n
a
g
e
t
h
e
g
i
v
en
q
u
er
y
b
y
th
e
u
s
er
a
n
d
r
etr
iev
e
b
ac
k
th
e
an
s
w
er
s
g
e
n
er
a
ted
b
y
th
e
w
o
r
k
er
s
,
(
ii
i)
w
o
r
k
er
s
,
w
h
o
i
s
r
esp
o
n
s
ib
le
to
r
u
n
th
e
as
s
ig
n
ed
tas
k
s
a
n
d
p
r
ep
ar
i
n
g
a
n
d
d
eliv
er
ed
th
e
r
es
u
lts
to
th
e
tar
g
et
u
s
er
[
5
]
,
[
2
5
]
.
C
r
o
w
d
-
s
o
u
r
ci
n
g
d
atab
ase
co
n
s
is
t
s
o
f
a
co
llectio
n
o
f
e
n
o
r
m
o
u
s
h
e
ter
o
g
en
eo
u
s
d
atab
ase
s
th
a
t
ar
e
s
to
r
ed
o
n
d
i
f
f
er
e
n
t
p
lace
s
o
n
th
e
i
n
ter
n
et
a
n
d
ca
n
b
e
ac
ce
s
s
ed
th
r
o
u
g
h
t
h
e
cr
o
w
d
.
C
r
o
w
d
-
s
o
u
r
ce
d
d
atab
ases
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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:
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5
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2
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I
n
d
o
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esia
n
J
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lec
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n
g
&
C
o
m
p
Sci,
Vo
l.
10
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No
.
2
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Ma
y
2
0
1
8
:
7
9
8
–
8
0
6
800
ex
ten
d
tr
ad
itio
n
al
d
atab
ases
t
o
s
u
p
p
o
r
t
m
o
r
e
t
y
p
es
o
f
q
u
e
r
ies
b
y
m
ea
n
s
o
f
t
h
e
p
o
w
er
o
f
p
eo
p
le,
s
u
ch
a
s
C
r
o
w
d
DB
[
5
]
,
Qu
r
k
[
6
]
,
Dec
o
[
7
]
,
an
d
s
o
o
n
.
T
h
ese
cr
o
wd
-
s
o
u
r
ce
d
d
atab
ases
ar
e
as
s
o
ciate
d
w
it
h
ce
r
tai
n
cr
o
w
d
-
s
o
u
r
cin
g
m
ar
k
etp
lace
s
,
s
u
c
h
a
s
AM
T
(
Am
az
o
n
Me
c
h
an
ica
l
T
u
r
k
)
[
5
]
,
C
r
o
w
d
Flo
wer
[
2
]
an
d
s
o
o
n
,
to
attr
ac
t
cr
o
w
d
s
to
w
o
r
k
f
o
r
th
e
m
.
Fu
r
t
h
er
m
o
r
e,
cr
o
w
d
-
s
o
u
r
ci
n
g
d
atab
ase
lev
er
ag
e
s
m
a
n
y
a
s
p
ec
ts
o
f
tr
ad
itio
n
al
d
ata
b
ase
s
y
s
te
m
s
,
h
o
w
e
v
er
,
t
h
er
e
ar
e
also
s
o
m
e
d
i
f
f
er
en
ce
s
b
et
w
ee
n
t
h
e
m
w
h
ich
ar
e
e
x
p
lain
ed
as
f
o
llo
w
s
.
T
r
a
d
itio
n
al
d
atab
ases
ar
e
co
n
s
id
er
ed
as
a
clo
s
ed
-
w
o
r
ld
f
o
r
q
u
er
y
p
r
o
ce
s
s
i
n
g
.
I
n
f
o
r
m
atio
n
t
h
at
i
s
n
o
t
i
n
t
h
e
d
atab
ase
is
co
n
s
id
er
ed
to
b
e
f
alse
o
r
n
o
n
-
e
x
i
s
te
n
t.
B
esid
es
,
tr
ad
itio
n
al
d
atab
ases
ar
e
ex
t
r
e
m
el
y
liter
al.
Fo
r
in
s
ta
n
ce
,
u
s
er
s
e
x
p
ec
t th
a
t t
h
e
d
ata
h
as
b
ee
n
p
r
o
p
er
l
y
clea
n
e
d
an
d
v
al
id
ated
b
ef
o
r
e
in
s
er
ti
n
g
i
n
to
d
atab
ase
a
n
d
d
o
n
o
t n
eg
ati
v
el
y
to
ler
ate
in
co
n
s
i
s
te
n
cies i
n
d
ata
o
r
q
u
er
ies [
5
]
.
T
h
er
e
ar
e
th
r
ee
m
ai
n
cr
itical
f
ac
to
r
s
t
h
at
in
f
l
u
e
n
ce
th
e
d
at
a
p
r
o
ce
s
s
in
g
i
n
cr
o
w
d
-
s
o
u
r
ci
n
g
s
y
s
te
m
.
T
h
is
en
co
m
p
a
s
s
es,
m
o
n
etar
y
co
s
t,
ti
m
e
late
n
c
y
,
a
n
d
t
h
e
ac
cu
r
ac
y
o
f
t
h
e
r
es
u
lt
s
[
1
]
,
[
2
]
,
[
8
]
,
[
9
]
,
[
1
0
]
,
[
1
1
]
.
T
h
ese
f
ac
to
r
s
ar
e
f
u
r
t
h
er
elab
o
r
ated
b
elo
w
.
Mo
n
etar
y
C
o
s
t:
E
v
er
y
ta
s
k
g
iv
en
b
y
t
h
e
u
s
er
s
h
o
u
ld
b
e
p
aid
as
th
e
cr
o
w
d
is
n
o
t
f
r
ee
,
th
er
ef
o
r
e,
r
eq
u
ester
n
ee
d
s
to
k
n
o
w
i
n
ad
v
an
ce
t
h
e
e
s
ti
m
ated
co
s
t o
f
t
h
e
g
iv
e
n
ta
s
k
b
e
f
o
r
e
ask
i
n
g
t
h
e
q
u
esti
o
n
a
n
d
s
e
n
d
it
to
th
e
w
o
r
k
er
.
Hen
ce
,
if
t
h
e
n
u
m
b
er
o
f
ta
s
k
s
ar
e
v
er
y
lar
g
e,
t
h
en
cr
o
w
d
-
s
o
u
r
ci
n
g
ca
n
b
e
v
e
r
y
e
x
p
en
s
i
v
e
[
1
1
]
.
T
im
e
L
aten
c
y
:
T
h
is
f
ac
to
r
r
ep
r
esen
ts
t
h
e
r
u
n
n
i
n
g
ti
m
e
to
o
b
tain
an
s
w
er
s
f
r
o
m
th
e
cr
o
w
d
.
Fo
r
ea
ch
task
,
th
e
r
eq
u
e
s
ter
ca
n
s
et
th
e
ti
m
e
b
o
u
n
d
(
e.
g
.
,
6
0
s
ec
o
n
d
s
)
to
an
s
w
er
it,
an
d
th
e
w
o
r
k
er
m
u
s
t
an
s
w
er
it
w
it
h
i
n
t
h
e
s
tip
u
lated
ti
m
e
b
o
u
n
d
.
T
h
e
r
e
q
u
ester
ca
n
also
s
et
th
e
ex
p
ir
atio
n
ti
m
e
o
f
th
e
tas
k
s
if
n
ec
e
s
s
ar
y
,
i.e
.
,
th
e
m
a
x
i
m
u
m
ti
m
e
th
at
t
h
e
tas
k
s
w
il
l b
e
av
ailab
le
to
w
o
r
k
er
s
in
th
e
p
lat
f
o
r
m
(
e.
g
.
,
2
4
h
o
u
r
s
)
[
2
]
.
A
cc
u
r
ac
y
:
C
r
o
w
d
-
s
o
u
r
ci
n
g
r
elies
o
n
h
u
m
an
w
o
r
k
er
s
t
o
ac
h
i
ev
e
t
h
e
j
o
b
an
d
th
e
q
u
alit
y
o
f
th
e
r
es
u
lt
s
o
b
tain
ed
is
h
i
g
h
l
y
i
n
f
l
u
e
n
ce
d
b
y
th
e
w
o
r
k
er
q
u
alit
y
.
H
u
m
a
n
s
m
o
s
t
o
f
te
n
te
n
d
to
m
a
k
e
er
r
o
r
s
,
p
ar
ticu
lar
l
y
f
o
r
h
ea
v
y
ta
s
k
s
t
h
at
n
ee
d
lo
n
g
ti
m
e
an
d
h
i
g
h
atte
n
tio
n
.
T
h
is
,
in
t
u
r
n
,
lead
s
i
n
t
h
e
w
o
r
s
t
ca
s
e
to
p
r
o
v
id
e
in
ac
cu
r
ate
r
esu
lt
s
to
th
e
r
eq
u
ester
.
B
es
id
es,
s
o
m
e
m
alicio
u
s
w
o
r
k
er
s
a
r
e
in
ter
es
ted
to
o
b
tain
r
e
w
ar
d
s
a
n
d
i
n
ten
tio
n
all
y
s
u
b
m
it
r
an
d
o
m
an
s
w
er
s
to
all
q
u
esti
o
n
s
o
r
g
i
v
e
w
r
o
n
g
a
n
s
w
er
s
.
T
h
is
ca
n
s
ig
n
i
f
ica
n
tl
y
d
ec
lin
e
th
e
q
u
a
lit
y
o
f
th
e
r
es
u
lt
s
.
Mo
r
eo
v
er
,
s
o
m
et
i
m
es
th
e
w
o
r
k
er
m
is
u
n
d
er
s
ta
n
d
s
th
e
ta
s
k
as
s
ig
n
ed
w
h
ic
h
e
v
en
tu
a
ll
y
n
e
g
ati
v
el
y
i
m
p
ac
t
o
n
th
e
q
u
al
it
y
o
f
th
e
t
ask
ac
co
m
p
li
s
h
ed
.
L
a
s
tl
y
,
w
o
r
k
er
s
m
a
y
h
a
v
e
d
if
f
er
en
t
le
v
e
ls
o
f
ex
p
er
tis
e,
an
d
u
n
tr
ai
n
ed
w
o
r
k
er
m
a
y
b
e
in
ca
p
ab
le
o
f
ac
co
m
p
lis
h
i
n
g
ce
r
tain
co
m
p
lica
ted
tas
k
s
[
2
]
.
2
.
2
.
I
nco
m
plet
e
Da
t
a
T
h
e
d
atab
ase
w
it
h
in
co
m
p
lete
d
ata
o
r
m
i
s
s
i
n
g
v
al
u
e
(
also
k
n
o
w
n
a
s
n
u
ll
v
al
u
e)
ar
e
b
ec
o
m
i
n
g
v
er
y
co
m
m
o
n
i
n
r
ea
l
w
o
r
ld
ap
p
lic
atio
n
s
s
u
c
h
a
s
w
eb
d
atab
ase
[
2
6
]
.
Fo
r
ex
a
m
p
le,
a
ce
n
s
u
s
d
atab
ase
th
at
allo
w
s
n
u
l
l
v
a
lu
e
s
f
o
r
s
o
m
e
attr
ib
u
tes
.
A
s
u
r
v
e
y
d
atab
ase
w
h
er
e
a
n
s
w
er
s
to
o
n
e
q
u
es
tio
n
ca
u
s
e
o
t
h
er
q
u
est
io
n
s
to
b
e
s
k
ip
p
ed
.
