I
nte
rna
t
io
na
l J
o
urna
l o
f
E
lect
rica
l a
nd
Co
m
pu
t
er
E
ng
ineering
(
I
J
E
CE
)
Vo
l.
15
,
No
.
6
,
Decem
b
er
20
25
,
p
p
.
5
9
7
8
~
5
9
8
5
I
SS
N:
2088
-
8
7
0
8
,
DOI
: 1
0
.
1
1
5
9
1
/ijece.
v
15
i
6
.
pp
5
9
7
8
-
5
9
8
5
5978
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ij
ec
e.
ia
esco
r
e.
co
m
O
n big
da
ta predi
ctive
a
na
ly
tics
-
t
re
nds, persp
ectives,
and
cha
lleng
es
Ya
s
s
ine
B
enla
chm
i,
Abdela
z
iz
E
l Y
a
zidi
,
Ab
da
lla
h Rha
t
t
o
y
,
M
o
ula
y
L
a
hcen
H
a
s
na
o
ui
I
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C
-
TEA
M
,
L
2
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S
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I
-
La
b
o
r
a
t
o
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y
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ES
TM
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M
o
u
l
a
y
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smai
l
U
n
i
v
e
r
s
i
t
y
o
f
M
e
k
n
e
s
,
M
e
k
n
e
s
,
M
o
r
o
c
c
o
Art
icle
I
nfo
AB
S
T
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
J
u
n
1
0
,
2
0
2
3
R
ev
is
ed
J
u
l 2
5
,
2
0
2
5
Acc
ep
ted
Sep
1
6
,
2
0
2
5
Th
e
wo
rl
d
is
e
x
p
e
rie
n
c
in
g
e
x
p
l
o
siv
e
g
ro
wt
h
i
n
n
u
m
e
ro
u
s
se
c
to
rs
su
c
h
a
s
h
e
a
lt
h
c
a
re
,
e
n
g
i
n
e
e
rin
g
,
sc
ien
ti
fi
c
stu
d
ies
,
b
u
sin
e
ss
,
s
o
c
ial
n
e
two
r
k
in
g
.
T
h
is
g
ro
wt
h
is
c
a
u
sin
g
a
n
imm
e
n
se
s
u
rg
e
in
d
a
ta
g
e
n
e
ra
ti
o
n
to
o
.
A
n
d
wit
h
th
e
e
m
e
rg
e
n
c
e
o
f
tec
h
n
o
lo
g
ies
li
k
e
i
n
tern
e
t
o
f
t
h
in
g
s
(
I
o
T
)
,
M
o
b
il
e
,
a
n
d
c
lo
u
d
c
o
m
p
u
ti
n
g
,
th
e
v
o
l
u
m
e
o
f
d
a
ta
b
e
in
g
p
r
o
d
u
c
e
d
is
sk
y
ro
c
k
e
ti
n
g
.
Ho
we
v
e
r,
m
a
k
in
g
se
n
se
o
f
th
is
c
o
l
o
ss
a
l
a
m
o
u
n
t
o
f
d
a
ta
is
a
d
a
u
n
t
in
g
c
h
a
ll
e
n
g
e
.
En
ter
b
ig
d
a
ta
c
o
m
p
u
ti
n
g
,
a
n
e
w
p
a
ra
d
ig
m
th
a
t
b
len
d
s
lar
g
e
d
a
ta
se
ts
with
a
d
v
a
n
c
e
d
a
n
a
l
y
ti
c
a
l
tec
h
n
iq
u
e
s.
Big
d
a
ta
is
c
h
a
ra
c
teriz
e
d
b
y
t
h
e
th
re
e
V'
s:
Vo
lu
m
e
,
v
e
l
o
c
it
y
,
a
n
d
v
a
riet
y
,
a
n
d
re
fe
rs
to
m
a
ss
iv
e
d
a
tas
e
ts.
By
p
ro
c
e
ss
in
g
th
is
d
a
ta,
we
c
a
n
u
n
c
o
v
e
r
n
e
w
o
p
p
o
rtu
n
it
ies
a
n
d
g
a
in
v
a
l
u
a
b
le
i
n
sig
h
ts
in
t
o
m
a
rk
e
t
tren
d
s.
Trad
it
i
o
n
a
l
tec
h
n
i
q
u
e
s
a
re
sim
p
l
y
n
o
t
e
q
u
i
p
p
e
d
t
o
h
a
n
d
le
th
e
sc
a
le
o
f
Big
Da
ta.
Th
e
p
u
rp
o
se
o
f
th
is
a
rti
c
le
is
to
g
a
th
e
r
re
v
iew
s
o
f
v
a
rio
u
s
p
re
d
ictiv
e
a
n
a
l
y
ti
c
s
a
p
p
li
c
a
ti
o
n
s
re
late
d
to
b
i
g
d
a
ta
a
n
d
t
h
e
a
d
v
a
n
tag
e
s
o
f
u
sin
g
b
i
g
d
a
ta an
a
ly
ti
c
s a
c
ro
ss
v
a
rio
u
s
d
e
c
isio
n
-
m
a
k
i
n
g
d
o
m
a
in
s
.
K
ey
w
o
r
d
s
:
B
ig
d
ata
an
aly
tics
Data
m
in
in
g
E
d
u
ca
tio
n
al
b
i
g
d
ata
Pre
d
ictiv
e
an
aly
tics
Pre
d
ictiv
e
m
o
d
els
T
h
is i
s
a
n
o
p
e
n
a
c
c
e
ss
a
rticle
u
n
d
e
r th
e
CC B
Y
-
SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Yass
in
e
B
en
lach
m
u
I
SIC
-
T
E
AM
,
L
2
I
SEI
-
L
ab
o
r
at
o
r
y
-
E
STM
,
Mo
u
lay
I
s
m
ail
Un
iv
er
s
ity
o
f
Me
k
n
es
Me
k
n
es,
Mo
r
o
cc
o
E
m
ail: y
ass
in
0
4
0
@
g
m
ail.
co
m
1.
I
NT
RO
D
UCT
I
O
N
T
o
d
ay
’
s
d
ig
ital
wo
r
ld
c
r
ea
tes
m
ass
iv
e
d
ata
—
s
o
m
e
ea
s
y
to
f
in
d
(
lik
e
wea
th
er
o
r
s
to
ck
i
n
f
o
)
,
s
o
m
e
n
ee
d
in
g
ac
ti
v
e
co
llectio
n
(
lik
e
s
o
cial
m
ed
ia)
.
Data
s
cien
ce
h
elp
s
r
ev
ea
l
h
id
d
en
in
s
ig
h
ts
.
D
esp
ite
p
r
o
g
r
ess
,
b
ig
ch
allen
g
es
r
em
ain
.
T
h
is
s
tu
d
y
u
s
es
b
ib
lio
m
etr
ic
m
eth
o
d
s
to
e
x
p
lo
r
e
h
o
w
b
ig
d
ata
an
d
p
r
ed
i
ctiv
e
an
aly
tics
ca
n
wo
r
k
ac
r
o
s
s
in
d
u
s
tr
ies,
o
f
f
er
in
g
f
o
u
r
k
ey
co
n
tr
i
b
u
tio
n
s
.
