I
nte
rna
t
io
na
l J
o
urna
l o
f
Adv
a
nces in Applie
d Science
s
(
I
J
AAS)
Vo
l.
5
,
No
.
2
,
J
u
n
e
201
6
,
p
p
.
101
~
1
0
8
I
SS
N:
2252
-
8814
101
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Mar
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[
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I
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2
2
5
2
-
8814
IJ
AA
S
Vo
l.
5
,
No
.
2
,
J
u
n
e
2
0
1
6
:
1
0
1
–
1
08
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s
s
u
c
h
a
s
Ma
p
an
d
R
ed
u
ce
,
t
h
at
t
h
e
Ma
p
R
ed
u
ce
f
r
a
m
e
w
o
r
k
ap
p
lies
to
th
e
i
n
s
ta
n
ce
s
(
m
ap
)
an
d
s
o
r
ted
g
r
o
u
p
s
o
f
i
n
s
tan
ce
s
th
at
s
h
ar
e
a
co
m
m
o
n
k
e
y
(
r
ed
u
ce
)
–
s
i
m
i
lar
to
th
e
s
o
r
t
o
f
p
ar
titi
o
n
ed
p
ar
allelis
m
u
tili
ze
d
in
s
h
ar
ed
-
n
o
t
h
i
n
g
p
ar
allel
q
u
er
y
p
r
o
ce
s
s
i
n
g
.
C
o
n
v
e
n
tio
n
all
y
,
B
ig
Data
is
d
escr
ib
ed
as
d
ata
is
to
o
b
i
g
f
o
r
e
x
is
ti
n
g
s
y
s
te
m
s
to
p
r
o
ce
s
s
.
B
i
g
Data
h
as
ess
e
n
tia
l
c
h
ar
ac
ter
is
tic
s
a
s
f
o
llo
w
s
Var
iet
y
,
Vo
lu
m
e
a
n
d
Velo
cit
y
a
s
s
h
o
wn
in
F
ig
.
1
.
I
n
a
Dis
tr
ib
u
ted
S
y
s
te
m
s
w
o
r
ld
,
B
ig
Data
s
tar
ted
to
b
ec
o
m
e
a
m
aj
o
r
ch
alle
n
g
e
i
n
t
h
e
late
1
9
9
0
’
s
d
u
e
to
th
e
i
m
p
ac
t
o
f
w
o
r
ld
-
w
i
d
e
w
eb
.
Data
b
ase
tech
n
o
lo
g
y
(
in
clu
d
i
n
g
p
ar
allel
d
atab
ases
)
w
as
co
n
s
id
er
ed
f
o
r
th
e
task
,
b
u
t
w
as
f
o
u
n
d
to
b
e
n
eith
er
w
ell
-
s
u
ited
n
o
r
co
s
t
-
ef
f
ec
t
iv
e
f
o
r
th
o
s
e
p
u
r
p
o
s
es.
T
h
e
tu
r
n
o
f
t
h
e
m
ill
en
n
i
u
m
t
h
e
n
b
r
o
u
g
h
t
f
u
r
t
h
er
c
h
alle
n
g
e
s
as
co
m
p
a
n
ies
b
eg
a
n
to
u
s
e
i
n
f
o
r
m
atio
n
s
u
c
h
as
th
e
to
p
o
lo
g
y
o
f
th
e
W
eb
an
d
u
s
er
s
‟
s
ea
r
ch
h
i
s
to
r
ies
in
o
r
d
er
to
p
r
o
v
id
e
in
cr
ea
s
in
g
l
y
u
s
ef
u
l
s
ea
r
c
h
r
esu
lt
s
,
as
w
ell
as
m
o
r
e
ef
f
ec
ti
v
el
y
-
tar
g
eted
ad
v
er
tis
i
n
g
to
d
is
p
la
y
alo
n
g
s
id
e
a
n
d
f
u
n
d
t
h
o
s
e
r
esu
lts
.
T
h
e
n
ec
ess
it
y
to
p
r
o
ce
s
s
m
as
s
iv
e
q
u
a
n
titi
e
s
o
f
d
ata
h
as
n
e
v
er
b
ee
n
g
r
ea
ter
.
No
t
o
n
ly
te
r
ab
y
te
an
d
p
etab
y
te
s
ca
le
d
at
asets
r
ap
id
l
y
b
ec
o
m
i
n
g
co
m
m
o
n
p
lace
,
b
u
t
th
er
e
is
co
n
s
e
n
s
u
s
t
h
at
g
r
ea
t
v
alu
e
l
ies
b
u
r
ied
i
n
th
e
m
,
w
ait
in
g
to
b
e
u
n
lo
c
k
ed
b
y
t
h
e
r
i
g
h
t c
o
m
p
u
tatio
n
al
to
o
ls
.
I
n
t
h
e
co
m
m
er
cial
w
o
r
ld
,
b
u
s
i
n
es
s
i
n
tel
lig
e
n
ce
g
ath
er
s
th
e
d
ata
f
r
o
m
ar
r
a
y
o
f
s
o
u
r
ce
s
.
B
ig
Da
ta
an
al
y
s
i
s
to
o
ls
lik
e
Ma
p
R
ed
u
ce
o
v
er
Had
o
o
p
,
HDFS,
to
ass
is
t to
o
r
g
an
izatio
n
s
b
etter
u
n
d
er
s
tan
d
t
h
eir
m
ar
k
et
p
lace
an
d
cu
s
to
m
er
s
h
o
p
ef
u
ll
y
lead
i
n
g
to
b
etter
b
u
s
in
e
s
s
d
ec
is
io
n
s
a
n
d
co
m
p
etiti
v
e
b
en
ef
its
.
Fo
r
en
g
i
n
ee
r
s
b
u
i
ld
in
g
in
f
o
r
m
a
tio
n
p
r
o
ce
s
s
i
n
g
to
o
ls
an
d
ap
p
l
icatio
n
s
,
lar
g
e
an
d
h
e
ter
o
g
en
eo
u
s
d
at
asets
w
h
ic
h
ar
e
g
e
n
er
atin
g
c
o
n
tin
u
o
u
s
f
lo
w
o
f
d
ata,
lead
to
m
o
r
e
e
f
f
ec
t
iv
e
alg
o
r
ith
m
s
f
o
r
a
w
id
e
r
an
g
e
o
f
task
s
.
2.
B
I
G
DA
T
A
CH
ARAC
T
E
RI
ST
I
CS
Fro
m
B
ig
Data
[
4
]
.
T
h
er
e
ar
e
d
if
f
er
en
t
ex
p
lan
a
tio
n
s
f
o
r
B
ig
Data
f
r
o
m
3
V
’
s
to
4
V’
s
.
A
cc
o
r
d
in
g
to
Do
u
g
L
a
n
e
y
,
Vo
l
u
m
e,
Ve
lo
cit
y
a
n
d
Var
iet
y
r
e
f
er
r
ed
to
as
3
Vs
[
9
]
.
A
cc
o
r
d
in
g
to
o
th
er
p
eo
p
le
s
p
ec
ial
r
eq
u
ir
e
m
en
ts
,
t
h
e
y
ar
e
e
x
ten
d
ed
an
o
th
er
V.
T
h
e
f
o
u
r
t
h
V
is
Valu
e,
Var
iab
ilit
y
[
1
0
]
.