A
ls
o
,
a
m
ed
ical
d
ata
b
ase
th
a
t
r
elate
s
h
u
m
a
n
b
o
d
y
an
al
y
s
t
(
a
s
u
b
s
ta
n
ce
t
h
at
ca
n
b
e
m
ea
s
u
r
ed
i
n
t
h
e
b
lo
o
d
o
r
u
r
in
e)
m
ea
s
u
r
e
m
e
n
t
s
to
a
n
u
m
b
er
o
f
d
is
ea
s
es,
o
r
p
atien
t
r
is
k
f
ac
to
r
s
to
a
s
p
ec
if
ic
d
is
ea
s
e.
T
h
e
d
ef
in
i
tio
n
o
f
i
n
co
m
p
lete
d
atab
ase
is
g
i
v
e
n
b
elo
w
[
1
4
]
.
I
nco
m
plet
e
Da
t
a
ba
s
e
:
g
i
v
e
n
a
d
atab
ase
D(
R
1
,
R
2
,
.
.
.
,
R
n
),
w
h
er
e
R
i
is
a
r
elatio
n
d
en
o
ted
b
y
R
i
(d
1
,
d
2
,
.
.
.
,
d
m
)
,
D
is
s
aid
to
b
e
in
co
mp
lete
if
an
d
o
n
l
y
i
f
it
co
n
tai
n
s
at
least
a
t
u
p
le
p
j
w
i
th
m
i
s
s
i
n
g
v
al
u
es
i
n
o
n
e
o
r
m
o
r
e
d
i
m
en
s
io
n
s
d
k
(
attr
ib
u
tes
)
; o
th
er
w
i
s
e,
it is
co
mp
lete
.
T
h
er
e
ar
e
m
an
y
r
ea
s
o
n
s
t
h
at
m
ak
e
th
e
d
atab
ase
to
b
e
i
m
p
er
f
ec
t
d
u
e
to
m
is
s
in
g
v
alu
e
s
.
T
h
e
d
ata
in
te
g
r
atio
n
f
r
o
m
d
i
f
f
er
e
n
t
h
u
g
e
d
atab
ases
,
d
ep
en
d
o
n
w
o
r
k
e
r
to
in
p
u
t
th
e
d
ata
th
at
also
lead
to
m
is
s
in
g
d
ata
w
h
et
h
er
in
te
n
tio
n
all
y
o
r
u
n
in
ten
tio
n
a
ll
y
.
A
d
d
itio
n
all
y
,
s
o
m
e
t
y
p
e
s
o
f
d
atab
ase
s
i
n
w
h
ich
d
ata
co
n
s
tan
t
l
y
ch
an
g
i
n
g
s
u
ch
a
s
te
m
p
o
r
al
d
atab
ase.
T
h
ese
ca
u
s
e
s
m
ig
h
t
i
m
p
ac
t
o
f
t
h
e
co
m
p
le
ten
e
s
s
a
n
d
co
r
r
ec
tn
ess
o
f
t
h
e
r
esu
lt p
r
o
d
u
ce
d
[
1
2
]
,
[
1
3
]
.
Miss
i
n
g
d
ata
is
al
w
a
y
s
u
n
w
a
n
ted
an
d
p
r
o
b
lem
at
ic,
esp
ec
ia
ll
y
d
u
r
i
n
g
q
u
er
y
p
r
o
ce
s
s
i
n
g
.
E
v
en
i
f
t
h
e
r
ate
o
f
th
e
m
i
s
s
i
n
g
v
al
u
es
is
s
m
al
l
it
m
i
g
h
t
s
er
io
u
s
l
y
b
ia
s
in
f
er
en
ce
.
Fro
m
th
e
d
atab
ase
lit
er
atu
r
e
it
ca
n
b
e
co
n
clu
d
ed
th
a
t
m
is
s
in
g
d
ata
m
ec
h
a
n
i
s
m
s
ar
e
o
f
ten
ca
te
g
o
r
ized
in
to
t
h
r
ee
d
if
f
er
en
t
ca
teg
o
r
ies.
First,
m
is
s
in
g
co
m
p
lete
l
y
at
r
an
d
o
m
(
MC
AR
)
w
h
ic
h
m
ea
n
s
t
h
er
e
ar
e
n
o
s
y
s
te
m
atic
d
i
f
f
er
e
n
ce
s
b
et
w
ee
n
m
is
s
in
g
an
d
o
b
s
er
v
ed
v
al
u
es
o
r
p
u
t
m
o
r
e
s
i
m
p
l
y
,
m
is
s
i
n
g
v
al
u
e
s
ap
p
ea
r
to
b
e
co
m
p
letel
y
r
an
d
o
m
.
Seco
n
d
,
m
i
s
s
i
n
g
a
t
r
an
d
o
m
(
MA
R
)
w
h
er
e
m
is
s
i
n
g
d
ata
o
cc
u
r
b
ased
o
n
s
o
m
e
o
b
s
er
v
ed
v
ar
iab
les
b
u
t
n
o
t
o
n
th
e
v
ar
iab
le
o
f
in
ter
est
o
r
th
e
v
ar
iab
le
u
n
d
er
s
t
u
d
y
.
I
n
p
ar
ticu
lar
t
h
is
m
ea
n
s
th
at
f
o
r
ea
ch
tu
p
le
m
o
s
t
a
ttrib
u
te
v
al
u
es
ar
e
av
ailab
le,
w
h
ile
j
u
s
t
s
o
m
e
ar
e
r
an
d
o
m
l
y
m
i
s
s
i
n
g
.
T
h
ir
d
,
m
is
s
i
n
g
n
o
t
at
r
a
n
d
o
m
(
MN
AR
)
,
i
n
t
h
i
s
ca
te
g
o
r
y
m
is
s
i
n
g
d
ata
o
cc
u
r
b
ased
o
n
n
v
ar
iab
le
o
f
in
ter
e
s
t o
r
th
e
d
ep
en
d
en
t
v
ar
iab
le
in
s
ta
tis
tica
l te
r
m
s
[
1
4
]
,
[
1
2
]
.
T
h
e
m
i
s
s
i
n
g
v
al
u
e
s
h
a
v
e
n
e
g
a
tiv
el
y
in
f
l
u
e
n
ce
o
n
t
h
e
q
u
alit
y
o
f
th
e
d
atab
ase
as
m
o
r
e
m
is
s
i
n
g
v
al
u
es
lead
to
h
ig
h
r
ate
o
f
p
r
ef
er
en
c
e
d
ata.
T
h
er
ef
o
r
e,
esti
m
at
in
g
t
h
e
m
is
s
in
g
v
alu
e
s
ca
n
e
n
h
an
c
e
th
e
q
u
alit
y
o
f
th
e
d
atab
ase
co
n
ten
ts
b
y
tr
an
s
f
o
r
m
in
g
th
e
d
atab
ase
s
tate
f
r
o
m
in
co
m
p
lete
t
o
co
m
p
lete.
I
n
th
is
s
ec
t
io
n
w
e
h
ig
h
li
g
h
t o
n
th
e
p
r
ed
ictio
n
m
e
th
o
d
s
o
f
h
a
n
d
lin
g
i
n
co
m
p
lete
d
ata.
W
e
class
i
f
y
th
e
p
r
ed
ictio
n
m
eth
o
d
s
in
to
t
w
o
m
ai
n
ca
teg
o
r
ies,
n
a
m
e
l
y
:
s
tati
s
tical
m
eth
o
d
s
a
n
d
m
ac
h
in
e
le
ar
n
in
g
m
eth
o
d
s
[
1
3
]
,
[
1
4
]
.
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
Mo
d
el
fo
r
P
r
o
ce
s
s
in
g
S
ky
lin
e
Qu
eries
in
C
r
o
w
d
-
s
o
u
r
ce
d
Da
ta
b
a
s
es
(
Ma
r
w
a
B
.
S
w
id
a
n
)
801
2
.
2
.
1
.
Sta
t
is
t
ica
l
M
et
ho
ds
\
Statis
t
ical
m
e
th
o
d
s
ar
e
th
e
b
a
s
e
f
o
r
m
a
n
y
o
f
th
e
p
r
ed
ictio
n
a
p
p
r
o
ac
h
es.
T
h
e
m
ai
n
ai
m
o
f
t
h
is
m
et
h
o
d
is
p
r
eser
v
i
n
g
t
h
e
d
i
s
tr
ib
u
tio
n
o
f
t
h
e
w
h
o
le
d
ata
b
y
a
v
o
id
in
g
b
ias o
n
t
h
e
d
ata
d
is
tr
ib
u
tio
n
.
T
h
e
p
r
ed
icted
v
alu
e
s
m
a
y
b
en
e
f
it
u
s
er
at
t
h
e
d
atab
a
s
e
le
v
el
b
u
t
n
o
t
at
t
h
e
t
u
p
le
le
v
el.
I
n
m
an
y
ca
s
es
u
s
er
s
ar
e
m
o
r
e
co
n
ce
r
n
o
n
t
h
e
in
d
iv
id
u
al
v
alu
e
o
f
t
h
e
attr
ib
u
te
r
ath
er
t
h
an
th
e
w
h
o
le
d
ata.
T
h
u
s
,
th
e
s
e
s
tati
s
tical
m
e
th
o
d
s
m
a
y
n
o
t
b
e
ap
p
r
o
p
r
iate
f
o
r
p
r
ef
er
en
ce
q
u
er
ies [
1
2
]
,
[
1
4
]
.
T
h
e
s
tatis
tical
m
et
h
o
d
s
ar
e
as f
o
ll
o
w
s
:
a.
Sin
g
le
I
m
p
u
tatio
n
(
SI)
-
I
n
th
i
s
m
et
h
o
d
,
th
e
w
h
o
le
m
i
s
s
i
n
g
v
alu
es
f
o
r
a
g
iv
en
attr
ib
u
te
ar
e
s
u
b
s
t
itu
ted
b
y
a
s
in
g
le
v
al
u
e.
T
h
e
m
et
h
o
d
s
th
at
in
v
o
lv
e
s
in
g
le
i
m
p
u
tatio
n
i
n
cl
u
d
e
m
ea
n
,
m
ed
ia
n
,
m
o
d
e,
m
ax
i
m
u
m
v
al
u
e
i
n
th
e
attr
ib
u
te
r
an
g
e,
m
i
n
i
m
u
m
v
alu
e
in
th
e
attr
ib
u
te
r
a
n
g
e,
a
n
d
ex
p
ec
tatio
n
m
ax
i
m
izat
io
n
.
T
h
is
m
et
h
o
d
i
s
n
o
t satiab
le
w
h
en
t
h
e
r
elev
a
n
t
er
r
o
r
is
v
er
y
h
i
g
h
.
b.
Mu
ltip
le
I
m
p
u
tatio
n
s
(
MI
)
-
I
n
th
i
s
m
et
h
o
d
,
m
o
r
e
th
an
o
n
e
esti
m
ated
v
al
u
es
ar
e
d
er
iv
ed
in
o
r
d
er
to
f
ill
in
th
e
m
is
s
i
n
g
v
al
u
e
s
.
T
h
e
co
m
p
u
tatio
n
co
s
t
o
f
th
i
s
m
et
h
o
d
is
h
i
g
h
er
t
h
an
SI,
b
u
t
it
i
s
m
o
r
e
ac
cu
r
ate
an
d
p
r
o
d
u
ctiv
e.
T
h
is
m
et
h
o
d
also
n
ee
d
s
a
n
ap
p
r
o
p
r
iate
m
ec
h
an
i
s
m
to
r
e
f
lect
t
h
e
u
n
ce
r
tai
n
t
y
o
f
m
is
s
i
n
g
d
ata
an
d
p
r
ec
is
el
y
est
i
m
a
te
t
h
e
m
is
s
i
n
g
v
alu
e
s
.