I
n
to
d
a
y
’
s
d
ig
ital
wo
r
l
d
,
h
u
g
e
am
o
u
n
ts
o
f
d
ata
a
r
e
g
e
n
er
ate
d
—
s
o
m
e
ea
s
ily
ac
ce
s
s
ib
le
lik
e
s
p
o
r
ts
o
r
wea
th
er
d
ata,
o
th
e
r
s
lik
e
s
o
cial
m
ed
ia
n
ee
d
co
llectio
n
.
Dat
a
s
cien
ce
h
el
p
s
u
n
co
v
er
h
i
d
d
en
in
s
ig
h
ts
.
Desp
ite
ex
is
tin
g
r
esear
ch
,
m
ajo
r
ch
al
len
g
es
r
em
ain
.
T
h
is
s
tu
d
y
u
s
es
b
ib
lio
m
etr
ic
m
et
h
o
d
s
to
e
x
p
lo
r
e
cr
o
s
s
-
s
ec
to
r
in
teg
r
atio
n
o
f
b
ig
d
ata
an
d
p
r
e
d
ictiv
e
an
aly
tics
,
o
f
f
e
r
in
g
f
o
u
r
k
ey
co
n
tr
ib
u
tio
n
s
:
a.
E
x
am
in
e
b
ig
d
ata'
s
f
u
n
d
am
en
t
al
p
r
in
cip
les
a
n
d
co
m
p
le
x
ities
,
en
co
m
p
ass
in
g
its
co
r
e
co
m
p
o
n
en
ts
,
d
ef
i
n
in
g
ch
ar
ac
ter
is
tics
,
an
d
th
e
latest tec
h
n
o
lo
g
ical
ad
v
an
ce
m
en
ts
th
at
en
ab
le
its
ex
is
ten
ce
.
b.
E
x
p
lo
r
e
r
ea
l
-
wo
r
ld
ex
am
p
les
o
f
th
e
s
u
cc
ess
f
u
l
im
p
lem
en
tatio
n
o
f
b
ig
d
ata
p
r
e
d
ictiv
e
an
a
ly
s
is
in
v
ar
io
u
s
in
d
u
s
tr
ies s
u
ch
as h
ea
lth
ca
r
e,
f
in
an
ce
,
an
d
m
ar
k
etin
g
.
c.
Dee
p
en
o
u
r
u
n
d
er
s
tan
d
in
g
ab
o
u
t p
o
ten
tial c
h
allen
g
es a
n
d
s
u
g
g
est wa
y
s
to
o
v
er
c
o
m
e
th
em
.
W
e
b
ased
th
is
s
tu
d
y
en
tire
l
y
o
n
e
x
is
tin
g
m
ater
ials
lik
e
b
o
o
k
s
,
ac
a
d
em
ic
jo
u
r
n
als,
an
d
Go
o
g
le
Sch
o
lar
ar
ticles.
Ou
r
g
o
al
wa
s
to
b
etter
u
n
d
er
s
tan
d
th
e
to
p
i
c
an
d
p
r
o
v
id
e
u
s
ef
u
l
in
s
ig
h
ts
f
o
r
f
u
tu
r
e
r
esear
c
h
.
T
h
e
p
ap
er
walk
s
th
r
o
u
g
h
o
u
r
ap
p
r
o
ac
h
in
s
ec
tio
n
2
an
d
in
tr
o
d
u
ce
s
a
f
r
esh
way
to
th
in
k
ab
o
u
t
b
ig
d
ata
—
th
e
'
s
u
n
f
lo
wer
m
o
d
el'
as sh
o
wn
in
Fig
u
r
e
1
—
d
esig
n
ed
to
r
ef
lect
to
d
ay
’
s
tech
r
ea
lity
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
On
b
ig
d
a
ta
p
r
ed
ictive
a
n
lytics
-
tr
en
d
s
p
ers
p
ec
tives,
a
n
d
ch
a
llen
g
es
(
Ya
s
s
in
e
B
en
la
ch
mi
)
5979
Fig
u
r
e
1
.
Utilizin
g
th
e
s
u
n
f
lo
wer
m
o
d
el
as a
n
a
n
alo
g
y
to
ex
p
lain
th
e
id
ea
o
f
b
ig
d
ata
[
1
]
2.
M
E
T
H
O
D
T
o
en
s
u
r
e
tr
u
s
two
r
th
y
s
o
u
r
ce
s
,
we
co
m
b
in
ed
two
r
ev
iew
a
p
p
r
o
ac
h
es:
s
y
s
tem
atic
liter
atu
r
e
r
ev
iew
(
SLR)
an
d
n
ar
r
ativ
e
r
ev
iew.
W
e
f
o
cu
s
ed
o
n
to
p
ics
lik
e
“
b
ig
d
ata,
”
“
p
r
ed
ictiv
e
an
aly
tics
,
”
an
d
r
elate
d
ar
ea
s
s
u
ch
as
an
aly
tics
p
er
f
o
r
m
an
ce
,
k
n
o
wled
g
e
m
an
ag
e
m
en
t,
a
n
d
I
o
T
.
Ou
r
p
r
o
ce
s
s
in
v
o
lv
e
d
th
o
r
o
u
g
h
ly
r
ev
iewin
g
r
elev
an
t
s
tu
d
ies
in
a
clea
r
an
d
s
tr
u
ctu
r
ed
way
:
i)
I
n
v
esti
g
ate
v
ar
io
u
s
co
m
b
in
atio
n
s
o
f
r
elev
an
t
k
e
y
wo
r
d
s
;
ii)
I
d
en
tify
an
d
d
if
f
er
en
tiate
a
r
ticles
th
at
co
n
tain
p
er
tin
en
t
k
ey
wo
r
d
p
h
r
ases
in
b
o
th
th
e
titl
e
an
d
b
o
d
y
o
f
th
e
d
o
cu
m
e
n
t
;
iii)
E
x
clu
d
e
ar
ticle
s
th
at
m
ay
co
n
tain
r
ele
v
an
t
k
ey
wo
r
d
s
b
u
t
a
r
e
n
o
t
s
u
b
s
tan
ti
ally
r
elate
d
to
th
e
f
ield
o
f
b
ig
d
ata
o
r
p
r
e
d
ictiv
e
an
aly
tics
;
an
d
iv
)
Ag
g
r
eg
ate
r
elev
an
t
r
esear
ch
p
ap
e
r
s
.
Ou
r
p
u
r
s
u
it
f
o
r
r
ele
v
an
t
liter
atu
r
e
was
ex
ten
s
iv
e
an
d
ex
h
au
s
tiv
e,
en
c
o
m
p
ass
in
g
a
t
h
o
r
o
u
g
h
e
x
p
lo
r
atio
n
o
f
s
ev
e
r
al
d
ig
ital
d
atab
ases
,
s
u
ch
as:
Scien
ce
Dir
ec
t
(
h
ttp
s
:
//w
w
w
.
s
cien
ce
d
ir
ec
t.c
o
m/
)
,
R
esear
ch
Gate
(
h
ttp
s
:
//w
w
w
.
r
e
s
ea
r
ch
g
a
te.
n
et/
)
,
a
n
d
I
E
E
E
Xp
lo
r
e
(
h
ttp
s
:
//ieeex
p
lo
r
e.
ieee
.
o
r
g
/
).