B
ig
Data
is
a
co
llectio
n
o
f
v
er
y
h
u
g
e
d
ata
s
et
s
w
i
th
d
iv
er
s
i
f
icatio
n
o
f
t
y
p
e
s
s
u
c
h
t
h
a
t,
it b
ec
o
m
e
s
ted
io
u
s
to
p
r
o
ce
s
s
b
y
u
s
in
g
th
e
s
tate
-
of
-
t
h
e
-
ar
t
d
ata
p
r
o
ce
s
s
in
g
ap
p
r
o
ac
h
es
o
r
co
n
v
en
tio
n
al
d
ata
p
r
o
ce
s
s
in
g
p
lat
f
o
r
m
s
.
I
n
th
e
y
ea
r
2
0
1
2
,
Gar
tn
er
de
f
in
ed
B
ig
Data
as
“
B
ig
Dat
a
is
Hig
h
Velo
cit
y
,
Hi
g
h
Vo
l
u
m
e
a
n
d
Hig
h
v
ar
iet
y
in
f
o
r
m
atio
n
ass
et
s
r
eq
u
ir
e
n
e
w
f
o
r
m
s
o
f
p
r
o
ce
s
s
i
n
g
to
en
ab
le
en
h
a
n
ce
d
d
ec
is
io
n
m
a
k
i
n
g
,
p
r
o
ce
s
s
o
p
ti
m
izatio
n
a
n
d
in
s
ig
h
t d
is
co
v
er
y
[
6
]
.
2
.
1
.
Vo
lu
m
e
Vo
lu
m
e
i
s
d
escr
ib
ed
as
t
h
e
r
e
lativ
e
s
i
ze
o
f
th
e
d
ata
to
th
e
p
r
o
ce
s
s
in
g
ca
p
ab
ilit
y
.
E
v
er
y
d
ay
w
e
ar
e
cr
ea
tin
g
2
.
5
q
u
in
t
illi
o
n
b
y
tes
o
f
d
ata
[
5
]
.
T
h
is
d
ata
is
g
e
n
er
ated
f
r
o
m
e
v
er
y
w
h
er
e
s
u
c
h
a
s
f
r
o
m
s
en
s
o
r
s
,
s
o
cial
m
ed
ia
s
ites
,
d
ig
ital
p
ict
u
r
es
v
id
eo
s
,
p
u
r
c
h
ase
tr
an
s
ac
tio
n
r
ec
o
r
d
s
,
etc.
to
o
v
er
co
m
e
t
h
i
s
v
o
lu
m
e
p
r
o
b
le
m
r
eq
u
ir
es
tech
n
o
lo
g
ie
s
t
h
at
s
t
o
r
e
m
a
s
s
i
v
e
a
m
o
u
n
ts
o
f
d
at
a
in
a
s
ca
lab
le
m
a
n
n
er
an
d
p
r
o
v
id
e
d
is
tr
ib
u
ted
ap
p
r
o
ac
h
es
to
f
in
d
t
h
at
d
ata.
A
p
ac
h
e
Had
o
o
p
b
ased
s
o
lu
tio
n
s
an
d
m
as
s
iv
e
l
y
p
ar
allel
p
r
o
ce
s
s
in
g
d
atab
ase
s
s
u
c
h
as E
MC Gr
ee
n
p
lu
m
,
C
al
p
o
n
t,
E
XA
S
O
L
,
I
B
M
Net
w
zz
a,
T
e
r
ad
ata
Kick
f
ir
e.
2
.
2
.
Velo
cit
y
Velo
cit
y
is
d
escr
ib
ed
as
a
f
r
eq
u
en
c
y
at
w
h
ic
h
th
e
d
ata
is
g
e
n
er
ated
,
s
h
ar
ed
an
d
ca
p
tu
r
ed
.
T
h
e
g
r
o
w
th
in
s
en
s
o
r
d
ata
f
r
o
m
d
ev
ice
s
,
a
n
d
w
eb
b
ased
clic
k
s
tr
ea
m
a
n
al
y
s
i
s
n
o
w
cr
ea
tes
r
eq
u
ir
e
m
e
n
ts
f
o
r
g
r
ea
ter
r
ea
l
-
ti
m
e
u
s
e
ca
s
e
s
.
T
h
e
v
elo
cit
y
o
f
m
a
s
s
i
v
e
d
ata
s
tr
ea
m
s
p
o
w
e
r
th
e
ab
ilit
y
to
p
ar
s
e
tex
t,
id
en
tify
i
n
g
n
e
w
p
at
ter
n
s
an
d
d
etec
t
s
e
n
ti
m
en
t.
Ke
y
t
ec
h
n
o
lo
g
ies
t
h
at
ad
d
r
ess
v
elo
cit
y
i
n
cl
u
d
e
s
tr
ea
m
in
g
p
r
o
ce
s
s
in
g
a
n
d
co
m
p
lex
ev
en
t
p
r
o
ce
s
s
i
n
g
.
W
h
en
r
elati
o
n
al
ap
p
r
o
ac
h
es
n
o
lo
n
g
er
m
a
k
e
s
e
n
s
e
,
No
SQ
L
d
atab
ases
ar
e
u
s
ed
.
I
n
ad
d
itio
n
to
th
at
,
co
lu
m
n
ar
d
atab
ases
,
t
h
e
u
s
e
o
f
i
n
-
m
e
m
o
r
y
d
ata
b
ases
(
I
MD
B
)
,
an
d
k
e
y
v
alu
e
s
to
r
es
h
elp
i
m
p
r
o
v
e
r
etr
iev
al
o
f
p
r
e
-
ca
lc
u
lated
d
ata
.
2
.
3
.
Va
riet
y
Sp
r
ea
d
o
f
d
ata
t
y
p
es
f
r
o
m
m
ac
h
in
e
to
m
ac
h
i
n
e,
s
o
cial
an
d
m
o
b
ile
s
o
u
r
ce
s
ad
d
n
e
w
d
a
ta
t
y
p
es
to
co
n
v
e
n
tio
n
al
tr
an
s
ac
tio
n
a
l
d
at
a.
Data
n
o
lo
n
g
er
f
i
ts
i
n
to
n
ea
t,
ea
s
y
to
co
n
s
u
m
e
s
tr
u
ct
u
r
es
.
Ne
w
t
y
p
e
s
i
n
clu
d
e
g
eo
-
s
p
atial,
co
n
ten
t,
h
ar
d
w
ar
e
d
ata
p
o
in
ts
,
lo
g
d
ata
,
m
ac
h
in
e
d
ata,
m
o
b
ile,
p
h
y
s
ical
d
ata
p
o
in
ts
,
p
r
o
ce
s
s
,
m
etr
ics,
R
FID
’
s
s
ea
r
c
h
,
s
o
cial,
w
eb
,
s
e
n
ti
m
en
t
s
tr
ea
m
i
n
g
d
ata
an
d
tex
t.
Un
s
tr
u
c
tu
r
ed
d
ata
s
u
ch
as
tex
t,
Evaluation Warning : The document was created with Spire.PDF for Python.
IJ
AA
S
I
SS
N:
2252
-
8814
A
S
tu
d
y
o
n
B
i
g
Da
ta
Tech
n
iq
u
es a
n
d
A
p
p
lica
tio
n
s
(
K
.
R
a
d
h
a
)
103
s
p
ee
ch
a
n
d
la
n
g
u
a
g
e
i
n
cr
ea
s
i
n
g
l
y
co
m
p
licate
t
h
e
ab
ilit
y
to
c
ateg
o
r
ize
d
ata.