An
i
m
p
o
r
tan
t
ar
g
u
m
e
n
t
i
n
f
a
v
o
u
r
o
f
t
h
is
a
p
p
r
o
ac
h
is
t
h
at,
f
r
eq
u
en
tl
y
,
attr
ib
u
te
s
h
a
v
e
r
el
atio
n
s
h
ip
s
(
co
r
r
elatio
n
s
)
a
m
o
n
g
t
h
e
m
s
el
v
es.
I
n
t
h
is
w
a
y
,
t
h
o
s
e
co
r
r
elatio
n
s
co
u
ld
b
e
u
s
ed
to
cr
ea
te
a
p
r
ed
i
ctiv
e
m
o
d
el
f
o
r
cla
s
s
i
f
icatio
n
o
r
r
eg
r
ess
io
n
(
d
ep
en
d
i
n
g
o
n
t
h
e
attr
ib
u
te
t
y
p
e
w
it
h
m
is
s
i
n
g
d
ata,
b
ein
g
,
r
esp
ec
tiv
el
y
,
n
o
m
in
a
l o
r
co
n
tin
u
o
u
s
)
.
2
.
2
.
2
.
M
a
chine Lea
rning
M
et
ho
ds
Ma
ch
i
n
e
lear
n
in
g
(
ML
)
m
eth
o
d
s
lear
n
p
r
ed
ictio
n
m
o
d
els
to
h
an
d
le
t
h
e
d
atab
ase
w
it
h
m
i
s
s
in
g
v
al
u
es
b
ased
o
n
th
e
co
m
p
lete
d
atab
as
e
in
s
tan
ce
s
.
So
m
e
o
f
t
h
e
M
L
m
et
h
o
d
s
in
v
o
l
v
e
p
r
o
b
ab
ilis
tic
ap
p
r
o
ac
h
to
p
r
ed
ict
th
e
m
is
s
in
g
v
alu
e
s
.
M
L
m
e
t
h
o
d
s
ar
e
n
o
n
-
p
ar
a
m
etr
ic
as
t
h
e
y
d
o
n
o
t
r
el
y
o
n
d
ata
d
is
t
r
ib
u
tio
n
d
u
r
i
n
g
t
h
e
tr
ain
i
n
g
o
r
test
i
n
g
p
r
o
ce
s
s
.
T
h
ese
m
et
h
o
d
s
ar
e
a
ls
o
ca
ll
ed
p
a
r
a
mete
r
-
fr
ee
m
et
h
o
d
s
o
r
d
is
tr
ib
u
tio
n
-
fr
ee
m
et
h
o
d
s
.
T
h
is
m
e
th
o
d
n
ee
d
s
l
o
n
g
ti
m
e
b
ef
o
r
e
g
et
ti
n
g
t
h
e
o
p
ti
m
al
v
alu
e
s
b
ec
au
s
e
it
r
ep
ea
ts
w
o
r
k
m
an
y
ti
m
es
u
n
t
il
it
ca
tch
es
t
h
e
lo
w
e
s
t
r
ele
v
an
t
er
r
o
r
.
T
h
er
e
ar
e
s
ev
er
al
ap
p
r
o
ac
h
es
th
at
h
a
v
e
b
ee
n
p
r
o
p
o
s
ed
w
h
ich
b
elo
n
g
to
th
e
M
L
m
et
h
o
d
s
w
h
ich
in
c
lu
d
e
C
4
.
5
,
A
u
to
cla
s
s
,
Fra
ctio
n
in
g
C
ase
s
,
C
N2
,
k
-
n
ea
r
es
t
n
eig
h
b
o
r
(
k
N
N)
[
1
4
]
,
[
2
9
]
.
2
.
3
.
Sk
y
lin
e
Q
ueries
Sk
y
li
n
e
q
u
er
ies
h
a
v
e
b
ee
n
w
i
d
ely
u
s
ed
as
a
n
attr
ac
ti
v
e
o
p
er
ato
r
in
m
u
lti
-
cr
iter
ia
d
ec
is
i
o
n
m
a
k
i
n
g
ap
p
licatio
n
s
,
w
h
er
e
m
an
y
cr
it
er
ia
ar
e
in
v
o
lv
ed
in
th
e
q
u
er
y
s
tate
m
e
n
t
to
s
elec
t
th
e
m
o
s
t
s
u
itab
le
a
n
s
w
er
th
a
t
f
its
t
h
e
u
s
er
r
eq
u
ir
e
m
en
ts
.
A
n
o
th
er
d
o
m
ai
n
th
at
ap
p
lied
th
e
s
k
y
li
n
e
q
u
er
ies
is
t
h
e
d
ec
is
io
n
s
u
p
p
o
r
t
s
y
s
te
m
a
n
d
r
ec
o
m
m
e
n
d
er
s
y
s
te
m
,
w
h
er
e
th
ese
s
y
s
te
m
s
co
m
b
in
e
v
ar
io
u
s
in
ter
e
s
ts
to
h
elp
u
s
er
s
b
y
r
ec
o
m
m
e
n
d
i
n
g
a
s
tr
ateg
ic
d
ec
is
io
n
.
So
m
e
d
e
f
in
itio
n
s
r
elate
d
to
s
k
y
li
n
e
q
u
er
i
es
ar
e
g
iv
e
n
i
n
th
e
f
o
llo
w
in
g
[
4
]
,
[
8
]
,
[
1
1
]
,
[
1
4
]
,
[
2
7
]
.
Do
m
i
na
nce:
g
iv
e
n
t
w
o
tu
p
le
s
p
i
an
d
p
j
D
d
ata
s
et
w
i
th
d
d
i
m
en
s
io
n
s
,
p
i
d
o
m
i
n
ates
p
j
(
th
e
g
r
ea
ter
is
b
etter
)
(
d
en
o
ted
b
y
p
i
>p
j
)
if
an
d
o
n
l
y
if
t
h
e
f
o
llo
w
i
n
g
c
o
n
d
itio
n
h
o
ld
s
:
l
j
l
i
l
k
j
k
i
k
d
p
d
p
d
d
d
p
d
p
d
d
.
.
,
.
.
,
S
k
y
lin
e:
s
k
y
li
n
e
tec
h
n
iq
u
e
r
etr
iev
es
t
h
e
s
k
y
li
n
e,
S
i
n
a
w
a
y
s
u
c
h
t
h
at
a
n
y
s
k
y
l
in
e
i
n
S
is
n
o
t
d
o
m
i
n
ated
b
y
an
y
o
th
er
t
u
p
le
s
in
th
e
d
ata
s
et.
S
k
y
lin
e
qu
er
ies:
s
elec
t
a
tu
p
l
e
p
i
f
r
o
m
t
h
e
s
et
o
f
d
ata
s
et
D
if
an
d
o
n
l
y
if
p
i
is
as
g
o
o
d
as
p
j
(
w
h
er
e
i
j
)
in
all
d
im
e
n
s
io
n
s
(
attr
ib
u
t
es)
an
d
s
tr
ictly
i
n
at
least
o
n
e
d
i
m
en
s
io
n
(
attr
ib
u
te)
.
W
e
u
s
e
S
skyline
to
d
en
o
te
th
e
s
et
o
f
s
k
y
lin
e
t
u
p
le
s
,
S
skyline
=
{
p
i
|
p
i
,
p
j
D
,
p
i
p
j
}.
Co
m
pa
ra
ble:
L
et
t
h
e
t
u
p
le
s
a
i
an
d
a
j
R
,
a
i
a
n
d
a
j
ar
e
co
m
p
ar
ab
le
(
d
en
o
ted
b
y
j
i
a
a
)
if
a
n
d
o
n
l
y
i
f
th
e
y
h
a
v
e
n
o
m
i
s
s
i
n
g
v
al
u
es
i
n
at
least
o
n
e
id
en
t
ical
d
i
m
e
n
s
io
n
;
o
th
er
w
i
s
e
a
i
is
i
n
co
m
p
ar
ab
le
to
a
j
(
d
en
o
ted
by
j
i
a
a
).
Mo
s
t
o
f
t
h
e
s
k
y
li
n
e
al
g
o
r
ith
m
s
r
el
y
o
n
t
h
e
as
s
u
m
p
tio
n
o
f
co
m
p
leten
e
s
s
o
f
t
h
e
d
ata,
i.e
.
all
v
al
u
es
o
f
p
o
in
ts
ar
e
p
r
esen
t
an
d
k
n
o
w
n
.
Ho
w
e
v
er
,
in
m
a
n
y
ca
s
es,
th
is
ass
u
m
p
tio
n
d
o
es
n
o
t
h
o
ld
.
T
h
u
s
,
co
n
v
e
n
tio
n
a
l
s
k
y
li
n
e
al
g
o
r
ith
m
s
ca
n
n
o
t
b
e
ap
p
lied
d
ir
ec
tly
.
B
esid
es,
t
h
e
in
co
m
p
lete
n
es
s
o
f
d
ata
lead
s
to
lo
s
s
tr
an
s
iti
v
it
y
p
r
o
p
er
ty
o
f
s
k
y
li
n
e
tec
h
n
iq
u
e
w
h
ic
h
i
s
h
eld
o
n
a
ll
e
x
is
t
in
g
s
k
y
li
n
e
tec
h
n
iq
u
e
s
.
T
h
is
f
u
r
t
h
er
lead
s
to
c
y
clic
d
o
m
i
n
a
n
ce
b
et
w
ee
n
th
e
t
u
p
les
as
s
o
m
e
tu
p
le
s
ar
e
in
co
m
p
ar
ab
le
w
it
h
ea
ch
o
th
er
an
d
th
u
s
n
o
tu
p
le
i
s
co
n
s
id
er
ed
as sk
y
li
n
e
[
4
]
.
3.
RE
L
AT
E
D
WO
RK
T
h
is
s
ec
tio
n
p
r
esen
t
s
a
n
d
r
ev
ie
w
s
t
h
e
p
r
ev
io
u
s
ap
p
r
o
ac
h
es
r
elate
d
to
s
k
y
li
n
e
q
u
er
ie
s
i
n
co
m
p
lete
tr
ad
itio
n
al
d
atab
ase.
Fu
r
t
h
er
m
o
r
e,
s
k
y
li
n
e
q
u
er
ies
i
n
i
n
co
m
p
lete
tr
ad
itio
n
al
d
atab
ase
h
av
e
b
ee
n
d
is
c
u
s
s
ed
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
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n
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J
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g
&
C
o
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p
Sci,
Vo
l.
10
,
No
.
2
,
Ma
y
2
0
1
8
:
7
9
8
–
8
0
6
802
L
ast
l
y
,
th
e
p
r
ev
io
u
s
s
tr
ate
g
ie
s
f
o
r
s
k
y
li
n
e
q
u
er
ies
i
n
cr
o
w
d
-
s
o
u
r
ci
n
g
d
atab
ases
h
a
v
e
b
ee
n
ex
p
lai
n
ed
an
d
d
is
cu
s
s
ed
d
elib
er
atel
y
.
3
.
1
.
S
k
y
lin
e
Q
ueries f
o
r
Co
m
p
let
e
Da
t
a
First
to
p
ic
o
f
s
k
y
lin
e
q
u
er
ie
s
in
co
m
p
lete
d
atab
ase
h
av
e
b
ee
n
ad
d
r
ess
ed
w
it
h
t
w
o
d
if
f
er
e
n
t
s
o
lu
tio
n
s
th
e
B
lo
ck
-
Ne
s
ted
-
L
o
o
p
(
B
NL
)
an
d
Div
id
e
-
an
d
-
C
o
n
q
u
er
(
D&
C
)
[
1
5
]
.
I
n
2
0
0
2
,
[
1
6
]
p
r
ese
n
ted
a
n
e
w
o
n
l
in
e
s
k
y
li
n
e
al
g
o
r
ith
m
ca
l
led
Nea
r
est
Nei
g
h
b
o
r
(
NN)
.