W
e
ca
r
ef
u
lly
f
ilter
ed
th
e
co
llected
ar
ticles,
r
em
o
v
in
g
an
y
th
at
wer
e
n
o
t
r
elev
an
t
—
lik
e
ed
ito
r
ials
,
b
o
o
k
r
e
v
iews,
o
r
in
tr
o
d
u
cto
r
y
p
ap
er
s
th
at
d
id
n
o
t
f
o
cu
s
o
n
b
i
g
d
ata
o
r
p
r
e
d
ictiv
e
an
aly
ti
cs.
W
e
u
s
ed
th
e
PR
I
S
MA
f
r
am
ewo
r
k
,
a
well
-
k
n
o
w
n
m
eth
o
d
f
o
r
s
y
s
tem
atic
r
ev
iews,
to
id
en
tify
th
e
m
o
s
t su
itab
le
s
tu
d
ies f
o
r
o
u
r
r
esear
ch
.
Fig
u
r
e
2
illu
s
tr
ates th
e
PR
I
SMA
m
o
d
el
in
ac
tio
n
.
Fig
u
r
e
2
.
PR
I
SMA
m
o
d
el
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
15
,
No
.
6
,
Decem
b
e
r
20
25
:
5
9
7
8
-
5
9
8
5
5980
3.
B
I
G
DA
T
A
AND
P
R
E
DI
CT
I
VE
AN
AL
Y
SI
S
–
CO
RE
C
O
NCEPT
S
3
.
1
.
F
ro
m
da
t
a
t
o
big
da
t
a
Data
co
m
es
in
two
m
ain
ty
p
es:
s
tr
u
ctu
r
ed
(
lik
e
ce
n
s
u
s
s
tats
,
f
in
an
cial
r
ec
o
r
d
s
,
o
r
s
u
r
v
ey
s
)
an
d
u
n
s
tr
u
ctu
r
ed
(
lik
e
web
co
n
te
n
t
o
r
XM
L
f
iles
)
.
O
n
ly
ab
o
u
t 5
%
o
f
d
ata
is
s
tr
u
ctu
r
e
d
[
2
]
,
wh
ile
th
e
r
est
is
m
ess
y
an
d
h
ar
d
to
p
r
o
ce
s
s
—
wh
at
we
n
o
w
ca
ll
b
ig
d
ata.
B
ig
d
ata
is
k
n
o
wn
f
o
r
its
3
Vs:
v
o
lu
m
e,
v
elo
city
,
an
d
v
ar
iety
—
f
ir
s
t
d
escr
ib
ed
b
y
L
an
ey
in
2
0
0
1
[
3
]
,
[
4
]
.
L
ate
r
,
v
er
ac
ity
an
d
v
alu
e
wer
e
ad
d
ed
to
f
o
r
m
th
e
5
V
m
o
d
el
[
4
]
,
[
5
]
.
So
m
e
ex
p
er
ts
ar
e
n
o
w
ex
p
lo
r
i
n
g
id
ea
s
b
ey
o
n
d
th
e
5
Vs
[
6
]
.
T
h
e
B
ig
s
f
r
am
ewo
r
k
ex
p
a
n
d
s
th
e
5
Vs
o
f
b
ig
d
ata
b
y
ad
d
in
g
t
ec
h
n
o
lo
g
y
(
in
tellig
en
ce
,
an
al
y
tics
,
in
f
r
astru
ctu
r
e
)
a
n
d
s
o
cio
-
ec
o
n
o
m
ic
asp
ec
ts
(
s
er
v
ice,
v
alu
e,
m
a
r
k
et)
.
I
t
ca
p
tu
r
es
d
ata
s
ca
le,
s
p
ee
d
,
d
iv
e
r
s
ity
,
tr
u
s
t,
an
d
h
ig
h
lig
h
ts
b
ig
d
ata’
s
im
p
ac
t
o
n
in
n
o
v
atio
n
an
d
g
r
o
wth
.
3
.
2
.
B
ig
da
t
a
a
na
ly
t
ics
B
ig
d
ata
an
aly
tics
in
v
o
lv
es
cu
r
atin
g
an
d
an
al
y
zin
g
lar
g
e
,
co
m
p
lex
d
ata
s
ets
to
d
is
co
v
er
p
atter
n
s
an
d
in
s
ig
h
ts
[
7
]
.
I
t
h
elp
s
o
r
g
an
iz
atio
n
s
m
ak
e
b
etter
d
ec
is
io
n
s
b
y
f
o
cu
s
in
g
o
n
th
e
m
o
s
t
r
el
ev
an
t
d
ata,
d
r
iv
in
g
im
p
ac
tf
u
l o
u
tc
o
m
es.
T
ab
le
1
c
o
m
p
ar
es c
o
n
v
en
tio
n
al
d
ata
an
aly
s
is
with
b
ig
d
ata
an
aly
s
is
.
T
ab
le
1
.
C
o
n
v
en
tio
n
al
d
ata
an
aly
tics
v
s
b
ig
d
ata
an
aly
tics
C
h
a
r
a
c
t
e
r
C
o
n
v
e
n
t
i
o
n
a
l
a
p
p
r
o
a
c
h
B
i
g
d
a
t
a
a
p
p
r
o
a
c
h
A
n
a
l
y
s
i
s m
e
t
h
o
d
H
y
p
o
t
h
e
s
i
s
-
b
a
se
d
M
a
c
h
i
n
e
l
e
a
r
n
i
n
g
P
r
i
mary
g
o
a
l
P
e
r
f
o
r
ma
n
c
e
ma
n
a
g
e
me
n
t
a
n
d
i
n
t
e
r
n
a
l
d
e
c
i
si
o
n
s
u
p
p
o
r
t
d
a
t
a
-
d
r
i
v
e
n
p
r
o
d
u
c
t
s
a
n
d
b
u
si
n
e
ss
p
r
o
c
e
sses
d
r
i
v
e
r
D
a
t
a
t
y
p
e
S
t
r
u
c
t
u
r
e
d
a
n
d
d
e
fi
n
e
d
(
f
o
r
ma
t
t
e
d
i
n
c
o
l
u
m
n
s &
r
o
w
s)
U
n
st
r
u
c
t
u
r
e
d
,
sem
i
s
t
r
u
c
t
u
r
e
d
V
o
l
u
me
1
0
’
s
o
f
TB
o
r
l
e
ss
1
0
0
t
e
r
a
b
y
t
e
s t
o
p
e
t
a
b
y
t
e
s
3
.
3
.
P
re
dict
iv
e
a
na
ly
t
ics
Pre
d
ictiv
e
an
aly
tics
f
o
r
ec
asts
f
u
tu
r
e
o
u
tco
m
es
a
n
d
u
n
co
v
er
s
o
p
p
o
r
tu
n
ities
an
d
r
is
k
s
u
s
in
g
t
ec
h
n
iq
u
es
lik
e
d
ata
m
in
in
g
,
m
o
d
elin
g
,
an
d
m
ac
h
in
e
lear
n
in
g
.
I
t
h
el
p
s
o
r
g
an
izatio
n
s
g
ain
a
co
m
p
etitiv
e
ed
g
e
ac
r
o
s
s
in
d
u
s
tr
ies
[
8
]
.
