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o a s
pec
i
f
i
c
am
ount
of
dat
a,
b
ut
t
o
d
es
c
r
i
be a
s
et
of
dat
a t
h
a
t
c
annot
be s
t
or
e
d or
pr
oc
es
s
ed us
i
n
g
t
r
adi
t
i
on
al
d
at
a
bas
e s
of
t
w
a
r
e.
E
x
am
pl
es
of
bi
g dat
a i
nc
l
ud
e G
oog
l
e S
ear
c
h I
n
d
ex
,
F
ac
ebook
dat
a
bas
e (
us
er
pr
of
i
l
e)
[
8
,
1
4]
.
B
i
g
dat
a
i
s
of
t
en
di
s
t
r
i
b
u
t
ed o
v
er
m
an
y
s
t
or
ag
e d
ev
i
c
es
,
c
an be
i
n
s
ev
er
a
l
di
f
f
e
r
ent
l
oc
at
i
ons
.
T
her
e ar
e s
ev
er
al
d
i
f
f
er
ent
t
y
p
es
of
s
o
f
t
w
ar
e s
ol
ut
i
ons
of
di
f
f
er
ent
bi
g
dat
a,
i
nc
l
ud
i
ng
dat
a
s
t
or
ag
e
pl
at
f
or
m
s
and
dat
a
an
al
y
s
i
s
pr
ogr
am
s
.
T
he
m
os
t
c
om
m
on
pr
oduc
t
of
t
he s
of
t
w
ar
e i
nc
l
u
des
A
pac
he H
ad
oo
p bi
g d
at
a,
I
B
M'
s
B
i
g D
at
a P
l
at
f
or
m
,
O
r
ac
l
e N
oS
Q
L
dat
a
bas
e,
Mi
c
r
os
of
t
and
E
MC
H
D
I
ns
i
g
ht
P
i
v
ot
a
l
O
n
e [
8]
.
O
t
her
def
i
n
i
t
i
ons
,
B
i
g
D
at
a
i
s
a
dat
a
ov
er
f
l
o
w
i
n
pac
e
nev
er
s
een
b
ef
or
e
-
has
doub
l
ed
ev
er
y
18
m
ont
hs
-
as
a
r
es
ul
t
of
ac
c
es
s
t
o
a
l
ar
g
er
c
us
t
om
er
dat
a
der
i
v
ed
f
r
om
p
ubl
i
c
s
our
c
es
,
e
xcl
u
si
v
el
y
,
as
w
el
l
as
ne
w
i
nf
or
m
at
i
on gat
her
e
d f
r
o
m
t
he w
eb
c
om
m
uni
t
y
dep
l
o
y
ed i
n a w
a
y
ne
w
[
3]
.
I
dent
i
f
y
t
h
e k
e
y
el
em
ent
s
of
bi
g
da
t
a i
s
,
f
i
r
s
t
c
om
pani
es
t
od
a
y
c
a
n c
ol
l
ec
t
d
at
a
ac
r
os
s
bus
i
n
es
s
uni
t
s
,
i
nc
r
eas
i
n
g
t
he dat
a,
e
v
en d
at
a f
r
om
par
t
ner
s
and c
us
t
om
er
s
(
l
ar
ge and
c
o
m
pl
ex
)
.
S
ec
o
nd,
a f
l
ex
i
b
l
e i
nf
r
as
t
r
uc
t
ur
e t
h
at
c
an
i
nt
egr
at
e
i
nf
or
m
at
i
on an
d ef
f
ec
t
i
v
e
l
y
t
o m
eet
t
he i
nc
r
eas
i
ng
w
a
v
e of
l
ar
ge dat
a.
T
hi
r
d,
ex
per
i
m
ent
al
and an
al
y
t
i
c
al
gor
i
t
hm
c
an
m
a
k
e
s
ens
e of
al
l
t
he
i
nf
or
m
at
i
on of
bi
g d
a
t
a.