I
t
is
d
i
f
f
e
r
en
t
ab
o
u
t
t
h
e
last
a
lg
o
r
it
h
m
s
th
at
co
m
p
u
ted
th
e
Sk
y
li
n
e
i
n
b
atch
,
b
u
t
t
h
e
NN
alg
o
r
it
h
m
r
etu
r
n
s
th
e
f
i
r
s
t
r
esu
lts
i
m
m
ed
iatel
y
,
a
n
d
f
i
n
d
m
o
r
e
r
es
u
lt
s
co
n
tin
u
o
u
s
l
y
.
F
u
r
t
h
er
m
o
r
e,
m
an
y
ap
p
r
o
ac
h
es
h
a
v
e
b
ee
n
s
u
g
g
e
s
tio
n
f
o
r
s
k
y
lin
e
q
u
er
ie
s
in
co
m
p
lete
d
atab
ase
in
cl
u
d
ed
:
So
r
t
-
Fil
ter
-
S
k
y
l
in
e
(
SF
S)
[
1
7
]
,
B
r
an
ch
-
B
o
u
n
d
-
S
k
y
l
in
e
(
B
B
S)
[
1
8
]
,
L
in
ea
r
E
li
m
i
n
atio
n
So
r
t
f
o
r
Sk
y
li
n
e
(
L
E
SS
)
[
1
9
]
,
So
r
t
a
n
d
L
i
m
it
Sk
y
li
n
e
al
g
o
r
ith
m
(
SaL
Sa)
[
2
0
]
,
C
ac
h
e
B
ased
C
o
n
s
tr
ain
ed
S
k
y
li
n
e
(
C
B
C
S)
[
2
1
]
,
an
d
o
th
er
s
.
3
.
2
.
Sk
y
lin
e
Q
ueries f
o
r
I
nc
o
m
p
let
e
Da
t
a
T
h
er
e
ar
e
n
u
m
er
o
u
s
alg
o
r
it
h
m
s
w
h
ic
h
ap
p
lied
s
k
y
lin
e
q
u
er
ies
f
o
r
in
co
m
p
lete
d
ata
b
ase.
T
h
is
in
cl
u
d
es
b
u
t
n
o
t
li
m
ited
t
h
e
f
ir
s
t
w
o
r
k
co
n
tr
ib
u
ted
i
n
[
4
]
p
r
o
p
o
s
ed
tw
o
al
g
o
r
it
h
m
s
f
o
r
h
an
d
lin
g
t
h
e
s
k
y
li
n
e
q
u
er
ies
i
n
i
n
co
m
p
lete
d
ata,
n
a
m
e
l
y
:
B
u
c
k
et
a
n
d
I
s
k
y
li
n
e.
Mo
r
eo
v
er
,
R
ep
lace
m
e
n
t
B
ased
Sets
S
k
y
li
n
e
C
o
m
p
u
tatio
n
(
R
B
S
SQ)
[
2
2
]
,
s
o
r
t
-
b
ased
I
n
co
m
p
lete
Data
S
k
y
l
in
e
(
SID
S)
[
2
3
]
,
I
n
co
s
k
y
li
n
e
[
1
4
]
h
av
e
b
ee
n
p
r
o
p
o
s
ed
to
s
o
lv
e
th
e
i
s
s
u
e
o
f
p
r
o
ce
s
s
i
n
g
s
k
y
li
n
e
q
u
er
ie
s
i
n
i
n
co
m
p
lete
d
atab
ase.
T
h
e
p
r
ev
io
u
s
w
o
r
k
s
h
as
o
n
l
y
f
o
cu
s
ed
o
n
h
o
w
to
tack
l
e
th
e
is
s
u
e
o
f
p
r
o
ce
s
s
i
n
g
s
k
y
l
in
e
q
u
er
ies
i
n
i
n
co
m
p
lete
d
at
a,
ai
m
in
g
at
s
o
l
v
i
n
g
th
e
is
s
u
e
o
f
c
y
clic
d
o
m
i
n
a
n
ce
an
d
lo
s
in
g
th
e
tr
a
n
s
it
iv
i
t
y
p
r
o
p
er
ty
o
f
s
k
y
li
n
e
tech
n
iq
u
e.
Ho
w
e
v
er
,
n
o
t
m
u
c
h
atten
tio
n
h
a
s
b
ee
n
p
aid
o
n
i
m
p
r
o
v
in
g
t
h
e
q
u
alit
y
o
f
t
h
e
s
k
y
lin
e
r
es
u
lt
s
b
y
p
r
o
v
id
i
n
g
e
s
ti
m
ated
v
a
lu
e
s
f
o
r
t
h
e
s
k
y
li
n
es
w
i
th
in
co
m
p
lete
d
at
a.
Ho
w
e
v
er
,
th
e
o
n
l
y
w
o
r
k
t
h
at
ad
d
r
ess
es
th
e
is
s
u
e
o
f
p
r
ed
ictin
g
th
e
m
i
s
s
in
g
v
alu
e
s
o
f
th
e
s
k
y
lin
e
s
is
co
n
tr
ib
u
ted
in
[
1
4
]
aim
in
g
at
m
a
n
i
p
u
latin
g
th
e
m
is
s
i
n
g
v
alu
e
s
b
ef
o
r
e
r
etu
r
n
in
g
th
e
s
k
y
li
n
es to
th
e
u
s
er
.
A
d
d
itio
n
a
ll
y
,
th
e
m
o
s
t o
f
w
o
r
k
s
d
esi
g
n
e
d
to
b
e
u
s
ed
in
tr
ad
itio
n
al
d
atab
ases
o
n
l
y
.
3
.
3
.
Sk
y
lin
e
i
n Cr
o
w
d
-
So
urcing
Da
t
a
ba
s
e
C
r
o
w
d
-
s
o
u
r
ci
n
g
d
atab
ases
h
a
s
v
ar
io
u
s
u
n
iq
u
e
f
ea
t
u
r
es
co
m
p
ar
ed
w
i
th
th
e
tr
ad
itio
n
al
d
atab
ases
.
T
h
er
ef
o
r
e,
it
m
i
g
h
t
n
o
t
b
e
p
o
s
s
ib
le
to
d
ir
ec
tl
y
ap
p
l
y
ap
p
r
o
a
ch
es
p
r
o
d
u
ce
d
f
o
r
tr
ad
itio
n
al
d
atab
ase
in
cr
o
w
d
-
s
o
u
r
cin
g
d
atab
ase
s
.
T
o
th
e
b
e
s
t
o
f
o
u
r
k
n
o
w
led
g
e,
th
er
e
is
n
o
t
m
u
c
h
atte
n
tio
n
g
i
v
e
n
o
n
p
r
o
ce
s
s
in
g
o
f
s
k
y
li
n
e
q
u
er
ies
in
cr
o
w
d
-
s
o
u
r
ci
n
g
d
at
ab
ases
w
it
h
in
co
m
p
lete
d
ata.
Mo
s
t
o
f
th
e
s
e
s
t
u
d
ies
co
n
ce
n
tr
ate
o
n
d
esig
n
in
g
s
k
y
li
n
e
s
tr
ate
g
ies
to
est
i
m
a
te
t
h
e
m
is
s
in
g
v
a
lu
e
s
o
f
s
k
y
li
n
e
r
esu
lt
s
en
s
u
r
i
n
g
lo
w
p
r
o
ce
s
s
in
g
co
s
t
an
d
les
s
ti
m
e
laten
c
y
a
n
d
h
i
g
h
ac
cu
r
ac
y
f
o
r
th
e
esti
m
ated
r
es
u
lts
.
I
n
[
8
]
,
[
9
]
th
ey
h
a
v
e
p
r
o
p
o
s
ed
a
h
y
b
r
id
ap
p
r
o
ac
h
co
m
b
in
i
n
g
d
y
n
a
m
ic
cr
o
w
d
-
s
o
u
r
ci
n
g
w
it
h
h
e
u
r
is
tic
tech
n
iq
u
es.
T
h
e
id
ea
o
f
t
h
e
p
r
o
p
o
s
ed
ap
p
r
o
ac
h
r
elies
o
n
u
tili
zin
g
a
s
et
o
f
h
e
u
r
is
tic
tec
h
n
iq
u
e
s
in
o
r
d
er
to
p
r
ed
ict
th
e
m
i
s
s
i
n
g
v
al
u
e
s
f
o
r
all
tu
p
les.
I
t
al
s
o
ex
p
lo
its
th
e
co
n
ten
t
s
o
f
t
h
e
cr
o
w
d
f
o
r
s
o
m
e
ca
s
es
to
i
m
p
r
o
v
e
ac
cu
r
ac
y
o
f
th
e
est
i
m
ated
v
al
u
es.
A
n
e
w
a
lg
o
r
it
h
m
n
a
m
ed
C
r
o
w
d
S
k
y
h
as
b
ee
n
p
r
o
p
o
s
e
d
[
1
1
]
.
T
h
e
alg
o
r
ith
m
co
n
s
id
er
s
th
r
ee
k
e
y
f
ac
to
r
s
th
at
in
f
lu
e
n
ce
th
e
p
r
o
ce
s
s
o
f
s
k
y
l
in
e
w
h
ic
h
ar
e
m
in
i
m
izin
g
th
e
m
o
n
etar
y
co
s
t,
r
ed
u
cin
g
th
e
late
n
c
y
,
a
n
d
i
m
p
r
o
v
in
g
th
e
ac
c
u
r
ac
y
o
f
a
cr
o
w
d
-
s
o
u
r
ce
d
s
k
y
lin
e.
A
l
th
o
u
g
h
th
e
w
o
r
k
ass
u
m
ed
th
at
t
h
e
p
r
o
ce
s
s
o
f
v
al
u
e
esti
m
atio
n
b
y
t
h
e
cr
o
w
d
w
il
l
b
e
ap
p
lied
o
n
a
s
et
o
f
v
ir
tu
al
at
tr
ib
u
tes.
W
h
ic
h
m
ea
n
s
th
e
i
n
itial
d
atab
ase
is
co
m
p
let
e,
b
u
t
w
it
h
i
n
s
u
f
f
icien
t
d
ata
th
at
m
i
g
h
t
n
o
t
h
elp
to
p
r
o
v
id
e
a
n
ac
cu
r
ate
a
n
s
w
er
s
f
o
r
s
k
y
li
n
e
q
u
er
ies.
T
h
er
ef
o
r
e,
u
til
izin
g
t
h
e
cr
o
w
d
d
atab
as
e
b
y
g
e
n
er
ati
n
g
s
o
m
e
r
elev
a
n
t
v
ir
tu
al
attr
ib
u
tes
a
n
d
th
eir
v
al
u
es a
r
e
p
r
ed
icted
b
y
t
h
e
cr
o
w
d
w
o
r
k
er
s
to
s
u
p
p
o
r
t th
e
q
u
er
y
r
es
u
lt
s
.
4.
RE
S
E
ARCH
M
E
T
H
O
D
I
n
th
i
s
s
ec
t
io
n
w
e
p
r
ese
n
t
o
u
r
p
r
o
p
o
s
ed
m
o
d
el
to
co
m
p
u
te
s
k
y
li
n
es
i
n
i
n
co
m
p
lete
cr
o
wd
-
s
o
u
r
ci
n
g
d
atab
ase.
F
ig
1
.
o
u
tli
n
es
t
h
e
d
etails
s
tr
u
ct
u
r
e
o
f
t
h
e
m
o
d
el
t
h
at
co
n
s
i
s
ts
o
f
eig
h
t
p
h
ase
s
.