So
m
e
o
f
t
h
e
te
ch
n
o
lo
g
ies
th
at
ar
e
d
ea
lin
g
w
i
th
u
n
s
tr
u
c
tu
r
ed
d
ata
in
clu
d
e
te
x
t a
n
a
l
y
t
ics,
d
ata
m
i
n
in
g
a
n
d
n
o
is
y
tex
t a
n
al
y
tic
s
.
3
.
RE
S
E
ARCH
M
E
T
H
O
D
3
.
1
.
M
a
p Re
du
ce
1)
T
h
u
s
th
e
Ma
p
R
ed
u
ce
f
r
a
m
e
wo
r
k
tr
an
s
f
o
r
m
s
a
li
s
t o
f
(
k
e
y
,
v
alu
e)
p
air
s
in
to
a
lis
t o
f
v
al
u
es
.
2)
Th
ese
b
eh
av
io
r
s
is
d
i
f
f
er
e
n
t
f
r
o
m
th
e
f
u
n
ctio
n
al
p
r
o
g
r
a
m
m
i
n
g
m
ap
a
n
d
r
ed
u
ce
co
m
b
i
n
ati
o
n
,
w
h
ic
h
ac
ce
p
ts
a
l
is
t
o
f
ar
b
itra
r
y
v
al
u
es
a
n
d
r
et
u
r
n
s
o
n
e
s
i
n
g
le
v
al
u
e
t
h
at
co
m
b
i
n
e
s
all
th
e
v
al
u
e
s
r
etu
r
n
ed
b
y
m
ap
.
3)
I
t
is
n
ec
ess
ar
y
b
u
t
n
o
t
s
u
f
f
ic
ien
t
to
h
a
v
e
i
m
p
le
m
e
n
tatio
n
s
o
f
th
e
m
ap
an
d
r
ed
u
ce
ab
s
tr
ac
tio
n
s
i
n
o
r
d
er
t
o
im
p
le
m
e
n
t M
ap
R
ed
u
ce
.
4)
Dis
tr
ib
u
ted
i
m
p
le
m
en
ta
tio
n
s
o
f
Ma
p
R
ed
u
ce
r
eq
u
ir
e
a
m
ea
n
s
o
f
co
n
n
ec
tin
g
t
h
e
p
r
o
ce
s
s
es
p
er
f
o
r
m
i
n
g
th
e
Ma
p
an
d
R
ed
u
ce
p
h
a
s
es.
5)
T
h
is
m
a
y
b
e
a
Dis
tr
ib
u
ted
f
i
le
s
y
s
te
m
.
6)
Oth
er
o
p
tio
n
s
ar
e
p
o
s
s
ib
le,
s
u
ch
as
d
ir
ec
t
s
t
r
ea
m
in
g
f
r
o
m
m
ap
p
er
s
to
r
ed
u
ce
r
s
,
o
r
f
o
r
th
e
m
ap
p
in
g
p
r
o
ce
s
s
o
r
s
to
s
er
v
e
u
p
th
eir
r
esu
lt
s
to
r
ed
u
ce
r
s
th
at
q
u
er
y
t
h
e
m
.
Fig
u
r
e
1
.
W
o
r
k
f
lo
w
o
f
Ma
p
R
ed
u
ce
3
.
1
.
1
.
User
P
ro
g
ra
m
1)
T
y
p
icall
y
E
x
ec
u
tio
n
o
f
a
p
r
o
g
r
a
m
b
eg
i
n
s
w
it
h
t
h
e
u
s
er
p
r
o
g
r
a
m
2)
Ma
p
R
ed
u
ce
lib
r
ar
ies
ar
e
i
m
p
o
r
ted
in
to
th
e
p
r
o
g
r
a
m
an
d
t
h
at
p
r
o
g
r
a
m
is
s
p
litt
ed
i
n
to
t
h
e
o
p
er
atio
n
s
th
at
ar
e
to
b
e
p
er
f
o
r
m
ed
o
n
th
e
in
p
u
t d
ataset.
3)
I
n
a
clu
s
ter
e
v
er
y
m
ac
h
in
e
h
as
a
s
ep
ar
ate
in
s
ta
n
ce
o
f
th
e
m
ap
p
er
p
r
o
g
r
a
m
r
u
n
n
i
n
g
o
n
it.
4)
T
h
er
e
ar
e
m
a
s
ter
an
d
w
o
r
k
er
s
.
On
e
o
f
th
e
co
p
ies
o
f
t
h
e
p
r
o
g
r
a
m
is
Ma
s
ter
a
n
d
R
e
m
ai
n
in
g
p
r
o
g
r
am
s
ar
e
ass
ig
n
ed
to
w
o
r
k
u
n
d
er
th
e
m
a
s
ter
ca
lled
as
W
o
r
k
er
.
T
h
er
e
ar
e
M
n
u
m
b
er
o
f
tas
k
s
an
d
N
Nu
m
b
er
o
f
r
ed
u
ce
o
p
er
atio
n
s
to
p
er
f
o
r
m
.
T
h
e
m
ap
p
er
p
ick
s
th
e
u
n
u
s
ed
w
o
r
k
er
s
a
n
d
as
s
ig
n
s
ea
c
h
o
f
th
e
m
a
m
ap
tas
k
o
r
r
ed
u
ce
task
.
3
.
1
.
2
.
M
a
p Wo
rk
er
s
1)
T
h
e
w
o
r
k
er
th
a
t
is
a
s
s
i
g
n
ed
th
e
Ma
p
tas
k
ta
k
es
t
h
e
s
p
lit
te
d
in
p
u
t
d
ata
a
n
d
p
r
o
d
u
ce
s
th
e
k
e
y
/v
al
u
e
p
air
f
o
r
ev
er
y
s
eg
m
e
n
t o
f
i
n
p
u
t d
ata.
2)
User
-
d
e
f
in
ed
m
ap
f
u
n
ct
io
n
is
i
n
v
o
k
ed
b
y
th
e
w
o
r
k
er
n
o
d
e.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8814
IJ
AA
S
Vo
l.
5
,
No
.
2
,
J
u
n
e
2
0
1
6
:
1
0
1
–
1
08
104
3)
T
h
e
r
e
s
u
ltan
t
v
al
u
es
o
f
th
e
M
ap
f
u
n
ctio
n
ar
e
b
u
f
f
er
ed
in
th
e
m
e
m
o
r
y
.
T
e
m
p
o
r
ar
y
d
ata
later
w
r
itte
n
to
th
e
d
is
k
.
4)
T
h
e
p
h
y
s
ical
ad
d
r
ess
o
f
t
h
ese
co
n
ten
t
s
is
p
as
s
ed
to
th
e
Ma
s
t
er
.
5)
T
o
p
er
f
o
r
m
th
e
R
ed
u
ce
ta
s
k
t
h
e
m
aster
f
in
d
t
h
e
p
ass
es t
h
e
s
e
p
h
y
s
ical
m
e
m
o
r
y
ad
d
r
ess
es t
o
th
e
m
.
3
.
1
.
3
.
Reduce
Wo
rk
er
s
1)
R
ed
u
ce
w
o
r
k
er
n
o
tifie
d
b
y
t
h
e
u
s
er
’
s
r
e
m
o
te
p
r
o
ce
d
u
r
e
ca
lls
to
ac
ce
s
s
th
e
b
u
f
f
er
ed
d
ata
f
r
o
m
t
h
e
Ma
p
w
o
r
k
er
s
.
2)
W
h
en
e
v
er
r
ed
u
ce
w
o
r
k
er
h
as
r
ea
d
all
th
e
in
ter
m
ed
iate
d
ata,
it
g
r
o
u
p
s
to
g
eth
er
all
th
e
d
ata
o
f
th
e
s
a
m
e
i
n
ter
m
ed
iate
k
e
y
.