Un
lik
e
o
th
er
tr
a
d
itio
n
al
B
I
m
eth
o
d
o
lo
g
ies
th
at
f
o
cu
s
o
n
an
aly
zin
g
p
ast
ev
en
t
s
to
m
ak
e
s
en
s
e
o
f
th
e
p
r
esen
t,
p
r
ed
ictiv
e
a
n
aly
ti
cs
is
f
u
tu
r
e
-
f
ac
in
g
an
d
p
r
o
v
id
e
s
o
r
g
an
izatio
n
s
with
th
e
ab
ilit
y
to
m
a
k
e
p
r
o
ac
tiv
e
d
ec
is
io
n
s
,
g
ain
v
alu
a
b
le
in
s
ig
h
ts
,
an
d
s
tay
ah
ea
d
o
f
th
e
c
o
m
p
etitio
n
as sh
o
wn
in
Fig
u
r
e
3
.
Fig
u
r
e
3
.
Of
all
t
h
e
b
u
s
in
ess
in
tellig
en
ce
d
is
cip
lin
es,
p
r
ed
ict
io
n
o
f
f
er
s
th
e
g
r
ea
test
p
o
ten
tial f
o
r
d
eliv
e
r
in
g
b
u
s
in
ess
v
alu
e
b
u
t is also
th
e
m
o
s
t in
tr
icate
to
ex
ec
u
te.
Fu
r
t
h
er
m
o
r
e
,
ea
ch
d
is
cip
lin
e
b
u
ild
s
u
p
o
n
th
e
p
r
ec
ed
in
g
o
n
e,
cu
lm
in
atin
g
in
a
co
m
p
r
e
h
en
s
iv
e,
ad
d
itiv
e
ap
p
r
o
ac
h
to
b
u
s
in
ess
in
tellig
en
ce
,
r
ath
er
th
a
n
an
ex
clu
s
iv
e
o
n
e
4.
T
E
CH
N
I
CA
L
AP
P
L
I
CA
T
I
O
NS F
O
R
T
H
E
B
I
G
DAT
A
P
RE
DIC
T
I
VE
AN
AL
Y
T
I
C
S
E
CO
SYS
T
E
M
T
h
e
p
o
ten
tial
ap
p
licatio
n
a
r
e
as
o
f
b
ig
d
ata
p
r
ed
ictiv
e
an
al
y
tics
ar
e
v
ast
an
d
v
ar
ie
d
,
r
an
g
in
g
f
r
o
m
m
ed
ical
an
d
f
in
an
cial
in
d
u
s
tr
i
es
to
clim
ate
an
d
ag
r
icu
ltu
r
e.
I
n
th
e
m
ed
ical
f
ield
,
AI
-
b
ased
b
ig
d
ata
an
aly
tics
m
o
d
els
ca
n
h
elp
to
p
r
e
d
ict
an
d
d
iag
n
o
s
e
d
is
ea
s
es
m
o
r
e
ac
c
u
r
ately
a
n
d
ef
f
icien
tly
.
Fo
r
in
s
tan
ce
,
th
e
u
s
e
o
f
b
i
g
d
ata
an
aly
tics
ca
n
aid
in
p
r
e
d
ictin
g
th
e
o
n
s
et
o
f
d
is
ea
s
es
a
n
d
ep
id
em
ics,
as
ev
id
en
c
ed
b
y
th
e
ap
p
licatio
n
o
f
Re
p
or
t
in
g:
W
h
a
t
Ha
ppe
n
e
d
(
Que
r
r
y
r
e
po
r
t
i
n
g
a
n
d
s
e
a
r
c
h
t
o
o
l
s
)
Anal
ys
is
:
W
h
y
di
d
i
t
h
a
ppe
n
(
OL
A
P
a
n
d
v
i
s
ul
i
z
a
t
i
o
n
s
t
o
o
l
s
)
M
o
n
i
t
o
r
i
n
g
:
w
h
a
t
s
h
a
p
p
e
n
i
n
g
n
o
w
?
(
D
a
sh
b
o
a
r
d
s
a
n
d
sco
r
e
c
a
r
d
s
)
P
r
e
di
c
t
i
o
n
:
wh
a
t
m
i
gh
t
h
a
ppe
n
?
P
r
e
di
c
t
i
v
e
a
n
a
l
y
s
i
s
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
On
b
ig
d
a
ta
p
r
ed
ictive
a
n
lytics
-
tr
en
d
s
p
ers
p
ec
tives,
a
n
d
ch
a
llen
g
es
(
Ya
s
s
in
e
B
en
la
ch
mi
)
5981
b
ig
d
ata
an
d
ar
tific
ial
in
tellig
en
ce
in
ep
id
e
m
ic
s
u
r
v
eillan
ce
an
d
co
n
tain
m
en
t.
I
n
th
e
f
in
a
n
ce
s
ec
to
r
,
b
ig
d
ata
p
r
ed
ictiv
e
a
n
aly
tics
ca
n
b
e
e
m
p
lo
y
ed
f
o
r
c
r
y
p
t
o
cu
r
r
e
n
cy
p
o
r
tf
o
lio
allo
ca
tio
n
u
s
in
g
h
y
b
r
i
d
an
d
p
r
ed
ictiv
e
b
ig
d
ata
d
ec
is
io
n
s
u
p
p
o
r
t
s
y
s
tem
s
.
Ad
d
itio
n
ally
,
b
ig
d
ata
an
aly
tics
ca
n
aid
in
p
r
ed
ictin
g
cli
m
ate
f
ac
to
r
s
,
wh
ich
ca
n
h
elp
to
in
f
o
r
m
ag
r
icu
ltu
r
al
d
is
aster
m
an
ag
em
en
t
s
tr
at
eg
ies.
B
ig
d
ata
p
r
ed
ictiv
e
an
aly
tics
ca
n
also
b
e
u
tili
ze
d
in
th
e
f
ash
io
n
an
d
ap
p
ar
el
in
d
u
s
tr
y
th
r
o
u
g
h
s
o
cial
m
ed
ia
u
s
er
b
eh
av
io
r
a
n
aly
s
is
.
E
d
u
ca
tio
n
al
in
s
titu
tio
n
s
ca
n
lev
e
r
ag
e
b
ig
d
ata
an
aly
tics
to
m
ak
e
in
f
o
r
m
e
d
p
r
ed
ictio
n
s
a
n
d
ap
p
licatio
n
s
f
o
r
s
tu
d
en
t
s
u
cc
ess
.
Fin
ally
,
b
ig
d
ata
an
aly
tics
h
a
s
p
r
o
m
is
in
g
a
p
p
licatio
n
s
in
p
o
wer
s
y
s
tem
s
b
y
o
p
tim
izin
g
en
er
g
y
co
n
s
u
m
p
tio
n
an
d
r
ed
u
cin
g
c
o
s
ts
.
4
.
1
.
P
re
dict
iv
e
a
na
ly
t
ics in
m
edica
l big
da
t
a
B
ig
d
ata
r
esear
ch
is
v
ital
i
n
h
ea
lth
ca
r
e
d
u
e
to
th
e
m
a
s
s
iv
e
d
ata
g
en
er
ated
an
n
u
ally
,
n
o
w
at
0
.
1
1
5
b
illi
o
n
ter
ab
y
tes
with
2
ex
a
b
y
tes
g
r
o
wth
p
er
y
ea
r
[
9
]
.
T
h
is
en
a
b
les
lar
g
e
-
s
ca
le
p
o
p
u
latio
n
h
ea
lth
an
aly
s
is
to
im
p
r
o
v
e
d
ec
is
io
n
-
m
ak
in
g
.