I
n
th
is
f
r
a
m
e
w
o
r
k
,
w
e
ass
u
m
e
a
tr
ad
itio
n
al
lo
ca
l
d
at
a
s
et
co
n
tai
n
s
tu
p
le
s
w
it
h
s
o
m
e
m
i
s
s
i
n
g
v
a
lu
e
s
a
n
d
th
e
u
s
er
w
ill
s
u
b
m
it
a
s
k
y
li
n
e
q
u
er
y
w
it
h
s
o
m
e
p
r
ed
ef
i
n
ed
p
r
ef
er
en
ce
s
.
T
h
e
u
s
er
q
u
er
y
m
a
y
al
s
o
co
n
tai
n
ad
d
iti
o
n
al
m
eta
-
d
ata
f
o
r
d
escr
ib
in
g
th
e
r
eq
u
ir
ed
r
es
u
lt
q
u
alit
y
,
m
ax
i
m
al
q
u
er
y
b
u
d
g
e
t,
r
esp
o
n
s
e
ti
m
e
r
eq
u
ir
e
m
e
n
ts
,
etc.
F
u
r
t
h
er
d
etails
r
elate
d
to
ea
ch
co
m
p
o
n
e
n
t a
r
e
ex
p
lain
i
n
g
in
t
h
e
f
o
llo
w
in
g
s
u
b
s
ec
tio
n
.
4
.
1
.
D
a
t
a
F
iltr
a
t
io
n
T
h
is
co
m
p
o
n
e
n
t
an
al
y
ze
s
th
e
in
itial
d
atab
ase
in
o
r
d
er
to
id
e
n
ti
f
y
a
n
d
r
em
o
v
e
th
e
u
n
n
ec
es
s
ar
y
tu
p
le
s
in
th
e
i
n
it
ial
d
atab
ase.
T
h
is
p
r
o
ce
s
s
lead
s
to
eli
m
i
n
ate
t
h
e
d
o
m
in
ated
t
u
p
les
b
y
ap
p
l
y
i
n
g
t
h
e
s
k
y
l
in
e
p
r
o
ce
s
s
o
n
th
e
i
n
co
m
p
lete
d
atab
ase.
T
h
er
ef
o
r
e,
th
i
s
p
r
o
ce
s
s
h
elp
s
to
r
ed
u
ce
th
e
a
m
o
u
n
t
o
f
d
ata
in
th
e
n
e
x
t
p
r
o
ce
s
s
es.
Ma
n
y
d
o
m
in
a
ted
tu
p
les
ca
n
b
e
s
af
el
y
r
e
m
o
v
ed
as
t
h
e
y
h
a
v
e
n
o
co
n
tr
ib
u
tio
n
i
n
th
e
s
k
y
l
i
n
e
r
es
u
lts
.
Do
i
n
g
s
o
r
esu
lt
s
in
to
m
i
n
i
m
izi
n
g
t
h
e
m
o
n
etar
y
co
s
t a
n
d
th
e
ti
m
e
late
n
c
y
to
es
ti
m
ate
t
h
e
m
i
s
s
i
n
g
v
al
u
es f
r
o
m
t
h
e
cr
o
w
d
.
Evaluation Warning : The document was created with Spire.PDF for Python.
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d
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J
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&
C
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p
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N:
2502
-
4752
A
Mo
d
el
fo
r
P
r
o
ce
s
s
in
g
S
ky
lin
e
Qu
eries
in
C
r
o
w
d
-
s
o
u
r
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d
Da
ta
b
a
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es
(
Ma
r
w
a
B
.
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w
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)
803
4
.
2
.
Sa
m
ple
Se
lect
o
r
a
nd
At
t
ribute
Ana
ly
ze
r
T
h
e
aim
o
f
t
h
i
s
co
m
p
o
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en
t
is
to
g
en
er
ate
a
s
a
m
p
le
f
r
o
m
t
h
e
in
itial
d
atab
ase
to
b
e
u
s
ed
f
o
r
an
al
y
z
in
g
th
e
r
elatio
n
s
h
ip
s
b
et
w
ee
n
attr
ib
u
tes.
A
n
a
l
y
zi
n
g
th
e
r
elatio
n
s
h
ip
b
et
w
ee
n
t
h
e
attr
ib
u
te
s
h
e
lp
s
to
esti
m
ate
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h
e
m
is
s
i
n
g
v
al
u
e
s
i
n
t
h
e
t
u
p
les.
I
t
is
i
m
p
r
ac
tical
to
d
o
t
h
is
p
r
o
ce
s
s
o
v
er
t
h
e
w
h
o
le
d
ata
s
i
n
ce
t
h
e
n
u
m
b
er
o
f
tu
p
les
i
n
th
e
in
i
tial
d
atab
ase
ar
e
v
er
y
h
u
g
e.
T
h
er
ef
o
r
e,
a
n
al
y
zi
n
g
th
e
attr
ib
u
te
co
r
r
elatio
n
d
ir
ec
tl
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o
n
t
h
e
w
h
o
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d
ata
m
ig
h
t
i
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cu
r
h
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o
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ea
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d
co
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s
u
m
e
s
i
g
n
i
f
ican
t
a
m
o
u
n
t
o
f
ti
m
e.
He
n
ce
,
ex
tr
ac
tin
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a
s
m
al
l
p
o
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tio
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o
f
th
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atab
ase
f
o
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a
ttrib
u
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al
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p
r
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m
is
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n
g
t
h
e
q
u
alit
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o
f
th
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esti
m
ated
v
a
l
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es.
T
h
e
s
a
m
p
le
w
ill
b
e
g
en
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ated
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an
d
o
m
l
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d
w
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h
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a
p
r
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ed
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an
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e.
A
t
tr
ib
u
te
a
n
al
y
s
i
s
ass
i
s
ts
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g
en
er
ati
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h
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ap
p
r
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x
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m
ate
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u
n
ctio
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ep
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d
en
cies
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et
w
ee
n
attr
ib
u
tes.
T
h
is
p
r
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ce
s
s
is
f
u
r
t
h
er
ex
p
lai
n
ed
in
t
h
e
n
e
x
t s
u
b
s
ec
tio
n
.
Fig
u
r
e
1
.
T
h
e
Pro
p
o
s
ed
Mo
d
el
o
f
Sk
y
li
n
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Qu
er
ie
s
in
C
r
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w
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-
So
u
r
cin
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I
n
co
m
p
le
te
Data
b
ase
s
4
.
3
.
Appro
x
i
m
a
t
e
F
un
ct
io
n
a
l D
ependencie
s
G
e
nera
t
o
r
T
h
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ai
m
o
f
t
h
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s
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m
p
o
n
e
n
t
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to
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er
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e
t
h
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ip
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et
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ttrib
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Fo
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th
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s
y
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o
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s
elec
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s
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m
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le
tr
y
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n
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t
o
ca
p
tu
r
e
th
e
r
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s
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ip
s
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et
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ee
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attr
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u
te
s
an
d
d
eter
m
in
e
h
o
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e
v
al
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e
in
o
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attr
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f
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h
e
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e
s
o
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e
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er
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tes.
Def
ini
t
io
n:
Ap
pro
x
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m
a
t
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F
u
nct
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Depen
dency
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AF
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Giv
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R
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et
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w
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s
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a
t
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x
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ate
f
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d
ep
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A
DF)
b
et
w
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X
an
d
d
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d
en
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ted
b
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Xf
d
i
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if
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co
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r
esp
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di
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all
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u
t
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a
ll
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tio
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les o
f
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T
h
e
s
et
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is
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lled
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d
etermin
in
g
s
et
o
f
d
i
d
en
o
ted
b
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Dtr
S
et(
d
i
)
[
1
4
]
.
T
o
g
en
er
ate
th
e
A
FD,
w
e
f
ir
s
t
d
iv
id
e
th
e
s
a
m
p
le
s
et
w
it
h
m
is
s
i
n
g
v
al
u
es
in
to
t
w
o
s
ets.
T
h
e
f
ir
s
t
s
e
t
co
n
tain
s
all
v
alu
e
s
t
h
at
m
is
s
i
n
g
i
n
t
h
e
id
en
t
ical
attr
ib
u
te
t
h
at
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ee
d
s
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esti
m
ated
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tain
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m
ai
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in
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.
T
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s
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ip
s
b
et
w
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e
attr
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b
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f
t
h
e
r
elatio
n
.
T
h
en
t
h
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AFD
i
s
g
en
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ated
,
t
h
at
h
elp
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n
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attr
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f
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th
e
v
al
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o
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T
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r
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t
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t
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A
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r
ef
lects t
h
e
r
elatio
n
s
h
ip
s
b
et
w
ee
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attr
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u
te
s
[
1
4
]
.
4
.
4
.
Str
eng
t
h o
f
P
r
o
ba
bil
it
y
Co
rr
ela
t
io
ns
E
s
t
i
m
a
t
o
r
T
h
e
ai
m
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co
m
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e
th
e
a
ttrib
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n
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attr
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tes
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el
y
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th
e
s
tr
en
g
t
h
o
f
p
r
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b
ab
ilit
y
co
r
r
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n
s
b
et
w
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t
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s
,
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o
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s
ta
n
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d
i
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d
d
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d
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o
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t
t
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d
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n
f
l
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ce
s
t
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al
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o
f
d
j
.
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h
e
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tr
en
g
th
o
f
p
r
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b
a
b
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co
r
r
elatio
n
s
b
et
w
ee
n
d
i
an
d
d
j
is
d
en
o
ted
as
P(
d
i
,
d
j
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an
d
is
f
o
r
m
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lated
as f
o
llo
w
s
[
1
4
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r
ef
er
s
to
t
h
e
n
u
m
b
e
r
o
f
d
is
ti
n
ct
v
al
u
es.
T
h
e
v
al
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o
f
P(
d
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d
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i
n
d
icate
s
th
e
s
tr
en
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t
h
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at
ev
er
y
d
is
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n
ct
v
al
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e
in
d
j
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s
o
ciate
d
w
it
h
a
u
n
iq
u
e
v
alu
e
i
n
d
i
.
4
.
5
.
M
is
s
ing
v
a
lues
predict
o
r
T
h
is
co
m
p
o
n
e
n
t
is
r
esp
o
n
s
ib
l
e
to
s
u
b
s
tit
u
te
t
h
e
m
is
s
i
n
g
v
a
lu
es
o
f
t
h
e
d
atab
ase
w
i
th
s
o
m
e
i
m
p
u
ted
v
alu
e
s
d
er
iv
ed
u
s
in
g
t
h
e
a
v
ail
ab
le
d
ata
o
f
t
h
e
d
atab
ase.
T
h
i
s
is
ac
h
ie
v
ed
b
ased
o
n
co
u
n
ti
n
g
th
e
f
r
eq
u
e
n
c
y
o
f
th
e
v
al
u
e
e
x
is
ts
w
it
h
s
e
v
er
al
alter
n
ati
v
e
v
al
u
es
b
y
r
ep
lace
th
e
m
i
s
s
i
n
g
v
alu
e
s
w
i
th
t
h
e
e
s
ti
m
ated
v
a
lu
e
s
.
I
n
th
is
p
r
o
ce
s
s
t
h
er
e
m
i
g
h
t
b
e
m
an
y
es
ti
m
ated
v
al
u
es
f
o
r
a
d
i
m
en
s
io
n
t
h
at
n
ee
d
to
b
e
c
o
n
s
i
d
er
ed
.
H
o
w
ev
er
,
in
s
o
m
e
ca
s
es
th
er
e
m
i
g
h
t
b
e
m
o
r
e
th
an
o
n
e
AFD
t
h
at
ca
n
b
e
g
e
n
er
ated
b
et
w
ee
n
th
e
d
i
m
e
n
s
io
n
s
.
I
n
t
h
i
s
ca
s
e,
th
e
r
esu
lts
o
f
t
h
e
AFDs
ar
e
co
m
b
in
ed
to
esti
m
ate
t
h
e
m
is
s
in
g
v
al
u
e.