3)
Var
io
u
s
d
i
f
f
er
e
n
t
k
e
y
s
m
ap
to
th
e
s
a
m
e
tas
k
b
ec
au
s
e
o
f
th
e
p
ar
allel
p
r
o
ce
s
s
in
g
n
a
tu
r
e
o
f
th
e
ta
s
k
s
.
Su
c
h
th
at
t
h
e
ab
o
v
e
m
en
t
io
n
e
d
s
o
r
tin
g
s
tep
i
s
n
ee
d
ed
.
4)
Fo
r
ev
er
y
u
s
er
ev
er
y
u
n
iq
u
e
k
e
y
a
n
d
its
d
ata
ar
e
p
ass
ed
b
y
t
h
e
r
ed
u
ce
w
o
r
k
er
to
t
h
e
R
ed
u
ce
f
u
n
ct
io
n
.
5)
Ou
tp
u
t o
f
a
R
ed
u
ce
ta
s
k
i
s
w
r
i
tten
to
an
o
u
tp
u
t u
s
u
all
y
to
d
is
tr
ib
u
ted
f
ile
s
y
s
te
m
.
3
.
1
.
4
.
Ret
urn
t
o
t
he
User
P
ro
g
ra
m
1)
Af
ter
r
u
n
n
i
n
g
all
th
e
Ma
p
an
d
R
ed
u
ce
h
av
e
b
ee
n
r
u
n
,
T
h
e
Ma
s
ter
n
o
d
e
s
en
d
s
co
n
tr
o
l
b
ac
k
to
th
e
u
s
er
s
id
e.
2)
T
h
er
e
ar
e
m
an
y
o
u
tp
u
t
f
i
les
av
ailab
le
to
t
h
e
u
s
er
as
th
er
e
w
er
e
R
ed
u
ce
ca
ll
s
.
u
p
o
n
co
m
p
letio
n
o
f
ab
o
v
e
m
en
t
io
n
ed
s
et
o
f
ta
s
k
s
3)
T
h
ese
f
iles
m
a
y
r
ei
n
s
er
t
in
to
an
o
th
er
Ma
p
R
ed
u
ce
tas
k
s
s
ess
io
n
o
r
th
e
y
m
a
y
d
ea
l
as
in
p
u
t
s
f
o
r
d
is
tr
ib
u
ted
p
r
o
ce
s
s
in
g
ap
p
licatio
n
s
.
3
.
2
.
L
o
g
ica
l V
iew
1)
Fo
r
b
o
t
h
th
e
Ma
p
an
d
R
ed
u
c
e
f
u
n
ctio
n
s
o
f
Ma
p
R
ed
u
ce
,
Data
is
as
s
u
m
ed
to
b
e
s
tr
u
ctu
r
ed
in
(
k
e
y
,
v
alu
e)
p
air
s
.
2)
Ma
p
tak
es
o
n
e
p
air
o
f
d
ata
w
i
th
a
t
y
p
e
i
n
o
n
e
d
ata
d
o
m
ai
n
,
an
d
r
etu
r
n
s
a
lis
t
o
f
p
air
s
i
n
a
d
if
f
er
e
n
t
d
o
m
ai
n
: M
ap
(
k
1
,
v
1
)
-
>
li
s
t(
k
2
,
v
2
)
3)
B
y
ap
p
l
y
i
n
g
th
is
m
ap
f
u
n
c
tio
n
in
p
ar
allel
to
ev
er
y
ite
m
in
th
e
i
n
p
u
t
d
ataset,
P
ar
allel
p
r
o
ce
s
s
i
n
g
i
s
in
tr
o
d
u
ce
d
4)
I
t
p
r
o
d
u
ce
s
a
li
s
t
o
f
(
K2
,
v
2
)
p
air
s
f
o
r
ea
c
h
ca
ll.
Af
ter
t
h
at,
th
e
Ma
p
R
ed
u
ce
f
r
a
m
e
w
o
r
k
co
llects
all
p
air
s
w
i
th
t
h
e
s
a
m
e
k
e
y
f
r
o
m
all
lis
t
s
an
d
g
r
o
u
p
s
t
h
e
m
to
g
et
h
er
,
th
u
s
cr
ea
ti
n
g
o
n
e
g
r
o
u
p
f
o
r
ea
ch
o
n
e
o
f
th
e
d
if
f
er
e
n
t g
e
n
er
ated
k
e
y
s
.
5)
T
h
is
p
h
ase
o
p
ti
m
ize
s
th
e
i
n
p
u
t
f
o
r
r
ed
u
ce
f
u
n
ctio
n
.
6)
T
h
e
R
ed
u
ce
f
u
n
ctio
n
i
s
t
h
en
a
p
p
lied
in
p
ar
allel
to
ea
ch
g
r
o
u
p
,
w
h
ich
i
n
t
u
r
n
p
r
o
d
u
ce
s
a
co
llectio
n
o
f
v
alu
e
s
i
n
th
e
s
a
m
e
d
o
m
ai
n
:
R
e
d
u
ce
(
k
2
,
lis
t (
v
2
)
)
-
>
lis
t(
v
3
)
Alg
o
rit
h
m
I
np
ut
:
Da
t
a
in t
he
f
o
r
m
o
f
(
key
,
v
a
lue)
pa
irs
O
utput
:
L
is
t
o
f
da
t
a
it
e
m
s
Alg
o
rit
h
m
:
1.
Ma
p
d
ata
f
r
o
m
o
n
e
d
o
m
a
in
to
an
o
th
er
[
Ma
p
(
m
1
,
v
1
)
-
>
li
s
t(
m
2
,
v
2
)
]
2.
Op
ti
m
ize
i
n
p
u
t
f
o
r
R
ed
u
ce
f
u
n
ctio
n
3.
R
ed
u
ce
t
h
e
d
ata
in
to
m
o
r
e
m
e
an
in
g
f
u
l d
ata
in
t
h
e
s
a
m
e
d
o
m
ain
[
R
ed
u
ce
(
m
2
,
lis
t (
v
2
)
)
-
>
l
is
t(
v
3
)
]
T
h
e
Ma
p
an
d
R
ed
u
ce
f
u
n
cti
o
n
s
ar
e
n
ec
es
s
ar
y
b
u
t
n
o
t
s
u
f
f
icien
t
f
o
r
Ma
p
R
ed
u
ce
f
r
a
m
e
w
o
r
k
.
T
h
ese
t
w
o
f
u
n
ctio
n
s
b
r
in
g
t
h
e
p
ar
allel
p
r
o
ce
s
s
in
g
to
th
e
al
g
o
r
it
h
m
a
s
th
e
y
ca
n
b
e
ex
ec
u
ted
s
i
m
u
lta
n
eo
u
s
l
y
f
o
r
ea
ch
g
i
v
e
n
d
ata.
L
et
u
s
ta
k
e
an
e
x
a
m
p
le
o
f
b
u
il
d
in
g
i
n
d
ex
f
o
r
w
eb
p
ag
es a
v
ai
lab
le
o
n
lin
e
a
n
d
s
ee
h
o
w
Ma
p
an
d
R
ed
u
ce
f
u
n
ctio
n
s
ca
n
b
e
ex
ec
u
ted
.
I
n
p
u
t c
an
b
e
co
n
s
id
er
ed
as a
s
et
o
f
d
o
cu
m
e
n
t
s
P
s
eudo
co
de
f
o
r
M
a
p
:
Fo
r
ea
ch
w
o
r
d
m
k
in
d
o
cu
m
e
n
t
co
u
n
t(
m
k
)
=
co
u
n
t(
m
k
)
+
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w
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m
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Evaluation Warning : The document was created with Spire.PDF for Python.