O
n
e
s
tu
d
y
an
aly
ze
d
3
5
6
,
5
0
7
p
atien
ts
’
d
ata
f
r
o
m
1
9
8
2
-
2
0
1
0
,
r
e
v
ea
lin
g
co
n
s
is
ten
t
p
atter
n
s
u
s
ef
u
l
f
o
r
p
r
ed
ictiv
e
m
o
d
elin
g
[
1
0
]
.
W
ea
r
ab
le
s
m
ar
twatch
es
co
llect
p
h
y
s
io
lo
g
ical
d
ata
lik
e
p
u
ls
e
a
n
d
o
x
y
g
en
lev
els,
wh
ich
ar
e
an
aly
ze
d
f
o
r
m
ed
ic
al
d
iag
n
o
s
is
an
d
war
n
in
g
s
[
1
1
]
.
B
ig
d
ata
an
d
AI
h
av
e
also
p
lay
ed
k
e
y
r
o
les
in
m
an
ag
in
g
c
o
r
o
n
a
v
ir
u
s
d
is
ea
s
e
(
C
OVI
D
-
19
)
b
y
p
r
o
v
id
in
g
in
s
ig
h
ts
an
d
g
u
id
in
g
s
tr
ateg
ies
[
1
2
]
.
Ad
d
itio
n
ally
,
b
ig
d
ata
aid
s
ch
ild
an
d
ad
o
lescen
t
m
en
tal
h
ea
lth
r
esear
c
h
[
1
3
]
an
d
h
elp
s
p
r
e
d
ict
h
ea
lth
r
is
k
s
f
r
o
m
p
esti
cid
e
ex
p
o
s
u
r
e
an
d
ai
r
q
u
ality
i
n
C
h
in
a
[
1
4
]
,
[
1
5
]
.
4
.
2
.
P
re
dict
iv
e
a
na
ly
t
ics in e
du
ca
t
io
na
l big
da
t
a
B
ig
d
ata
is
wid
ely
v
iewe
d
as
u
s
ef
u
l
f
o
r
aid
in
g
ed
u
ca
tio
n
a
l
r
ef
o
r
m
s
at
v
ar
io
u
s
lev
els
o
f
teac
h
in
g
an
d
ad
m
in
is
tr
atio
n
,
an
d
ev
en
r
esear
ch
m
an
ag
em
e
n
t
[
1
6
]
.
I
t
is
s
p
lit
in
to
two
ty
p
es
o
f
an
aly
tical
o
r
ien
tatio
n
s
:
o
n
e
is
d
ata
-
b
ased
an
al
y
s
is
w
h
ich
f
o
c
u
s
es
o
n
r
eso
u
r
ce
s
s
u
ch
as
en
r
o
llm
en
t
an
d
cu
r
r
icu
l
u
m
d
ata,
wh
ile
th
e
o
th
er
is
d
em
a
n
d
-
b
ased
an
aly
s
is
wh
ich
ties
s
tu
d
en
ts
to
th
e
ed
u
ca
tio
n
al
q
u
ality
in
d
icato
r
s
as
illu
s
tr
ated
in
Fig
u
r
e
4
.
Fig
u
r
e
4
.
Data
-
b
ased
an
aly
s
is
o
r
ien
tatio
n
So
m
e
r
esear
ch
er
s
ar
e
in
cr
ea
s
in
g
ly
f
o
c
u
s
ed
o
n
b
ig
d
ata
in
t
h
e
co
n
tex
t
o
f
h
ig
h
er
ed
u
ca
tio
n
an
d
its
im
p
ac
t
o
n
th
e
d
ev
elo
p
m
en
t,
p
l
an
n
in
g
,
ef
f
icien
c
y
o
f
in
s
tr
u
cti
o
n
,
a
n
d
t
h
e
o
v
er
all
q
u
ality
o
f
l
ea
r
n
in
g
.
I
m
p
o
r
tan
t
to
o
ls
s
u
ch
as
lear
n
in
g
an
aly
tics
an
d
ed
u
ca
tio
n
al
d
ata
m
in
in
g
s
er
v
e
f
o
r
f
o
r
ec
asti
n
g
,
class
if
icatio
n
,
an
d
clu
s
ter
in
g
[
1
7
]
.
T
h
e
MA
ST
E
R
to
o
l,
aim
ed
at
p
r
ed
ictin
g
s
tu
d
en
ts
’
ad
ap
tatio
n
is
s
u
e
s
,
h
as
im
p
lem
en
ted
SMOT
E
an
d
p
r
io
r
ity
f
o
r
est
r
eso
u
r
ce
allo
ca
tio
n
tech
n
iq
u
e
s
to
s
tr
ea
m
lin
e
r
eso
u
r
ce
allo
ca
tio
n
[
1
8
]
.
Oth
e
r
s
tu
d
ies
lo
o
k
at
th
e
r
o
le
o
f
b
ig
d
ata
in
o
n
lin
e
e
d
u
ca
tio
n
,
esp
e
cially
d
u
r
in
g
t
h
e
C
OVI
D
-
1
9
p
an
d
em
ic,
f
o
cu
s
in
g
o
n
th
e
in
n
o
v
atio
n
an
d
d
ata
-
d
r
i
v
en
ap
p
r
o
ac
h
es to
teac
h
in
g
[
1
9
]
.
4
.
3
.
P
re
dict
iv
e
a
na
ly
t
ics in so
cia
l m
edia
big
da
t
a
So
cial
m
ed
ia
p
latf
o
r
m
s
g
e
n
er
ate
v
ast
u
n
s
tr
u
ctu
r
ed
d
ata
v
al
u
ab
le
f
o
r
p
r
e
d
ictiv
e
an
aly
s
is
[
2
0
]
,
[
2
1
]
.
Stu
d
ies
u
s
e
th
is
d
ata
to
im
p
r
o
v
e
m
ar
k
etin
g
in
f
ash
io
n
[
2
2
]
,
p
r
e
d
ict
h
ea
lth
ca
r
e
o
u
tco
m
e
s
[
2
3
]
,
an
d
f
o
r
ec
ast
to
u
r
is
m
with
m
ac
h
in
e
lear
n
in
g
[
2
4
]
.
T
h
e
B
D
-
SMAB
m
o
d
el
aid
s
co
m
p
etito
r
an
al
y
s
is
v
ia
s
o
cial
f
ee
d
b
ac
k
[
2
5
]
,
wh
ile
f
u
r
th
er
r
esear
ch
ex
p
lo
r
e
s
s
o
cial
m
ed
ia’
s
b
u
s
in
ess
an
d
ac
ad
em
ic
u
s
es
[
2
6
]
–
[
2
8
]
.
4
.
4
.
P
re
dict
iv
e
a
na
ly
t
ics in e
co
no
m
ic
big
da
t
a
B
ig
d
ata
b
o
o
s
ts
r
eso
u
r
ce
allo
ca
tio
n
,
p
r
o
d
u
ctio
n
ef
f
icien
c
y
,
an
d
s
u
s
tain
ab
le
d
ev
elo
p
m
en
t
,
with
AI
,
clo
u
d
co
m
p
u
tin
g
,
I
o
T
,
an
d
b
lo
ck
ch
ain
p
la
y
in
g
k
ey
r
o
les
[
2
9
]
.
T
h
e
DHM
-
B
DA
m
o
d
el
im
p
r
o
v
es
d
is
aster
m
an
ag
em
en
t
v
ia
b
etter
p
r
e
d
ic
tio
n
an
d
r
is
k
r
e
d
u
ctio
n
[
3
0
]
.