4
.
6
.
Rela
t
iv
e
er
ro
r
a
nd
t
hresh
o
ld v
a
lue ident
if
ier
T
h
is
co
m
p
o
n
en
t
co
n
ce
r
n
s
o
n
ch
ec
k
in
g
t
h
e
ac
cu
r
ac
y
o
f
t
h
e
esti
m
ated
v
al
u
es
g
e
n
er
ated
u
s
i
n
g
th
e
ex
is
t
in
g
d
ata
o
f
th
e
d
atab
a
s
e.
Af
ter
al
l
m
is
s
i
n
g
v
a
lu
e
s
h
as
b
ee
n
es
ti
m
ated
i
n
la
s
t
s
tag
e
o
f
t
h
e
p
r
o
p
o
s
ed
m
o
d
el,
th
e
s
y
s
te
m
co
m
p
u
tes
th
e
r
elat
iv
e
er
r
o
r
w
h
ic
h
i
n
d
icate
s
th
e
er
r
o
r
r
ate
b
etw
ee
n
t
h
e
o
b
tain
e
d
esti
m
a
ted
v
al
u
e
s
w
h
e
n
th
e
d
atab
ase
h
a
s
s
o
m
e
m
is
s
i
n
g
v
al
u
es
in
o
n
e
o
r
m
o
r
e
d
i
m
en
s
io
n
s
a
n
d
th
e
r
ea
l
v
al
u
e
s
w
h
e
n
t
h
e
d
atab
ase
is
co
m
p
lete
w
i
th
n
o
m
is
s
in
g
v
alu
e
s
.
I
f
th
e
r
elati
v
e
er
r
o
r
r
ate
is
lar
g
er
t
h
a
n
t
h
e
u
s
er
d
ef
i
n
ed
th
r
es
h
o
ld
v
al
u
e,
th
en
th
e
e
s
ti
m
ated
v
alu
e
s
w
i
ll
b
e
d
is
ca
r
d
ed
.
Oth
er
w
i
s
e,
a
cc
ep
t
th
e
esti
m
ated
v
al
u
es
a
n
d
u
s
e
th
e
m
i
n
t
h
e
s
k
y
li
n
e
r
esu
lts
.
I
n
o
n
e
ca
s
e,
t
h
is
h
elp
s
u
s
to
b
en
ef
i
t
f
r
o
m
t
h
e
ex
i
s
ti
n
g
d
ata
i
n
p
r
ed
icatin
g
th
e
m
is
s
i
n
g
v
al
u
e
s
an
d
r
ed
u
ce
t
h
e
a
m
o
u
n
t
o
f
d
ata
to
b
e
g
en
er
ated
f
r
o
m
t
h
e
cr
o
w
d
.
Mo
r
eo
v
er
,
it
also
h
elp
s
to
r
ed
u
ce
t
h
e
m
o
n
etar
y
co
s
t
b
y
av
o
id
i
n
g
s
en
d
in
g
m
an
y
r
e
q
u
e
s
t
to
t
h
e
cr
o
w
d
w
o
r
k
er
s
.
I
n
o
t
h
er
ca
s
e,
if
t
h
e
q
u
ali
t
y
o
f
esti
m
ated
v
al
u
es
is
lo
w
,
t
h
en
w
e
m
a
y
r
e
f
er
to
th
e
cr
o
w
d
-
s
o
u
r
ce
d
d
atab
ase
to
o
b
tain
b
etter
r
esu
lts
t
h
at
m
ig
h
t
b
e
w
it
h
in
t
h
e
u
s
er
d
ef
i
n
ed
th
r
esh
o
ld
v
al
u
e.
Hen
ce
,
w
e
ca
n
co
n
cl
u
d
e
th
at
o
u
r
m
o
d
el
h
as
co
n
s
id
er
ed
b
o
th
s
ce
n
ar
io
s
a
n
d
en
s
u
r
in
g
t
h
e
co
s
t a
n
d
th
e
q
u
ali
t
y
o
f
th
e
r
es
u
lt
s
h
av
e
b
ee
n
m
ai
n
tai
n
ed
.
4
.
7
.
An a
cc
ura
t
e
esti
m
a
t
e
v
a
lues
g
ener
a
t
o
r
T
h
is
co
m
p
o
n
e
n
t
i
s
r
e
s
p
o
n
s
ib
l
e
to
g
e
n
er
ate
e
s
ti
m
ated
v
al
u
es
f
o
r
t
h
o
s
e
s
k
y
li
n
e
s
w
it
h
in
co
m
p
lete
d
ata.
T
h
e
p
r
o
c
ess
s
tar
ts
b
y
ev
a
lu
at
in
g
th
e
q
u
alit
y
o
f
t
h
e
es
ti
m
at
ed
v
alu
e
s
th
r
o
u
g
h
co
m
p
ar
i
n
g
th
e
r
elati
v
e
er
r
o
r
b
et
w
ee
n
t
h
e
r
ea
l
m
is
s
in
g
v
al
u
e
an
d
th
e
esti
m
ated
o
n
e
ag
ai
n
s
t
t
h
e
u
s
er
d
ef
i
n
ed
t
h
r
esh
o
ld
v
alu
e.
I
f
th
e
r
elat
iv
e
er
r
o
r
p
r
o
d
u
ce
d
less
th
a
n
th
e
th
r
es
h
o
ld
v
alu
e,
t
h
en
t
h
e
esti
m
ated
v
al
u
es
w
il
l
b
e
ac
ce
p
te
d
f
o
r
r
ep
lace
m
en
t
.
Oth
er
w
i
s
e,
th
e
esti
m
ated
v
al
u
es
w
ill
b
e
ig
n
o
r
ed
an
d
atte
m
p
t
to
ex
p
lo
it
t
h
e
d
ata
in
th
e
cr
o
w
d
to
g
e
n
er
ate
m
o
r
e
ac
cu
r
ate
v
a
lu
e
s
.
W
e
ar
g
u
e
t
h
at
u
ti
lizi
n
g
th
e
lo
ca
l
d
ata
i
n
t
h
e
d
atab
ase
h
elp
s
to
e
s
ti
m
a
te
a
lar
g
e
n
u
m
b
er
o
f
esti
m
ated
v
a
lu
e
s
w
it
h
h
i
g
h
q
u
alit
y
.
T
h
is
is
b
ec
au
s
e
t
h
e
v
alu
e
esti
m
ated
ar
e
g
en
er
ated
b
ase
d
o
n
ex
p
lo
iti
n
g
t
h
e
ex
p
licit
r
elatio
n
s
h
ip
b
et
w
ee
n
th
e
d
i
m
e
n
s
io
n
s
an
d
v
ia
id
en
t
i
f
y
in
g
t
h
e
s
tr
e
n
g
th
o
f
t
h
e
p
r
o
b
ab
ilit
y
co
r
r
elatio
n
b
et
w
ee
n
th
e
d
i
m
e
n
s
io
n
s
.
Ho
w
ev
er
,
f
o
r
ce
r
tain
ca
s
es,
p
ar
ticu
lar
l
y
f
o
r
d
ata
w
it
h
h
i
g
h
m
is
s
in
g
r
ate
o
r
th
e
d
ata
is
in
d
ep
en
d
en
t,
t
h
e
n
it
m
i
g
h
t
b
e
ch
alle
n
g
in
g
to
est
i
m
a
te
ac
cu
r
ate
v
alu
e
s
.
T
h
er
ef
o
r
e,
w
e
r
e
q
u
est
t
h
e
cr
o
w
d
t
o
g
en
er
ate
m
o
r
e
p
r
ec
is
e
e
s
ti
m
at
ed
v
alu
e
s
w
h
ic
h
r
es
u
lt
s
i
n
to
a
r
elativ
e
er
r
o
r
b
elo
w
th
e
g
i
v
en
t
h
r
es
h
o
ld
v
al
u
e.
B
asicall
y
,
th
i
s
co
m
p
o
n
e
n
t
h
as
t
w
o
s
u
b
-
co
m
p
o
n
e
n
ts
wh
ich
ar
e
lo
ca
l
esti
m
a
tio
n
a
n
d
cr
o
w
d
-
s
o
u
r
cin
g
esti
m
atio
n
.
T
h
ese
s
u
b
-
co
m
p
o
n
en
ts
ar
e
f
u
r
t
h
er
ex
p
lai
n
ed
in
t
h
e
f
o
llo
w
in
g
.
4
.
8
.
Sk
y
lin
e
identif
ier
T
h
is
co
m
p
o
n
en
t
is
th
e
last
co
m
p
o
n
en
t
i
n
t
h
e
p
r
o
p
o
s
ed
f
r
am
e
w
o
r
k
w
h
ic
h
ai
m
s
to
id
en
ti
f
y
th
e
f
i
n
a
l
s
k
y
li
n
es
o
f
t
h
e
d
atab
ase
an
d
g
et
th
e
q
u
er
y
r
e
s
u
l
t.
T
h
e
p
air
w
i
s
e
co
m
p
ar
is
o
n
p
r
o
ce
s
s
w
ill
b
e
ap
p
lied
o
n
f
in
al
co
m
p
lete
d
atab
ase
to
g
et
s
k
y
l
in
es.
T
h
is
h
elp
s
u
s
er
s
i
n
s
e
lec
tin
g
t
h
e
r
elev
a
n
t
s
k
y
li
n
es
f
r
o
m
s
e
v
er
al
ca
n
d
id
ate
s
k
y
li
n
es.
5.
CO
NCLU
SI
O
N
Sk
y
li
n
e
q
u
er
y
i
s
th
e
p
r
o
ce
s
s
o
f
id
en
ti
f
y
in
g
t
h
e
n
o
n
-
d
o
m
in
a
ted
tu
p
les
in
t
h
e
d
atab
ase
an
d
r
etu
r
n
i
n
g
th
e
m
to
t
h
e
en
d
u
s
er
b
ased
o
n
th
e
u
s
er
g
i
v
e
n
p
r
ef
er
en
ce
.
I
t
is
o
n
e
o
f
th
e
m
o
s
t
p
r
ed
o
m
i
n
an
t
ty
p
e
o
f
p
r
ef
er
en
c
e
q
u
er
ies
th
at
h
a
v
e
b
ee
n
in
tr
o
d
u
ce
d
in
to
th
e
d
atab
ase
i
n
th
e
last
d
ec
ad
e.
T
h
is
p
ap
er
atte
m
p
ts
to
in
tr
o
d
u
ce
a
m
o
d
el
to
p
r
o
ce
s
s
s
k
y
li
n
e
q
u
er
ies
in
cr
o
w
d
-
s
o
u
r
ci
n
g
d
atab
ases
w
it
h
p
ar
tiall
y
co
m
p
let
e
d
ata.
T
h
e
m
o
d
el
d
escr
ib
es
th
e
n
ec
e
s
s
ar
y
co
m
p
o
n
en
t
s
to
p
r
o
ce
s
s
s
k
y
li
n
e
q
u
e
r
y
a
n
d
r
etu
r
n
s
k
y
li
n
es
o
f
th
e
d
atab
ase
w
it
h
th
e
in
te
n
tio
n
o
f
r
ed
u
c
in
g
t
h
e
n
u
m
b
er
o
f
p
air
w
i
s
e
co
m
p
ar
i
s
o
n
s
b
et
w
ee
n
t
u
p
les
w
h
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Facto
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FR
GS
1
5
-
205
-
0
4
9
1
,
Min
is
tr
y
o
f
E
d
u
ca
t
io
n
,
Ma
la
y
s
ia
.
RE
F
E
R
E
NC
E
S
[1
]
A
.
P
a
ra
m
e
s
w
a
ra
n
,
a
n
d
N.