IJ
AA
S
I
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N:
2252
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8814
A
S
tu
d
y
o
n
B
i
g
Da
ta
Tech
n
iq
u
es a
n
d
A
p
p
lica
tio
n
s
(
K
.
R
a
d
h
a
)
105
4
.
B
I
G
DA
T
A
AP
P
L
I
CA
T
I
O
N
S
B
ig
Data
is
o
n
e
o
f
th
e
cu
r
r
e
n
t
an
d
f
u
t
u
r
e
r
esear
ch
b
o
u
n
d
ar
i
es.
Gar
tn
er
lis
ted
t
h
e
“
Fo
r
t
h
e
Nex
t
Fi
v
e
Yea
r
s
T
o
p
1
0
C
r
itical
T
ec
h
T
r
en
d
s
”
[
7
]
an
d
“
F
o
r
2
0
1
3
T
o
p
1
0
Stra
teg
ic
T
ec
h
n
o
lo
g
y
T
r
en
d
s
”
[
8
]
.
I
n
m
a
n
y
ar
ea
s
B
ig
Data
is
ch
a
n
g
ed
s
u
c
h
as
p
u
b
lic
ad
m
i
n
is
tr
atio
n
,
s
ci
en
ti
f
ic
r
esear
ch
,
b
u
s
in
e
s
s
,
T
h
e
Fin
a
n
cial
Ser
v
ice
s
I
n
d
u
s
tr
y
,
Au
to
m
o
ti
v
e
I
n
d
u
s
tr
y
,
S
u
p
p
l
y
C
h
ain
,
L
o
g
is
tics
,
a
n
d
I
n
d
u
s
tr
ial
E
n
g
i
n
ee
r
in
g
,
R
e
tail,
E
n
ter
tai
n
m
e
n
t
,
etc.
Oth
er
B
ig
Data
ap
p
lic
atio
n
s
ar
e
ex
is
t
i
n
at
m
o
s
p
h
er
ic
s
cie
n
ce
,
astro
n
o
m
y
,
m
ed
icin
e,
b
io
lo
g
ic,
b
io
g
eo
ch
e
m
i
s
tr
y
,
g
e
n
o
m
ics
an
d
i
n
ter
d
is
cip
li
n
ar
y
a
n
d
co
m
p
lex
r
esear
ch
e
s
.
W
eb
-
b
ase
d
ap
p
lica
tio
n
s
ar
e
en
co
u
n
ter
b
ig
d
ata
s
u
c
h
a
s
s
o
cial
co
m
p
u
ti
n
g
(
in
cl
u
d
es
o
n
lin
e
co
m
m
u
n
ities
,
r
ep
u
tati
o
n
s
y
s
te
m
s
,
s
o
cial
n
et
w
o
r
k
an
al
y
s
is
,
p
r
ed
ictio
n
m
ar
k
et
s
,
r
ec
o
m
m
e
n
d
er
s
y
s
te
m
s
,
I
n
ter
n
et
s
ea
r
ch
in
d
e
x
in
g
,
I
n
ter
n
et
te
x
t
a
n
d
d
o
cu
m
en
ts
.
T
h
er
e
ar
e
v
ar
io
u
s
s
en
s
o
r
s
a
v
ailab
le
ar
o
u
n
d
u
s
,
t
h
e
y
w
ill
g
e
n
er
ate
s
ea
m
les
s
s
e
n
s
o
r
d
ata
t
h
at
n
ee
d
to
b
e
u
til
ized
f
o
r
e
x
a
m
p
le
i
n
t
ellig
e
n
t
tr
a
n
s
p
o
r
tatio
n
s
y
s
te
m
s
(
I
T
S)
[
1
1
]
ar
e
b
ased
o
n
th
e
an
al
y
s
is
o
f
m
a
s
s
i
v
e
v
o
lu
m
e
o
f
co
m
p
lex
s
e
n
s
o
r
d
ata
.
Data
-
in
te
n
s
iv
e
ap
p
licatio
n
s
ar
e
l
ar
g
e
s
ca
le
e
-
co
m
m
er
ce
[
1
2
]
.
T
h
is
d
ata
-
in
te
n
s
i
v
e
ap
p
licatio
n
co
n
s
i
s
t
s
o
f
m
as
s
i
v
e
n
u
m
b
er
o
f
tr
an
s
ac
t
io
n
s
an
d
cu
s
to
m
er
s
.
I
n
th
e
f
o
llo
w
i
n
g
s
u
b
s
ec
tio
n
s
w
e
w
i
ll
b
r
ief
l
y
i
n
tr
o
d
u
ce
v
ar
i
o
u
s
ap
p
l
icatio
n
s
o
f
t
h
e
B
ig
D
ata
p
r
o
b
lem
s
i
n
b
u
s
i
n
ess
,
s
o
ciet
y
ad
m
in
i
s
tr
atio
n
an
d
s
cien
tific
r
esear
ch
f
ield
s
.
4
.
1
.
B
ig
Da
t
a
in So
ciet
y
Ad
m
i
nis
t
ra
t
io
n
P
u
b
lic
ad
m
in
i
s
tr
atio
n
h
as
B
i
g
Data
p
r
o
b
lem
s
[
1
4
]
.
u
s
u
all
y
p
o
p
u
latio
n
o
f
o
n
e
co
u
n
tr
y
i
s
v
er
y
lar
g
e.
I
n
ea
ch
ag
e
le
v
el
r
eq
u
ir
e
d
is
ti
n
ct
p
u
b
lic
s
er
v
ice
s
.
Fo
r
in
s
ta
n
ce
,
ad
u
lts
a
n
d
k
id
s
r
eq
u
ir
e
m
o
r
e
e
d
u
ca
tio
n
a
n
d
eld
er
s
n
ee
d
h
i
g
h
lev
el
o
f
h
ea
lt
h
ca
r
e.
in
ev
er
y
p
u
b
lic
s
ec
tio
n
,
ea
ch
p
er
s
o
n
p
r
o
d
u
ce
s
a
lo
t
o
f
d
ata,
s
u
c
h
t
h
at,
to
tal
n
u
m
b
er
o
f
d
ata
ab
o
u
t
p
u
b
lic
ad
m
in
i
s
tr
atio
n
i
n
o
n
e
n
atio
n
is
v
er
y
h
u
g
e.
Fo
r
e
x
a
m
p
le,
b
y
2
0
1
1
th
er
e
ar
e
3
ter
ab
y
te
s
o
f
d
ata
co
llected
b
y
th
e
US
L
ib
r
ar
y
o
f
C
o
n
g
r
es
s
.
I
n
2
0
1
2
,
T
h
e
Ob
a
m
a
ad
m
in
i
s
tr
atio
n
a
n
n
o
u
n
ce
d
th
at
t
h
e
B
ig
Data
r
esear
c
h
an
d
d
ev
elo
p
m
e
n
t
i
n
it
iati
v
e.
I
t
in
v
esti
g
ate
s
an
d
ad
d
r
ess
ed
t
h
at,
b
y
u
s
i
n
g
s
u
c
h
b
i
g
d
ata
g
o
v
er
n
m
en
t
f
ac
i
n
g
t
h
e
p
r
o
b
lem
s
.