B
ig
d
ata
also
tr
an
s
f
o
r
m
s
d
ig
i
tal
ec
o
n
o
m
ics
an
d
g
u
id
es
m
ac
r
o
ec
o
n
o
m
ic
s
tr
ateg
ies
[
3
1
]
,
wh
ile
en
h
a
n
cin
g
s
a
les
m
an
ag
em
en
t
th
r
o
u
g
h
d
ata
-
d
r
iv
en
co
n
tr
o
l
an
d
p
r
ed
ictiv
e
m
o
d
els to
f
o
s
ter
b
u
s
in
ess
g
r
o
wth
[
3
2
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
15
,
No
.
6
,
Decem
b
e
r
20
25
:
5
9
7
8
-
5
9
8
5
5982
5.
O
P
E
N
CH
AL
L
E
NG
E
S
T
O
T
H
E
M
E
T
AV
E
RS
E
I
M
P
L
E
M
E
N
T
A
T
I
O
N
A
T
A
L
A
RG
E
SCA
L
E
B
ig
d
ata
p
r
ed
ictiv
e
an
aly
s
is
c
an
r
e
v
o
lu
tio
n
ize
h
ea
lth
ca
r
e,
f
in
an
ce
,
ed
u
ca
tio
n
,
a
n
d
m
o
r
e
b
y
o
f
f
er
in
g
b
etter
in
s
ig
h
ts
an
d
p
r
ed
ictio
n
s
.
Ho
wev
er
,
ch
allen
g
es
lik
e
d
ata
q
u
ality
,
p
r
iv
ac
y
,
an
d
th
e
n
ee
d
f
o
r
s
k
illed
ex
p
er
ts
m
u
s
t
b
e
ad
d
r
ess
ed
[
3
3
]
.
T
h
ese
ch
allen
g
es
f
all
in
to
th
r
ee
ca
teg
o
r
ies:
en
s
u
r
in
g
v
alid
d
ata
th
r
o
u
g
h
p
r
o
ce
s
s
in
g
,
ag
g
r
eg
atin
g
d
i
v
er
s
e
d
ata
s
o
u
r
ce
s
,
an
d
o
v
er
co
m
in
g
d
ata
a
v
ailab
ilit
y
is
s
u
es.
Fig
u
r
e
5
s
u
m
m
ar
izes
th
ese
ch
allen
g
es in
b
ig
d
ata
p
r
ed
ictiv
e
an
aly
s
is
.
Fig
u
r
e
5
.
Dif
f
ic
u
lties
en
co
u
n
te
r
ed
in
th
e
a
n
aly
s
is
o
f
lar
g
e
d
at
asets
5
.
1
.
Ana
ly
zing
un
-
s
t
ruct
ured
da
t
a
Ab
o
u
t
9
5
%
o
f
b
ig
d
ata
is
m
ad
e
u
p
o
f
u
n
s
tr
u
ctu
r
e
d
d
a
ta.
Sem
i
-
s
tr
u
ctu
r
ed
d
ata
is
a
ty
p
e
o
f
u
n
s
tr
u
ctu
r
ed
d
ata
th
at
d
o
es
n
o
t
f
o
llo
w
s
tr
ict
f
o
r
m
attin
g
r
u
les.
A
g
o
o
d
ex
am
p
le
o
f
s
em
i
-
s
tr
u
ctu
r
ed
d
ata
is
XM
L
,
a
lan
g
u
ag
e
u
s
ed
to
e
x
ch
an
g
e
d
ata
o
v
er
th
e
web
.
X
ML
d
o
c
u
m
en
ts
co
n
tain
ta
g
s
t
h
at
ca
n
b
e
r
ea
d
b
y
m
ac
h
in
es
an
d
ar
e
d
e
f
in
ed
b
y
th
e
u
s
er
.
So
to
ad
d
r
ess
th
e
u
n
s
t
r
u
ctu
r
ed
d
ata
is
a
b
ig
is
s
u
e
in
b
ig
d
ata
p
r
ed
ictiv
e
an
aly
s
is
.
5
.
2
.
M
a
inta
ini
ng
da
t
a
qu
a
lit
y
T
h
e
q
u
ality
o
f
th
e
d
ata
u
s
ed
f
o
r
an
aly
s
is
is
cr
u
cial
to
th
e
s
u
cc
ess
o
f
b
ig
d
ata
p
r
ed
ictiv
e
an
aly
s
is
[
3
4
]
.
I
n
ac
cu
r
ate
o
r
in
co
m
p
lete
d
ata
ca
n
lead
to
in
co
r
r
ec
t
p
r
ed
ictio
n
s
an
d
ca
n
u
ltima
tely
im
p
ac
t
b
u
s
in
ess
d
ec
is
io
n
s
.
I
t
is
im
p
o
r
tan
t
t
o
e
n
s
u
r
e
t
h
at
th
e
d
ata
is
r
elev
an
t,
ac
cu
r
at
e,
an
d
r
eliab
le.
Ad
d
itio
n
ally
,
d
ata
clea
n
in
g
an
d
p
r
ep
r
o
ce
s
s
in
g
tech
n
iq
u
es sh
o
u
ld
b
e
em
p
l
o
y
ed
t
o
elim
in
ate
a
n
y
ir
r
elev
a
n
t o
r
i
n
co
m
p
lete
d
a
ta.
5
.
3
.
Da
t
a
pro
t
ec
t
io
n a
nd
priv
a
cy
B
ig
d
ata
in
co
r
p
o
r
ates
v
ar
io
u
s
ty
p
es
o
f
d
ata,
i
n
clu
d
in
g
p
e
r
s
o
n
al
in
f
o
r
m
atio
n
,
w
h
ich
m
ay
lead
to
p
r
iv
ac
y
co
n
ce
r
n
s
ev
en
wh
en
an
o
n
y
m
ized
[
3
5
]
.
T
h
er
ef
o
r
e,
p
r
iv
ac
y
p
r
o
tectio
n
is
n
ec
ess
ar
y
f
o
r
o
r
g
a
n
izatio
n
s
,
in
d
iv
id
u
als,
a
n
d
g
o
v
er
n
m
en
ts
,
r
eq
u
ir
in
g
a
co
m
p
r
eh
en
s
iv
e
f
r
am
ewo
r
k
[
3
6
]
.
I
t
is
c
r
u
cial
t
o
in
v
o
l
v
e
t
h
e
p
u
b
lic
an
d
o
th
er
s
tak
eh
o
ld
er
s
i
n
th
e
co
n
v
er
s
atio
n
s
u
r
r
o
u
n
d
in
g
d
a
ta
s
ec
u
r
ity
an
d
p
r
iv
ac
y
to
ad
d
r
ess
co
n
ce
r
n
s
an
d
av
o
id
ex
ce
s
s
iv
ely
r
estrictiv
e
r
eg
u
latio
n
s
.
5
.
4
.
Da
t
a
inte
g
r
a
t
io
n
On
e
o
f
th
e
p
r
im
ar
y
c
h
allen
g
e
s
in
d
ata
in
teg
r
atio
n
is
th
e
is
s
u
e
o
f
d
ata
h
eter
o
g
en
eity
,
wh
i
ch
r
ef
e
r
s
to
th
e
d
if
f
e
r
en
ce
s
in
d
ata
f
o
r
m
at
s
,
s
tr
u
ctu
r
es,
an
d
s
em
a
n
tics
.