P
o
ly
z
o
ti
s,
“
A
n
swe
rin
g
q
u
e
rie
s
u
si
n
g
h
u
ma
n
s,
a
lg
o
rith
ms
a
n
d
d
a
t
a
b
a
se
s,”
P
re
se
n
ted
a
t
th
e
5
t
h
Bien
n
ial
C
o
n
f
e
re
n
c
e
o
n
In
n
o
v
a
ti
v
e
Da
ta S
y
ste
m
s R
e
se
a
r
c
h
(CIDR’1
1
),
A
silo
m
a
r,
Ca
li
f
o
rn
ia,US
A
,
2
0
1
1
.
[2
]
G
.
L
i,
J.
Wan
g
,
Y.
Zh
e
n
g
,
a
n
d
M
.
J.
F
ra
n
k
li
n
,
”
Cro
w
d
so
u
rc
e
d
d
a
ta
m
a
n
a
g
e
m
e
n
t:
A
su
rv
e
y
,
”
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
Kn
o
wled
g
e
a
n
d
D
a
ta
En
g
i
n
e
e
rin
g
,
2
8
(
9
),
2
0
1
6
,
p
p
.
2
2
9
6
-
2
3
1
9
.
[3
]
G
.
W
o
lf
,
A
.
Ka
l
a
v
a
g
a
tt
u
,
H.
Kh
a
tri
,
R.
Ba
lak
rish
n
a
n
,
B.
Ch
o
k
sh
i,
J.
F
a
n
,
a
n
d
S
.
Ka
m
b
h
a
m
p
a
ti
,
“
Q
u
e
ry
p
ro
c
e
ss
in
g
o
v
e
r
in
c
o
m
p
lete
a
u
to
n
o
m
o
u
s
d
a
tab
a
se
s:
q
u
e
ry
r
e
w
rit
in
g
u
sin
g
lea
rn
e
d
d
a
ta
d
e
p
e
n
d
e
n
c
ies
,
”
VL
DB
J
o
u
rn
a
l
-
T
h
e
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
n
Ver
y
L
a
rg
e
Da
ta
Ba
se
s
,
1
8
(
5
),
2
0
0
9
,
p
p
.
1
6
7
-
1
1
9
0
.
[4
]
M
.
E.
Kh
a
lef
a
,
M
.
F
.
M
o
k
b
e
l
a
n
d
J.
J.
L
e
v
a
n
d
o
sk
i,
“
S
k
y
li
n
e
q
u
e
ry
p
ro
c
e
ss
in
g
fo
r
in
c
o
m
p
lete
d
a
t
a
,
”
t
h
e
2
4
t
h
In
tern
a
ti
o
n
a
l
C
o
n
f
e
re
n
c
e
o
n
Da
ta
En
g
in
e
e
rin
g
(ICDE),
Ca
n
c
u
n
,
M
e
x
ico
,
2
0
0
8
,
p
p
.
5
5
6
-
5
6
5
.
[5
]
M
.
J.
F
ra
n
k
li
n
,
D.
Ko
ss
m
a
n
n
,
T
.
Kra
sk
a
,
S
.
Ra
m
e
sh
a
n
d
R.
Xin
,
“
Cro
w
d
DB:
a
n
swe
rin
g
q
u
e
rie
s
wit
h
c
ro
wd
so
u
rc
in
g
,
”
A
CM
S
IG
M
OD
In
tern
a
ti
o
n
a
l
Co
n
f
e
re
n
c
e
o
n
M
a
n
a
g
e
m
e
n
t
o
f
d
a
ta
S
I
G
M
OD
’1
1
,
A
th
e
n
s,
G
re
e
c
e
,
2
0
1
1
,
p
p
.
6
1
–
7
2
.
[6
]
A
.
M
a
r
c
u
s,
E.
W
u
,
D.
R.
Ka
r
g
e
r
,
S
.
M
a
d
d
e
n
a
n
d
R.
C.
M
il
ler,
“
Cro
wd
so
u
rc
e
d
d
a
ta
b
a
se
s:
q
u
e
ry
p
ro
c
e
ss
in
g
wit
h
p
e
o
p
le,
”
th
e
5
t
h
Bien
n
ial
Co
n
f
e
re
n
c
e
o
n
In
n
o
v
a
ti
v
e
Da
ta S
y
ste
m
s
Re
se
a
rc
h
(CIDR
'
1
1
),
A
silo
m
a
r,
C
a
li
f
o
rn
ia,
USA
,
2
0
1
1
,
p
p
.
2
1
1
-
2
1
4
.
[7
]
H.
P
a
rk
,
H.
G
a
rc
ia
-
M
o
li
n
a
,
R.
P
a
n
g
,
N.
P
o
ly
z
o
ti
s,
A
.
P
a
ra
m
e
s
w
a
ra
n
,
a
n
d
J.
W
id
o
m
,
“
De
c
o
:
A
sy
ste
m
fo
r
d
e
c
la
ra
ti
v
e
c
ro
wd
s
o
u
rc
in
g
,
”
th
e
V
L
DB E
n
d
o
wm
e
n
t,
5
(1
2
),
Ista
n
b
u
l,
T
u
rk
e
y
,
2
0
1
2
,
p
p
.
1
9
9
0
-
1
9
9
3
.
[8
]
C.
L
o
f
i,
K.
El
M
a
a
rr
y
a
n
d
WT
.
Ba
lk
e
,
“
S
k
y
li
n
e
q
u
e
rie
s
o
v
e
r
in
c
o
mp
lete
d
a
t
a
-
e
rr
o
r
mo
d
e
ls
fo
r
f
o
c
u
se
d
c
ro
wd
-
so
u
rc
in
g
,
”
t
h
e
3
2
t
h
In
ter
n
a
ti
o
n
a
l
Co
n
f
e
re
n
c
e
o
n
Co
n
c
e
p
tu
a
l
M
o
d
e
l
in
g
,
Ho
n
g
K
o
n
g
,
C
h
in
a
,
2
0
1
3
,
p
p
.
2
9
8
-
3
1
2
.
[9
]
C.
L
o
f
i,
K.
El
M
a
a
rr
y
a
n
d
WT
.
Ba
lk
e
,
“
S
k
y
li
n
e
q
u
e
rie
s
in
c
ro
wd
-
e
n
a
b
led
d
a
ta
b
a
se
s,”
t
h
e
1
6
t
h
In
tern
a
ti
o
n
a
l
Co
n
f
e
re
n
c
e
o
n
Ex
ten
d
in
g
Da
tab
a
se
T
e
c
h
n
o
l
o
g
y
EDB
T
/ICD
T
'
1
3
,
G
e
n
o
a
,
Ital
y
,
2
0
1
3
,
p
p
.
4
6
5
-
4
7
6
.
[1
0
]
K.
El
M
a
a
rr
y
,
C.
L
o
f
i,
a
n
d
W
T
.
Ba
lk
,
“
Cro
w
d
so
u
rc
in
g
f
o
r
q
u
e
r
y
p
ro
c
e
ss
in
g
o
n
w
e
b
d
a
ta:
a
c
a
s
e
stu
d
y
o
n
th
e
sk
y
li
n
e
o
p
e
ra
to
r,
”
J
o
u
rn
a
l
o
f
Co
mp
u
ti
n
g
a
n
d
I
n
fo
rm
a
ti
o
n
T
e
c
h
n
o
l
o
g
y
–
CIT
,
2
3
(
1
),
2
0
1
5
,
p
p
.
4
3
-
60.
[1
1
]
J.
L
e
e
,
D.
L
e
e
,
a
n
d
S
.
W
.
Kim
,
“
Cro
wd
S
k
y
:
sk
y
li
n
e
c
o
mp
u
t
a
ti
o
n
w
it
h
c
ro
wd
so
u
rc
in
g
,
”
th
e
1
9
th
I
n
tern
a
ti
o
n
a
l
Co
n
f
e
re
n
c
e
o
n
Ex
ten
d
in
g
Da
tab
a
se
T
e
c
h
n
o
l
o
g
y
(EDB
T
),
Bo
rd
e
a
u
x
,
F
ra
n
c
e
,
2
0
1
6
,
p
p
.
1
2
5
-
1
3
6
.
[1
2
]
M
.
A
.
S
o
li
m
a
n
,
I.
F
.
Ily
a
s
a
n
d
S
.
Be
n
-
Da
v
id
,
“
S
u
p
p
o
r
ti
n
g
ra
n
k
in
g
q
u
e
ries
o
n
u
n
c
e
rtain
a
n
d
in
c
o
m
p
lete
d
a
ta,”
th
e
Ver
y
L
a
rg
e
Da
t
a
b
a
se
J
o
u
rn
a
l,
VL
DB
,
1
9
(4
),
2
0
1
0
,
p
p
.
4
7
7
-
5
0
1
.
[1
3
]
L
.
W
a
n
g
a
n
d
R.
Jo
n
e
s,
“
Big
d
a
ta
a
n
a
ly
ti
c
s
f
o
r
d
isp
a
ra
te
d
a
ta,”
Ame
ric
a
n
J
o
u
rn
a
l
o
f
I
n
telli
g
e
n
t
S
y
ste
ms
,
7
(2
)
,
2
0
1
7
,
p
p
.
3
9
-
4
6
.
[1
4
]
A
.
A
.
A
l
w
a
n
,
H.
Ib
ra
h
im
,
N.
I.
U
d
z
ir
a
n
d
F
.
S
id
i
,
“
Esti
m
a
ti
n
g
mis
sin
g
v
a
lu
e
s
o
f
sk
y
li
n
e
s
in
i
n
c
o
mp
l
e
te
d
a
ta
b
a
se
,
”
th
e
T
h
e
S
e
c
o
n
d
I
n
tern
a
ti
o
n
a
l
C
o
n
f
e
re
n
c
e
o
n
Dig
it
a
l
En
ter
p
rise
a
n
d
I
n
f
o
rm
a
ti
o
n
S
y
ste
m
s
(DEI
S
2
0
1
3
)
,
K
u
a
l
a
L
u
m
p
u
r,
M
a
lay
sia
,
2
0
1
3
,
p
p
.
2
2
0
-
2
2
9
.
[1
5
]
S
.
Bo
rz
so
n
y
,
D.
Ko
ss
m
a
n
n
a
n
d
K.
S
to
c
k
e
r,
“
T
h
e
sk
y
li
n
e
o
p
e
ra
t
o
r
,
”
th
e
1
7
th
In
tern
a
ti
o
n
a
l
Co
n
f
e
re
n
c
e
o
n
Da
ta
En
g
in
e
e
rin
g
(ICDE0
1
),
He
id
e
l
b
e
r
g
,
G
e
r
m
a
n
y
,
v
o
l.
3
8
9
6
,
2
0
0
1
,
p
p
.
4
2
1
-
4
3
0
.
[1
6
]
D.
k
o
ss
m
a
n
n
,
F
.
Ra
m
s
a
k
a
n
d
S
.
R
o
st,
“
S
h
o
o
t
in
g
sta
rs
in
th
e
S
k
y
:
a
n
o
n
li
n
e
a
l
g
o
rit
h
m
f
o
r
sk
y
li
n
e
q
u
e
rie
s,”
th
e
2
8
t
h
In
tern
a
ti
o
n
a
l
C
o
n
f
e
re
n
c
e
o
n
V
e
ry
L
a
rg
e
Da
ta Bas
e
s (V
L
DB’0
2
),
H
o
n
g
Ko
n
g
,
Ch
i
n
a
,
2
0
0
2
,
p
p
.
2
7
5
-
2
8
6
.
[1
7
]
J.
Ch
o
m
ick
i,
P
.
G
o
d
f
re
y
,
J.
G
r
y
z
a
n
d
D.