Six
d
ep
ar
t
m
e
n
ts
w
er
e
in
v
o
l
v
ed
f
o
r
th
e
in
it
iativ
e
co
n
s
i
s
ts
o
f
8
4
d
is
tin
ct
B
ig
Data
p
r
o
g
r
am
s
.
I
n
E
u
r
o
p
e,
th
is
s
it
u
atio
n
is
r
ep
ea
ted
.
T
o
i
m
p
r
o
v
e
th
e
p
r
o
d
u
ctiv
it
y
o
f
g
o
v
er
n
m
en
t
s
ar
o
u
n
d
th
e
w
o
r
ld
t
h
e
y
ar
e
f
ac
i
n
g
u
n
f
av
o
u
ar
ab
le
cir
cu
m
s
tan
ce
s
.
I
n
p
u
b
lic
ad
m
in
is
tr
atio
n
,
t
h
e
y
ar
e
m
o
r
e
ef
f
ec
t
iv
e.
W
ith
s
ig
n
i
f
ica
n
t
b
u
d
g
etar
y
co
n
s
tr
ai
n
ts
,
in
t
h
e
r
ec
en
t
g
lo
b
al
r
ec
ess
io
n
m
an
y
g
o
v
er
n
m
e
n
t
s
h
a
v
e
to
p
r
o
v
id
e
a
h
ig
h
er
le
v
el
o
f
p
u
b
lic
s
er
v
ices
.
Hen
ce
,
t
h
e
y
w
o
u
ld
ta
k
e
B
ig
Data
as
a
p
o
ten
t
ial
b
u
d
g
e
t
r
es
o
u
r
ce
an
d
d
ev
elo
p
to
o
ls
to
g
et
alter
n
ati
v
e
s
o
l
u
tio
n
s
to
r
ed
u
ce
n
atio
n
al
d
eb
t le
v
e
ls
an
d
d
ec
r
ea
s
e
b
ig
b
u
d
g
et
d
e
f
icits
4
.
2
.
B
ig
Da
t
a
in B
us
ines
s
a
n
d Co
mm
e
rc
e
A
cc
o
r
d
in
g
to
t
h
e
f
o
r
ec
asti
n
g
o
f
[
1
3
]
,
f
o
r
ev
er
y
1
.
2
y
ea
r
s
t
h
e
v
o
lu
m
e
o
f
w
o
r
ld
w
id
e
b
u
s
i
n
es
s
d
ata
ac
r
o
s
s
al
m
o
s
t
co
m
p
an
ie
s
.
Fo
r
ex
a
m
p
le,
i
n
R
e
tail
I
n
d
u
s
tr
y
,
ar
o
u
n
d
2
6
7
m
il
lio
n
tr
a
n
s
ac
tio
n
s
p
er
d
a
y
i
n
W
al
-
Ma
r
t’
s
6
0
0
0
s
to
r
es
w
o
r
ld
w
id
e.
R
ec
en
t
l
y
,
W
al
-
Ma
r
t
i
s
co
l
l
ab
o
r
ated
w
ith
He
w
lett
P
ac
k
ar
d
to
s
to
r
e
4
p
eta
b
y
te
s
o
f
d
ata,
i.e
.
4
0
0
0
tr
illi
o
n
b
y
tes;
it
i
s
tr
ac
ed
f
r
o
m
t
h
eir
p
o
in
t
-
of
-
s
ale
ter
m
i
n
als
f
o
r
e
v
er
y
p
u
r
c
h
ase
r
ec
o
r
d
.
W
ith
th
e
h
elp
o
f
m
ac
h
i
n
e
lear
n
in
g
tech
n
iq
u
es
t
h
e
y
h
av
e
s
u
cc
es
s
f
u
ll
y
i
m
p
r
o
v
ed
th
e
ef
f
ic
ien
c
y
o
f
t
h
eir
ad
v
er
tis
i
n
g
ca
m
p
ai
g
n
s
an
d
p
r
icin
g
s
tr
ateg
ie
s
.
T
h
e
m
a
n
ag
e
m
en
t
o
f
th
eir
in
v
e
n
to
r
y
an
d
Su
p
p
l
y
ch
ai
n
s
ig
n
i
f
ica
n
tl
y
b
en
e
f
it
ted
f
r
o
m
lar
g
e
d
ata
w
ar
eh
o
u
s
e.
Mc
Kin
s
e
y
’
s
R
ep
o
r
t
s
a
y
i
n
g
t
h
a
t
[
1
5
]
,
B
ig
Data
f
u
n
ctio
n
alitie
s
s
u
c
h
as
h
ig
h
er
lev
els
o
f
e
f
f
ec
tiv
e
n
e
s
s
an
d
ef
f
icien
c
y
,
p
r
o
v
id
e
th
e
p
u
b
lic
s
e
cto
r
to
im
p
r
o
v
e
th
e
p
r
o
d
u
ctiv
it
y
a
n
d
r
eser
v
i
n
g
t
h
e
in
f
o
r
m
ati
v
e
p
atter
n
s
a
n
d
k
n
o
wled
g
e.
4
.
3
.
B
ig
Da
t
a
I
n Scient
if
ic
Resea
rc
h
Ma
n
y
o
f
t
h
e
s
cien
ti
f
ic
ar
ea
s
a
r
e
alr
ea
d
y
w
ith
t
h
e
d
ev
elo
p
m
en
t
o
f
co
m
p
u
ter
s
cie
n
ce
s
h
i
g
h
l
y
d
a
ta
-
d
r
iv
en
[
1
6
]
.
Fo
r
ex
am
p
le,
m
e
teo
r
o
lo
g
y
,
astro
n
o
m
y
,
s
o
cial
co
m
p
u
ti
n
g
[
1
7
]
,
co
m
p
u
tat
io
n
al
b
io
lo
g
y
[
1
8
]
an
d
b
io
in
f
o
r
m
at
ics
ar
e
b
ased
o
n
s
cien
ti
f
ic
d
i
s
co
v
er
y
as
m
ass
iv
e
v
o
l
u
m
e
o
f
d
ata
i
s
g
en
er
ated
w
it
h
d
is
ti
n
ct
t
y
p
e
s
th
ese
s
cien
ce
f
ield
s
.
5.
ST
A
T
E
O
F
T
H
E
A
RT
T
O
O
L
S
AND
T
E
CH
NI
Q
UE
S
T
O
H
AND
L
E
DAT
A
-
I
NT
E
NS
I
VE
AP
P
L
I
CA
T
I
O
N
S
5
.
1
.
B
ig
Da
t
a
T
ec
hn
o
lo
g
ies a
nd
T
ec
hn
i
qu
e
s
W
e
n
ee
d
to
d
ev
elo
p
n
e
w
tech
n
o
lo
g
ies
an
d
tec
h
n
iq
u
e
s
to
an
al
y
ze
t
h
e
d
ata
a
n
d
to
ca
p
tu
r
e
th
e
v
alu
e
f
r
o
m
b
ig
d
ata.
T
ill
n
o
w
s
cien
t
is
ts
h
av
e
d
ev
e
lo
p
ed
v
ar
io
u
s
te
ch
n
iq
u
es
to
cu
r
ate,
ca
p
tu
r
e
a
n
al
y
ze
an
d
v
is
u
alize
th
e
B
ig
Data
.
T
h
ese
tech
n
o
lo
g
ies
an
d
tech
n
iq
u
es
cr
o
s
s
e
d
a
n
u
m
b
er
o
f
d
is
cip
li
n
es
s
u
ch
as
ec
o
n
o
m
ic
s
,
co
m
p
u
ter
s
cien
ce
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s
tati
s
tic
s
,
m
at
h
e
m
a
tics
an
d
o
t
h
er
e
x
p
er
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e.