T
h
e
in
teg
r
atio
n
o
f
s
u
ch
d
iv
er
s
e
d
ata
s
o
u
r
ce
s
m
ay
lead
to
d
ata
in
co
n
s
is
ten
cy
,
lo
s
s
o
f
in
f
o
r
m
atio
n
,
an
d
in
ac
cu
r
ac
ies
in
an
aly
s
is
r
esu
lt
s
.
T
h
er
ef
o
r
e,
d
ata
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
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&
C
o
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p
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I
SS
N:
2088
-
8
7
0
8
On
b
ig
d
a
ta
p
r
ed
ictive
a
n
lytics
-
tr
en
d
s
p
ers
p
ec
tives,
a
n
d
ch
a
llen
g
es
(
Ya
s
s
in
e
B
en
la
ch
mi
)
5983
in
teg
r
atio
n
r
eq
u
ir
es
s
o
p
h
is
ticated
to
o
ls
an
d
tech
n
iq
u
es,
in
cl
u
d
in
g
d
ata
p
r
ep
r
o
ce
s
s
in
g
,
d
ata
m
ap
p
in
g
,
a
n
d
d
ata
tr
an
s
f
o
r
m
atio
n
.
T
o
ad
d
r
ess
th
e
ch
allen
g
es
o
f
d
ata
in
teg
r
atio
n
in
b
ig
d
ata
an
al
y
s
is
,
s
ev
er
al
s
tu
d
ies
h
av
e
p
r
o
p
o
s
ed
d
if
f
er
en
t
ap
p
r
o
ac
h
es
an
d
f
r
am
ewo
r
k
s
.
Fo
r
in
s
tan
c
e
Ham
m
ad
et
a
l.
[
3
7
]
p
r
o
p
o
s
ed
a
f
r
am
ewo
r
k
th
at
u
tili
ze
s
s
em
an
tic
tech
n
o
lo
g
ies to
in
teg
r
ate
h
eter
o
g
en
e
o
u
s
d
at
a
f
r
o
m
m
u
ltip
le
s
o
u
r
ce
s
.
5
.
5.
Da
t
a
s
a
la
bil
it
y
Scalab
ilit
y
is
a
cr
u
cial
ch
allen
g
e
in
b
ig
d
ata
p
r
e
d
ictiv
e
an
aly
s
is
.
As
th
e
v
o
lu
m
e,
v
elo
city
,
a
n
d
v
ar
iety
o
f
d
ata
in
cr
ea
s
e,
th
e
co
m
p
u
tat
io
n
al
r
eso
u
r
ce
s
n
ee
d
ed
to
p
r
o
ce
s
s
an
d
an
aly
ze
it
al
s
o
in
cr
ea
s
e.
T
h
e
tr
ad
itio
n
al
co
m
p
u
tin
g
r
eso
u
r
ce
s
m
ay
n
o
t
b
e
s
u
f
f
icien
t
to
h
an
d
le
th
e
s
ca
le
o
f
d
ata,
w
h
ich
ca
n
lead
to
p
er
f
o
r
m
a
n
ce
is
s
u
es,
s
u
ch
as
s
lo
w
p
r
o
ce
s
s
in
g
tim
es
,
d
elay
s
,
a
n
d
s
y
s
tem
cr
ash
es.
T
h
er
ef
o
r
e,
it
is
ess
en
tial
to
d
e
s
ig
n
an
d
im
p
lem
e
n
t
a
s
ca
lab
le
ar
ch
itectu
r
e
th
at
ca
n
h
an
d
le
th
e
in
cr
ea
s
in
g
d
ata
v
o
l
u
m
e
wh
ile
m
ain
tain
in
g
o
p
tim
a
l p
er
f
o
r
m
an
ce
.
T
o
ad
d
r
ess
th
e
s
ca
lab
ilit
y
ch
a
llen
g
e
in
b
ig
d
ata
an
al
y
tics
,
r
e
s
ea
r
ch
er
s
an
d
in
d
u
s
tr
y
p
r
o
f
ess
io
n
als
ar
e
ex
p
lo
r
in
g
v
ar
io
u
s
tech
n
iq
u
es
an
d
tech
n
o
l
o
g
ies,
s
u
ch
as
d
is
t
r
ib
u
ted
co
m
p
u
tin
g
,
cl
o
u
d
co
m
p
u
tin
g
,
an
d
p
ar
allel
p
r
o
ce
s
s
in
g
[
3
8
]
.
T
h
ese
tech
n
i
q
u
es
en
ab
le
d
ata
p
r
o
ce
s
s
in
g
an
d
an
al
y
s
is
to
b
e
p
er
f
o
r
m
ed
s
im
u
ltan
eo
u
s
ly
o
n
m
u
ltip
le
m
ac
h
in
es,
th
er
eb
y
s
ig
n
if
ican
tly
r
ed
u
cin
g
p
r
o
ce
s
s
in
g
tim
e
an
d
im
p
r
o
v
i
n
g
s
y
s
tem
p
er
f
o
r
m
an
ce
.
Ho
wev
er
,
im
p
lem
e
n
tin
g
t
h
e
s
e
tech
n
iq
u
es
r
eq
u
ir
es
ca
r
ef
u
l
co
n
s
id
er
atio
n
o
f
th
e
s
y
s
tem
'
s
d
esig
n
,
d
ata
d
is
tr
ib
u
tio
n
,
an
d
p
r
o
ce
s
s
in
g
alg
o
r
ith
m
s
.
6.
CO
NCLU
SI
O
N
T
h
e
p
o
ten
tial
f
o
r
u
tili
zin
g
b
i
g
d
ata
p
r
ed
ictiv
e
an
aly
s
is
in
g
o
v
er
n
a
n
ce
is
ex
te
n
s
iv
e,
e
n
co
m
p
ass
in
g
a
r
an
g
e
o
f
a
p
p
licatio
n
s
f
r
o
m
s
o
lv
in
g
tr
af
f
ic
p
r
o
b
lem
s
to
i
m
p
r
o
v
i
n
g
h
ea
lth
ca
r
e,
m
an
ag
i
n
g
s
u
p
p
ly
ch
ai
n
s
,
p
r
o
tectin
g
th
e
en
v
ir
o
n
m
en
t,
cu
s
to
m
izin
g
e
d
u
ca
tio
n
,
an
d
en
h
an
cin
g
s
ec
u
r
ity
.
As
tech
n
o
lo
g
y
co
n
tin
u
es
to
ev
o
lv
e,
g
o
v
er
n
m
en
ts
h
a
v
e
th
e
o
p
p
o
r
tu
n
ity
t
o
tr
an
s
f
o
r
m
th
e
way
th
ey
o
p
er
ate,
in
clu
d
in
g
c
itizen
s
in
d
ec
is
io
n
-
m
ak
in
g
p
r
o
ce
s
s
es
an
d
im
p
r
o
v
in
g
o
v
er
all
ef
f
icien
cy
.
Pre
d
i
ctiv
e
an
aly
s
is
ca
n
h
elp
p
u
b
lic
wo
r
k
e
r
s
b
e
m
o
r
e
ef
f
ec
tiv
e
with
lim
ited
r
eso
u
r
c
es,
b
u
t
it
s
h
o
u
ld
n
o
t
r
ep
lace
i
n
tu
itio
n
,
lo
ca
l
k
n
o
wled
g
e,
a
n
d
ex
p
er
tis
e.