B
i
g
D
at
a
has
a
l
s
o
b
ec
o
m
e a c
or
e el
em
ent
of
t
he s
t
r
at
eg
y
[
3]
.
B
i
g
D
at
a
f
oc
us
on
t
he
s
i
z
e
of
t
he
dat
a
i
n
s
t
or
ag
e.
T
he
s
i
z
e
of
t
he
i
s
s
ue,
bu
t
t
h
e
r
e
ar
e
ot
her
i
m
por
t
ant
at
t
r
i
but
es
of
bi
g dat
a i
s
t
he di
v
er
s
i
t
y
of
dat
a and dat
a r
at
es
.
T
h
r
ee V
bi
g d
at
a
(
v
ol
um
e,
v
ar
i
et
y
a
nd
v
e
l
oc
i
t
y
)
es
t
ab
l
i
s
hes
a c
om
pr
ehens
i
v
e d
ef
i
ni
t
i
o
n,
an
d t
h
us
r
educ
i
n
g t
h
e
m
y
t
h t
h
at
bi
g d
at
a
i
s
j
us
t
a
bout
t
he
v
ol
um
e of
dat
a [
6]
.
I
n
3
V
b
i
g
da
t
a,
r
el
a
t
ed
t
o
t
he
s
i
z
e
of
t
he
v
o
l
um
e
t
hat
i
s
T
er
r
ab
y
t
e,
r
ec
or
ds
,
t
r
ans
ac
t
i
ons
,
t
ab
l
es
and
f
i
l
es
.
V
e
l
oc
i
t
y
r
e
l
at
ed
t
o
bat
c
h
,
n
ear
-
t
i
m
e,
r
eal
t
i
m
e,
s
t
r
ea
m
(
s
t
r
eam
)
.
W
hi
l
e
V
ar
i
et
y
r
e
l
at
i
n
g
t
o
s
t
r
uc
t
ur
ed,
uns
t
r
uc
t
ur
ed,
s
em
i
-
s
t
r
uc
t
ur
ed an
d t
hr
ee.
A
s
s
h
o
w
n
i
n t
he f
i
gur
e be
l
o
w
.
F
i
gur
e
1.
T
he T
hr
ee of
V
B
i
g dat
a
T
he
v
o
l
um
e
of
dat
a
i
s
a
m
aj
or
at
t
r
i
but
e
of
b
i
g
dat
a,
m
o
s
t
peo
p
l
e
def
i
n
e
t
he
s
i
z
e
of
b
i
g
dat
a
-
t
er
ab
y
t
e
(
T
B
)
,
s
o
m
et
i
m
e
s
i
n
pet
ab
y
t
es
(
3
t
o
1
0
T
B
)
.
S
o
m
e
or
gani
z
at
i
o
ns
f
i
nd
m
or
e
us
ef
ul
f
or
t
he quant
i
f
i
c
at
i
on of
bi
g
dat
a i
n t
er
m
s
of
t
i
m
e,
s
uc
h
as
A
c
t
l
i
m
i
t
at
i
ons
s
ev
en
y
e
ar
s
i
n A
m
er
i
c
a
,
m
an
y
c
om
pani
es
m
ai
nt
a
i
n
dat
a f
or
s
e
v
en
y
e
ar
s
f
or
r
i
s
k
anal
y
s
i
s
,
c
om
pl
ai
nt
s
an
d l
a
w
[
6]
.
B
i
g d
at
a i
s
a di
f
f
i
c
ul
t
par
t
of
t
he c
o
m
pet
i
t
i
on f
or
m
ar
k
et
s
har
e.
I
t
i
s
i
m
por
t
ant
t
o not
e t
hat
t
he t
hr
eat
s
and
o
ppor
t
u
ni
t
i
es
as
s
oc
i
at
e
d
w
i
t
h
l
ar
ge
da
t
a of
t
en
has
i
m
pl
i
c
at
i
ons
f
o
r
or
gan
i
z
at
i
ons
t
hat
on
l
y
t
h
e at
t
ent
i
o
n of
s
e
ni
or
ex
ec
ut
i
v
es
c
an
han
dl
e
i
t
.