M
u
ltid
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ip
lin
ar
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m
et
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ar
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r
eq
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ir
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s
e
f
u
l
in
f
o
r
m
at
i
o
n
f
r
o
m
B
i
g
Data
.
W
e
w
i
ll
d
is
cu
s
s
p
r
esen
t
tec
h
n
o
lo
g
ies
an
d
tech
n
iq
u
e
s
to
ex
p
lo
it
th
e
d
ata
in
ten
s
i
v
e
ap
p
l
icatio
n
s
.
T
o
m
ak
e
s
e
n
s
e
o
f
B
ig
Data
,
w
e
n
ee
d
to
o
ls
(
p
latf
o
r
m
s
)
.
P
r
esen
t
to
o
ls
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
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IJ
AA
S
Vo
l.
5
,
No
.
2
,
J
u
n
e
2
0
1
6
:
1
0
1
–
1
08
106
f
o
cu
s
in
g
o
n
th
r
ee
cla
s
s
e
s
,
s
u
ch
as
s
tr
ea
m
p
r
o
ce
s
s
in
g
to
o
ls
,
b
atch
p
r
o
ce
s
s
in
g
to
o
ls
a
n
d
in
ter
ac
ti
v
e
an
a
l
y
s
is
to
o
ls
.
Ma
n
y
o
f
b
atch
p
r
o
ce
s
s
in
g
to
o
ls
[
2
0
]
ar
e
b
ased
u
p
o
n
t
h
e
A
p
ac
h
e
Had
o
o
p
in
f
r
ast
r
u
ctu
r
e
a
s
f
o
llo
w
s
Dr
y
ad
an
d
Ma
h
o
u
t.
Fo
r
lar
g
e
s
ca
le
s
tr
ea
m
i
n
g
d
ata
an
al
y
ti
cs
p
latf
o
r
m
s
S4
an
d
Sto
r
m
a
r
e
ex
a
m
p
les.
I
n
a
n
in
ter
ac
ti
v
e
en
v
ir
o
n
m
en
t
,
t
h
e
in
ter
ac
ti
v
e
an
al
y
s
i
s
p
r
o
ce
s
s
es
th
e
d
ata
an
d
allo
w
s
th
e
u
s
er
s
t
o
co
m
m
it
o
n
e
th
eir
o
w
n
an
al
y
s
is
o
f
i
n
f
o
r
m
atio
n
.
I
n
r
ea
l
ti
m
e
u
s
er
is
co
n
n
ec
ted
t
o
P
C
an
d
h
e
ca
n
i
n
ter
ac
t
w
it
h
it.
T
h
e
d
ata
ca
n
b
e
co
m
p
ar
ed
,
r
ev
ie
w
ed
a
n
d
an
a
l
y
ze
d
in
g
r
ap
h
ica
l
f
o
r
m
at
o
r
t
ab
u
lar
f
o
r
m
a
t
o
r
b
o
th
at
t
h
e
s
a
m
e
ti
m
e.
A
p
ac
h
e
Dr
ill
an
d
Go
o
g
le’
s
Dr
e
m
el
ar
e
b
ased
up
on
th
e
in
ter
ac
tiv
e
a
n
al
y
s
i
s
.
5
.
1
.
1
.
B
ig
D
a
t
a
T
ec
hn
iqu
es
B
ig
d
ata
r
eq
u
ir
es
o
u
t
s
tan
d
i
n
g
tech
n
iq
u
es
to
ef
f
icie
n
tl
y
p
r
o
ce
s
s
m
as
s
iv
e
v
o
l
u
m
e
o
f
d
ata
w
it
h
i
n
t
h
e
li
m
ited
r
u
n
ti
m
es.
Fo
r
ex
a
m
p
le,
to
ex
p
lo
r
e
p
atter
n
s
f
r
o
m
th
eir
lar
g
e
v
o
lu
m
e
o
f
tr
a
n
s
ac
t
io
n
d
ata
,
W
al
-
Ma
r
t
ap
p
lies
s
tatis
tical
tec
h
n
iq
u
es
an
d
m
ac
h
i
n
e
lear
n
i
n
g
.
T
h
ese
p
atter
n
s
g
e
n
er
ate
h
i
g
h
co
m
p
e
tin
g
in
ad
v
er
tis
i
n
g
ca
m
p
aig
n
s
an
d
p
r
icin
g
s
tr
ateg
ies.
T
ao
b
ao
,
A
C
h
i
n
e
s
e
co
m
p
an
y
lik
e
eB
a
y
,
o
n
u
s
er
s
’
b
r
o
w
s
e
d
ata
r
ec
o
r
d
ed
o
n
its
w
eb
s
ite
a
n
d
ex
p
lo
it
s
a
g
o
o
d
d
ea
l
o
f
u
s
e
f
u
l
i
n
f
o
r
m
atio
n
t
o
s
u
p
p
o
r
t
th
eir
d
ec
is
io
n
-
m
a
k
i
n
g
,
i
t
w
as
ad
o
p
ted
a
m
as
s
i
v
e
s
tr
ea
m
d
ata
m
i
n
i
n
g
te
ch
n
iq
u
es.
B
ig
d
ata
tec
h
n
iq
u
es
in
v
o
l
v
ed
in
n
u
m
b
er
o
f
ar
ea
s
s
u
ch
a
s
d
ata
m
i
n
i
n
g
,
s
tatis
t
ics,
n
e
u
r
al
n
e
t
w
o
r
k
s
,
m
a
ch
in
e
lear
n
i
n
g
,
s
o
cial
n
et
w
o
r
k
an
al
y
s
i
s
,
p
atter
n
r
ec
o
g
n
itio
n
,
s
ig
n
al
p
r
o
ce
s
s
i
n
g
,
o
p
tim
izatio
n
m
et
h
o
d
s
an
d
v
i
s
u
aliza
tio
n
ap
p
r
o
ac
h
es.
5
.
2
.
Sta
t
is
t
ics
T
o
co
llect,
o
r
g
an
ize
an
d
in
te
r
p
r
et
th
e
d
ata
s
tatis
tics
tec
h
n
iq
u
es
ar
e
u
s
ed
.
T
o
ex
p
lo
it
th
e
ca
s
u
al
r
elatio
n
s
h
ip
an
d
co
r
r
elatio
n
s
h
ip
a
m
o
n
g
d
is
ti
n
ct
o
b
j
ec
tiv
es.
Au
t
h
o
r
s
p
r
o
p
o
s
ed
ef
f
icien
t
ap
p
r
o
x
i
m
a
te
alg
o
r
ith
m
f
o
r
lar
g
e
-
s
ca
le
m
u
l
tiv
ar
iate
m
o
n
o
to
n
ic
r
eg
r
e
s
s
io
n
.
I
t
is
an
ap
p
r
o
ac
h
f
o
r
esti
m
atin
g
f
u
n
ctio
n
s
th
at
ar
e
m
o
n
o
to
n
ic
w
it
h
r
esp
ec
t
to
in
p
u
t
v
ar
iab
les.
A
n
o
t
h
er
tr
en
d
o
f
d
ata
-
d
r
iv
e
n
s
tatis
tical
a
n
al
y
s
i
s
f
o
cu
s
in
g
on
s
ca
le
an
d
p
ar
allel
i
m
p
le
m
e
n
ta
tio
n
o
f
s
tatis
tical
al
g
o
r
ith
m
s
.