T
h
e
m
o
s
t
ef
f
ec
tiv
e
u
s
e
o
f
th
is
s
y
s
tem
is
to
c
o
m
p
lem
e
n
t
e
x
is
tin
g
p
r
ac
tices.
Pre
d
ictiv
e
an
al
y
s
is
ca
n
a
ls
o
h
elp
b
u
ild
tr
u
s
t
in
ch
an
g
e
an
d
en
co
u
r
ag
e
ex
p
er
im
en
tatio
n
.
Ho
wev
er
,
th
er
e
ar
e
c
h
allen
g
es
to
b
e
co
n
s
id
er
ed
wh
en
im
p
lem
en
tin
g
b
ig
d
ata
in
th
e
p
u
b
lic
s
ec
to
r
,
an
d
m
o
r
e
r
esear
c
h
is
n
ee
d
ed
to
f
in
d
s
o
lu
tio
n
s
.
RE
F
E
R
E
NC
E
S
[
1
]
M
.
S
.
R
a
h
m
a
n
a
n
d
H
.
R
e
z
a
,
“
A
sy
s
t
e
mat
i
c
r
e
v
i
e
w
t
o
w
a
r
d
s
b
i
g
d
a
t
a
a
n
a
l
y
t
i
c
s
i
n
s
o
c
i
a
l
me
d
i
a
,
”
Bi
g
D
a
t
a
M
i
n
i
n
g
a
n
d
A
n
a
l
y
t
i
c
s
,
v
o
l
.
5
,
n
o
.
3
,
p
p
.
2
2
8
–
2
4
4
,
2
0
2
2
,
d
o
i
:
1
0
.
2
6
5
9
9
/
B
D
M
A
.
2
0
2
2
.
9
0
2
0
0
0
9
.
[
2
]
M
.
M
.
Y
o
u
sef,
“
B
i
g
d
a
t
a
a
n
a
l
y
t
i
c
s
i
n
h
e
a
l
t
h
c
a
r
e
:
a
r
e
v
i
e
w
p
a
p
e
r
,
”
I
n
t
e
r
n
a
t
i
o
n
a
l
J
o
u
rn
a
l
o
f
C
o
m
p
u
t
e
r
S
c
i
e
n
c
e
a
n
d
I
n
f
o
rm
a
t
i
o
n
T
e
c
h
n
o
l
o
g
y
,
v
o
l
.
1
3
,
p
p
.
1
7
–
2
8
,
2
0
2
1
.
[
3
]
D
.
La
n
e
y
,
“
3
D
d
a
t
a
m
a
n
a
g
e
me
n
t
:
c
o
n
t
r
o
l
l
i
n
g
d
a
t
a
v
o
l
u
me
,
v
e
l
o
c
i
t
y
a
n
d
v
a
r
i
e
t
y
,
”
ME
T
A
G
ro
u
p
R
e
se
a
rc
h
N
o
t
e
,
2
0
0
1
.
[
4
]
B
.
S
e
n
a
,
A
.
P
.
A
l
l
i
a
n
,
a
n
d
E
.
Y
.
N
a
k
a
g
a
w
a
,
“
C
h
a
r
a
c
t
e
r
i
z
i
n
g
b
i
g
d
a
t
a
s
o
f
t
w
a
r
e
a
r
c
h
i
t
e
c
t
u
r
e
s
:
a
sy
s
t
e
m
a
t
i
c
map
p
i
n
g
s
t
u
d
y
,
”
Pro
c
e
e
d
i
n
g
s o
f
t
h
e
1
1
t
h
Bra
zi
l
i
a
n
S
y
m
p
o
s
i
u
m
o
n
S
o
f
t
w
a
r
e
C
o
m
p
o
n
e
n
t
s,
A
rch
i
t
e
c
t
u
re
s,
a
n
d
R
e
u
se
,
S
BC
AR
S
'
1
7
,
B
r
a
z
i
l
,
2
0
1
7
,
d
o
i
:
1
0
.
1
1
4
5
/
3
1
3
2
4
9
8
.
3
1
3
2
5
1
0
.
[
5
]
M
.
S
.
R
a
h
ma
n
a
n
d
H
.
R
e
z
a
,
“
S
y
s
t
e
mat
i
c
m
a
p
p
i
n
g
s
t
u
d
y
o
f
n
o
n
-
f
u
n
c
t
i
o
n
a
l
r
e
q
u
i
r
e
m
e
n
t
s
i
n
b
i
g
d
a
t
a
s
y
st
e
m,
”
i
n
2
0
2
0
I
EE
E
I
n
t
e
r
n
a
t
i
o
n
a
l
C
o
n
f
e
r
e
n
c
e
o
n
E
l
e
c
t
r
o
I
n
f
o
rm
a
t
i
o
n
T
e
c
h
n
o
l
o
g
y
(
EI
T
)
,
2
0
2
0
,
p
p
.
2
5
–
3
1
,
d
o
i
:
1
0
.
1
1
0
9
/
EI
T4
8
9
9
9
.
2
0
2
0
.
9
2
0
8
2
8
8
.
[
6
]
Z.
S
u
n
,
K
.
S
t
r
a
n
g
,
a
n
d
R
.
L
i
,
“
B
i
g
d
a
t
a
w
i
t
h
t
e
n
b
i
g
c
h
a
r
a
c
t
e
r
i
st
i
c
s
,
”
i
n
Pr
o
c
e
e
d
i
n
g
s
o
f
t
h
e
2
n
d
I
n
t
e
r
n
a
t
i
o
n
a
l
C
o
n
f
e
r
e
n
c
e
o
n
Bi
g
D
a
t
a
R
e
se
a
rc
h
,
2
0
1
8
,
p
p
.
5
6
–
6
1
,
d
o
i
:
1
0
.
1
1
4
5
/
3
2
9
1
8
0
1
.
3
2
9
1
8
2
.
[
7
]
N
.
M
.
A
d
a
ms
,
“
P
e
r
sp
e
c
t
i
v
e
s
o
n
d
a
t
a
mi
n
i
n
g
,
”
I
n
t
e
r
n
a
t
i
o
n
a
l
J
o
u
r
n
a
l
o
f
M
a
rk
e
t
R
e
se
a
rc
h
,
v
o
l
.
5
2
,
n
o
.
1
,
p
p
.
1
1
–
1
9
,
2
0
1
0
,
d
o
i
:
1
0
.
2
5
0
1
/
S
1
4
7
0
7
8
5
3
1
0
2
0
1
0
3
X
.
[
8
]
K
.
V
a
ss
a
k
i
s,
E.
P
e
t
r
a
k
i
s,
a
n
d
I
.
K
o
p
a
n
a
k
i
s
,
“
B
i
g
d
a
t
a
a
n
a
l
y
t
i
c
s
:
a
p
p
l
i
c
a
t
i
o
n
s,
p
r
o
sp
e
c
t
s
a
n
d
c
h
a
l
l
e
n
g
e
s
,
”
i
n
M
o
b
i
l
e
Bi
g
D
a
t
a
:
A
Ro
a
d
m
a
p
f
r
o
m
Mo
d
e
l
s
t
o
T
e
c
h
n
o
l
o
g
i
e
s
,
G
.
S
k
o
u
r
l
e
t
o
p
o
u
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