L
ian
g
,
“
S
k
y
li
n
e
wit
h
p
re
so
rtin
g
,
”
th
e
1
9
t
h
In
ter
n
a
ti
o
n
a
l
Co
n
f
e
re
n
c
e
o
n
Da
ta E
n
g
in
e
e
rin
g
(ICDE0
3
)
,
Ba
n
g
a
lo
re
,
In
d
ia,
2
0
0
3
,
p
p
.
7
1
7
-
8
1
6
.
[1
8
]
D.
P
a
p
a
d
ias
,
Y.
T
a
o
,
G
.
F
u
a
n
d
B.
S
e
e
g
e
r,
“
An
o
p
ti
ma
l
a
n
d
p
r
o
g
re
ss
ive
a
lg
o
rith
m
fo
r
sk
y
li
n
e
q
u
e
rie
s,”
A
CM
Co
n
f
e
re
n
c
e
o
n
th
e
M
a
n
a
g
e
m
e
n
t
o
f
Da
ta (S
I
G
M
OD
),
S
a
n
Die
g
o
,
Ca
li
f
o
rn
ia,
USA
,
2
0
0
3
,
p
p
.
4
4
3
-
4
5
4
.
[1
9
]
P
.
G
o
d
f
re
y
,
R.
S
h
ip
ley
a
n
d
J.
G
r
y
z
,
“
M
a
x
ima
l
v
e
c
to
r
c
o
mp
u
ta
ti
o
n
i
n
la
r
g
e
d
a
t
a
se
ts,”
th
e
3
1
st
In
tern
a
ti
o
n
a
l
Co
n
f
e
re
n
c
e
o
n
V
e
ry
L
a
rg
e
Da
ta
Ba
se
s (V
L
DB’0
5
),
T
ro
n
d
h
e
im
,
No
rw
a
y
,
2
0
0
5
,
p
p
.
2
2
9
-
2
4
0
.
[2
0
]
I.
Ba
rto
li
n
i
,
P
.
Ciac
c
ia
a
n
d
M
.
P
a
tella,
“
S
a
L
S
a
:
c
o
mp
u
ti
n
g
th
e
sk
y
li
n
e
wit
h
o
u
t
sc
a
n
n
in
g
t
h
e
wh
o
le
sk
y
,
”
th
e
1
5
t
h
In
tern
a
ti
o
n
a
l
Co
n
f
e
re
n
c
e
o
n
In
fo
rm
a
ti
o
n
a
n
d
Kn
o
w
led
g
e
M
a
n
a
g
e
m
e
n
t
(ICIKM
0
6
),
A
rli
n
g
to
n
,
V
irg
in
ia,
USA
,
2
0
0
6
,
p
p
.
4
0
5
-
4
1
4
.
[2
1
]
M
.
L
.
M
o
rten
se
n
,
S
.
C
h
e
ste
r,
I.
A
s
se
n
t
a
n
d
M
.
M
a
g
n
a
n
i,
“
Ef
fi
c
i
e
n
t
c
a
c
h
i
n
g
f
o
r
c
o
n
str
a
in
e
d
sk
y
li
n
e
q
u
e
rie
s,”
t
h
e
1
8
t
h
In
tern
a
ti
o
n
a
l
Co
n
f
e
re
n
c
e
o
n
Ex
ten
d
in
g
Da
tab
a
se
T
e
c
h
n
o
lo
g
y
(
EDBT
),
2
0
1
5
,
p
p
.
3
3
7
-
3
4
8
.
[2
2
]
M
.
S
.
A
re
f
in
a
n
d
Y.
M
o
rim
o
to
,
“
S
k
y
li
n
e
se
ts
q
u
e
ries
f
o
r
in
c
o
m
p
lete
d
a
ta,”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
C
o
mp
u
ter
S
c
ien
c
e
&
In
fo
rm
a
ti
o
n
T
e
c
h
n
o
l
o
g
y
,
4
(5
),
2
0
1
2
,
p
p
.
6
7
-
8
0
.
[2
3
]
R.
Bh
a
ru
k
a
a
n
d
S
.
P
.
Ku
m
a
r,
“
Fi
n
d
i
n
g
sk
y
li
n
e
s
fo
r
in
c
o
m
p
lete
d
a
t
a
,
”
th
e
2
4
t
h
A
u
stra
las
ian
Da
tab
a
se
Co
n
f
e
re
n
c
e
,
A
d
e
laid
e
,
A
u
stra
li
a
,
v
o
l.
1
3
7
,
2
0
1
3
,
p
p
.
1
0
9
-
1
1
7
.
[2
4
]
A
.
A
.
A
l
w
a
n
,
H.
Ib
ra
h
im
,
N.
I.
Ud
z
ir
a
n
d
F
.
S
id
i
,
“
A
n
e
ff
ici
e
n
t
a
p
p
r
o
a
c
h
f
o
r
p
ro
c
e
ss
in
g
sk
y
li
n
e
q
u
e
ries
in
in
c
o
m
p
lete
m
u
lt
id
im
e
n
sio
n
a
l
d
a
tab
a
se
,
”
Ara
b
i
a
n
J
o
u
rn
a
l
f
o
r S
c
ie
n
c
e
a
n
d
En
g
i
n
e
e
rin
g
,
4
1
(
8
),
2
0
1
6
,
p
p
.
2
9
2
7
-
2
9
4
3
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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5
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l.
10
,
No
.
2
,
Ma
y
2
0
1
8
:
7
9
8
–
8
0
6
806
[2
5
]
E.
D.
Dif
a
ll
a
h
,
M
.
Ca
tas
ta,
G
.
De
m
a
rti
n
i,
G
.
P
.
I
p
e
iro
ti
s
a
n
d
P
.
Cu
d
ré
-
M
a
u
r
o
u
x
,
“
T
h
e
d
y
n
a
mic
s
o
f
M
icr
o
-
T
a
sk
c
ro
wd
so
u
rc
in
g
,
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t
h
e
2
4
t
h
In
ter
n
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ti
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l
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W
id
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re
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e
(W
WW
2
0
1
5
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l
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n
c
e
,
Italy
,
2
0
1
5
,
p
p
.
238
-
2
4
7
.
[2
6
]
G
.
Ca
n
a
h
u
a
te,
M
.
G
ib
a
s
a
n
d
H.
F
e
rh
a
to
sm
a
n
o
g
lu
,
“
In
d
e
x
in
g
in
c
o
mp
lete
d
a
ta
b
a
se
s,”
t
h
e
1
0
th
In
tern
a
t
io
n
a
l
Co
n
f
e
re
n
c
e
o
n
Ex
ten
d
in
g
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tab
a
se
T
e
c
h
n
o
l
o
g
y
(EDB
T
),
M
u
n
ich
,
G
e
r
m
a
n
y
,
2
0
0
6
,
p
p
.
8
8
4
-
9
0
1
.
[2
7
]
Y.
G
u
lza
r,
A
.
A
.
A
l
w
a
n
,
N.
S
a
ll
e
h
,
I.
F
.
A
l
-
S
h
a
ik
h
li
a
n
d
S
.
I.
M
a
iraj
A
l
v
i,
“
A
Fra
me
wo
rk
fo
r
Ev
a
lu
a
ti
n
g
S
k
y
li
n
e
Qu
e
rie
s o
v
e
r In
c
o
mp
lete
Da
t
a
,
”
P
r
o
c
e
d
ia Co
m
p
u
ter S
c
ien
c
e
,
9
4
,
2
0
1
6
,
p
p
.
1
9
1
-
1
9
8
.
[2
8
]
Y.
G
u
lza
r,
A
.
A
.
A
l
wa
n
,
N.
S
a
ll
e
h
,
a
n
d
I.
F
.
A
l
-
S
h
a
ik
h
li
,
“
S
k
y
li
n
e
q
u
e
ry
p
ro
c
e
ss
in
g
fo
r
in
c
o
mp
let
e
d
a
ta
i
n
c
lo
u
d
e
n
v
iro
n
me
n
t
i
n
Z
u
li
k
h
a
,
J
.
&
N.
H.
Z
a
k
a
ri
a
(
Ed
s.),
”
th
e
6
th
I
n
tern
a
ti
o
n
a
l
Co
n
f
e
re
n
c
e
o
n
Co
m
p
u
ti
n
g
&
In
f
o
r
m
a
ti
c
s,
(ICOCI),
Ku
a
la L
u
m
p
u
r,
2
0
1
7
,
p
p
.
5
6
7
-
5
7
6
.
[2
9
]
J.
Qiu
,
Q.
W
u
,
G
.
Din
g
,
Y.
X
u
,
a
n
d
S
.
F
e
n
g
,
“
A
su
rv
e
y
o
f
m
a
c
h
in
e
lea
rn
i
n
g
f
o
r
b
ig
d
a
ta
p
ro
c
e
ss
i
n
g
,
”
EURA
S
IP
J
o
u
rn
a
l
o
n
A
d
v
a
n
c
e
s in
S
ig
n
a
l
Pro
c
e
ss
in
g
,
2
0
1
6
,
DO
I
1
0
.
1
1
8
6
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3
6
3
4
-
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6
-
0
3
5
5
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B
I
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S
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h
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n
tern
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ti
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n
a
l
Isla
m
ic
Un
iv
e
rsit
y
M
a
la
y
sia
,
IIUM
,
M
a
la
y
sia
.
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h
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e
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S
c
.
a
n
d
M
.
S
c
.
d
e
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re
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s
in
Co
m
p
u
ter
S
c
ien
c
e
f
ro
m
T
rip
o
li
Un
iv
e
rsity
,
L
ib
y
a
in
2
0
0
4
a
n
d
2
0
0
9
re
sp
e
c
ti
v
e
l
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re
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ro
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in
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a
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se
sy
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e
m
s a
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li
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e
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.
A
li
A
.
Aw
a
n
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u
rre
n
tl
y
a
n
a
ss
istan
t
p
ro
f
e
ss
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r
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t
Ku
ll
iy
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h
(F
a
c
u
lt
y
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f
In
f
o
r
m
a
ti
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T
e
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y
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In
tern
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l
Isla
m
ic
Un
iv
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a
la
y
sia
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),
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a
la
y
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.
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re
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iv
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h
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ste
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h
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i
n
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S
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e
(2
0
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ro
m
Un
iv
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P
u
tra
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a
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),
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a
lay
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.
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re
se
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re
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ries
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ro
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n
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se
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ss
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ti
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iza
ti
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n
a
n
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m
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a
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ra
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a
ti
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se
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ial
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e
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s
(
L
BS
N),
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c
o
m
m
e
n
d
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ti
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n
d
d
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ra
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tai
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e
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is P
h
D
in
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p
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ter
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e
in
2
0
1
0
f
ro
m
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sit
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V
irg
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T
a
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a
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S
p
a
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P
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D
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M
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2
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1
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ro
m
N
a
ti
o
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a
l
Un
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y
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f
Uz
b
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k
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w
a
s
a
p
o
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ra
d
u
a
te
re
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a
rc
h
e
r
in
Un
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P
u
tra
M
a
lay
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f
ro
m
2
0
0
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to
2
0
1
2
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is
a
f
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m
b
e
r
a
t
De
p
a
rtme
n
t
o
f
Co
m
p
u
ter
S
c
ien
c
e
,
F
a
c
u
lt
y
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f
In
f
o
rm
a
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a
n
d
Co
m
m
u
n
ica
ti
o
n
T
e
c
h
n
o
lo
g
y
,
In
tern
a
ti
o
n
a
l
Isla
m
ic
Un
iv
e
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y
M
a
la
y
sia
sin
c
e
2
0
1
2
.
His
re
se
a
r
c
h
in
tere
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c
lu
d
e
g
ra
p
h
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e
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ry
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late
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rit
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