W
ith
th
e
h
elp
o
f
s
tati
s
tics
n
u
m
er
ical
d
escr
ip
tio
n
s
ar
e
g
en
er
ated
[
6
]
.
Statis
tical
le
ar
n
in
g
an
d
Sta
tis
tica
l
co
m
p
u
t
i
n
g
ar
e
t
h
e
t
w
o
h
o
t r
esear
ch
s
u
b
-
f
ield
s
.
5
.
3
.
O
pti
m
iza
t
io
n M
e
t
ho
ds
T
o
s
o
lv
e
q
u
an
t
itati
v
e
p
r
o
b
le
m
s
i
n
m
a
n
y
ar
ea
s
s
u
c
h
a
s
b
io
lo
g
y
,
p
h
y
s
ic
s
,
ec
o
n
o
m
ics
an
d
e
n
g
i
n
ee
r
i
n
g
Op
ti
m
izatio
n
m
et
h
o
d
s
ar
e
a
p
p
lied
.
I
n
[
1
9
]
,
v
ar
io
u
s
co
m
p
u
tat
io
n
al
s
tr
ate
g
ies
ar
e
ad
d
r
ess
ed
f
o
r
g
lo
b
al
o
p
tim
izatio
n
p
r
o
b
le
m
s
s
u
ch
as
ad
ap
tiv
e
s
i
m
u
lated
an
n
ea
l
in
g
,
s
i
m
u
lated
an
n
ea
li
n
g
g
en
etic
alg
o
r
ith
m
a
n
d
q
u
an
t
u
m
an
n
ea
li
n
g
.
Sto
c
h
asti
c
o
p
ti
m
izatio
n
in
c
lu
d
es
e
v
o
l
u
tio
n
ar
y
p
r
o
g
r
a
m
m
i
n
g
;
g
en
e
tic
p
r
o
g
r
am
m
i
n
g
a
n
d
p
ar
ticle
s
w
ar
m
o
p
ti
m
izat
io
n
ar
e
u
s
e
f
u
l.
Mo
s
t
o
f
th
e
r
esear
ch
w
o
r
k
s
ar
e
d
o
n
e
to
s
ca
le
u
p
lar
g
e
-
s
ca
le
o
p
tim
izatio
n
b
y
co
-
e
v
o
lu
tio
n
ar
y
al
g
o
r
ith
m
s
.
R
ea
l
-
ti
m
e
o
p
tim
izat
io
n
is
ne
ed
ed
in
v
ar
io
u
s
B
ig
Dat
a
ap
p
licatio
n
,
s
u
c
h
as
I
T
Ss
an
d
W
SNs
.
P
ar
alleliza
tio
n
an
d
Data
r
ed
u
ctio
n
ar
e
also
alter
n
ativ
e
ap
p
r
o
ac
h
es
in
o
p
tim
izatio
n
p
r
o
b
le
m
s
.
5
.
4.
D
a
t
a
M
ini
ng
Data
m
in
in
g
i
s
a
co
llectio
n
o
f
tech
n
iq
u
e
s
to
e
x
tr
ac
t
u
s
ef
u
l
p
atter
n
s
f
r
o
m
d
ata
su
c
h
a
s
C
las
s
if
ica
tio
n
an
d
C
lu
s
ter
i
n
g
an
a
l
y
s
is
,
as
s
o
ciatio
n
r
u
le
m
in
i
n
g
,
an
d
r
eg
r
ess
io
n
,
d
is
cr
i
m
in
a
te
an
al
y
s
i
s
.
I
t
in
v
o
lv
es
th
e
m
et
h
o
d
s
f
r
o
m
s
tat
is
tic
s
an
d
m
ac
h
in
e
lear
n
in
g
.
W
h
en
co
m
p
ar
ed
to
co
n
v
en
tio
n
a
l
d
ata
m
i
n
in
g
al
g
o
r
ith
m
s
B
ig
Data
m
in
in
g
is
a
C
h
al
len
g
i
n
g
is
s
u
e.
Mo
s
t
o
f
t
h
e
e
x
te
n
s
io
n
s
u
s
u
all
y
r
el
ie
s
o
n
a
n
al
y
zin
g
a
p
ar
ticu
lar
a
m
o
u
n
t
o
f
s
a
m
p
les
o
f
B
i
g
Data
,
a
n
d
v
ar
y
i
n
h
o
w
t
h
e
s
a
m
p
le
-
b
ased
r
esu
lt
s
ar
e
u
s
ed
to
d
er
iv
e
a
p
ar
titi
o
n
f
o
r
th
e
o
v
er
al
l
d
ata.
C
lu
s
ter
i
n
g
al
g
o
r
ith
m
s
s
u
c
h
as
C
L
AR
A
(
C
l
u
s
ter
i
n
g
L
AR
g
e
A
p
p
licatio
n
s
)
alg
o
r
ith
m
,
C
L
AR
A
N
S
(
C
lu
s
ter
in
g
L
ar
g
e
A
p
p
licatio
n
s
b
ased
u
p
o
n
R
A
Nd
o
m
ized
Sear
ch
)
,
B
I
R
C
H
(
B
alan
ce
d
I
ter
ativ
e
R
ed
u
ci
n
g
u
s
i
n
g
C
l
u
s
ter
Hier
ar
ch
ies)
al
g
o
r
ith
m
,
etc.
T
o
r
ef
lect
th
e
g
o
o
d
n
ess
Ge
n
etic
al
g
o
r
ith
m
s
ar
e
also
ap
p
lied
to
clu
s
ter
i
n
g
as
o
p
ti
m
iza
tio
n
cr
it
er
io
n
.
So
cial
Net
w
o
r
k
A
n
a
l
y
s
i
s
(
SN
A
)
is
e
m
er
g
ed
as a
k
e
y
t
ec
h
n
iq
u
e
i
n
m
o
d
er
n
s
o
cio
lo
g
y
,
v
ie
w
s
s
o
cial
r
elati
o
n
s
h
ip
s
in
ter
m
s
o
f
n
et
w
o
r
k
t
h
eo
r
y
;
it
co
n
s
i
s
ts
o
f
n
o
d
es
an
d
ties
.
Vis
u
aliza
tio
n
A
p
p
r
o
ac
h
es
ar
e
th
e
tec
h
n
iq
u
e
s
u
s
ed
to
cr
ea
te
d
iag
r
a
ms
,
ta
b
les,
i
m
a
g
es
a
n
d
o
th
er
in
t
u
iti
v
e
d
is
p
la
y
w
a
y
s
to
u
n
d
er
s
ta
n
d
d
ata.
Ma
ch
i
n
e
lea
r
n
in
g
is
a
n
i
m
p
o
r
tan
t
s
u
b
j
ec
t
o
f
ar
tif
icia
l
in
te
lli
g
en
ce
.
I
t
i
s
ai
m
ed
to
d
esi
g
n
alg
o
r
ith
m
s
t
h
at
allo
w
co
m
p
u
te
r
s
to
ev
o
lv
e
b
eh
a
v
io
r
s
b
ased
o
n
e
m
p
ir
ical
d
ata.
B
ig
Data
to
o
ls
f
o
r
b
atch
p
r
o
ce
s
s
i
n
g
1)
Kar
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f
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2
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IBM
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IBM
,
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[6
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C.
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,
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2
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4
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Eri
c
S
a
v
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,
G
a
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Crit
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Do
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3
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Da
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m
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m
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trate
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ro
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).
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d
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trea
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M
c
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ra
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2
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Evaluation Warning : The document was created with Spire.PDF for Python.
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Evaluation Warning : The document was created with Spire.PDF for Python.