Lea
der
s
ar
e
t
o
o l
i
t
t
l
e
t
o
u
nder
s
t
an
d t
he
pot
e
nt
i
al
of
bi
g
dat
a i
n t
h
ei
r
b
us
i
nes
s
,
d
at
a
as
s
et
s
and
l
i
ab
i
l
i
t
i
es
of
t
he
bus
i
nes
s
,
or
t
h
ei
r
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KO
M
NI
K
A
I
S
S
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:
1
693
-
6
930
B
i
g
D
at
a A
na
l
y
s
i
s
w
i
t
h
M
o
n
goD
B
f
or
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ec
i
s
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n S
upp
or
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em
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1085
s
t
r
at
egi
c
c
ho
i
c
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i
s
t
o
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e
m
ade
t
o
beg
i
n
ut
i
l
i
z
i
ng
l
ar
ge
dat
a.
B
y
f
oc
us
i
n
g
on
t
hes
e
i
s
s
ues
,
s
eni
or
ex
ec
ut
i
v
es
c
an
he
l
p or
gan
i
z
at
i
ons
bui
l
d
a c
om
pet
i
t
i
v
e
adv
ant
age
bas
ed
on
t
he
da
t
a [
3]
.
T
ec
hnol
og
y
t
o
us
e
and
a
n
al
y
z
e
t
he
i
nf
or
m
at
i
on w
i
d
el
y
a
v
ai
l
a
bl
e
,
but
m
an
y
c
om
pan
i
es
ar
e
t
ak
i
ng
a
ne
w
l
e
v
e
l
dat
a
us
i
ng
I
T
t
o
s
upp
or
t
a
ppr
opr
i
at
el
y
,
di
r
ec
t
i
n
g
d
ec
i
s
i
o
n
s
and
t
es
t
ne
w
pr
oduc
t
s
,
bus
i
nes
s
m
odel
s
,
and i
n
no
v
at
i
o
n t
o t
he c
u
s
t
om
er
ex
per
i
enc
e,
i
n s
om
e
c
as
es
t
hi
s
appr
o
ac
h hel
ps
c
om
pani
es
t
o
m
a
k
e dec
i
s
i
ons
i
n r
eal
t
i
m
e.
T
he c
o
m
pan
y
s
el
l
s
ph
y
s
i
c
al
pr
od
uc
t
s
al
s
o
us
e
bi
g
dat
a
t
o
t
he
ap
pr
opr
i
a
t
e
ex
per
i
m
ent
s
.
U
s
e
t
he
i
nf
or
m
at
i
on
t
o
anal
y
z
e
ne
w
bus
i
nes
s
oppor
t
un
i
t
i
es
,
s
uc
h as
t
he
ef
f
ec
t
i
v
e pr
om
ot
i
on
of
t
he
r
i
ght
s
egm
ent
.
O
t
her
c
om
pan
i
es
c
ol
l
ec
t
dat
a f
r
om
s
oc
i
al
ne
t
w
or
k
s
i
n r
eal
t
i
m
e (
F
or
d Mot
or
,
P
e
ps
i
C
o a
nd
S
out
h
w
es
t
A
i
r
l
i
n
es
)
.
T
he us
e o
f
ex
per
i
m
en
t
al
and
bi
g dat
a
as
an i
m
por
t
ant
c
om
pone
nt
i
n m
anagem
ent
dec
i
s
i
on m
ak
i
ng r
equ
i
r
es
ne
w
c
ap
ab
i
l
i
t
i
es
,
as
w
e
l
l
a
s
or
gani
z
a
t
i
ona
l
a
nd c
u
l
t
ur
al
c
ha
nge
,
m
os
t
c
o
m
pani
e
s
aw
a
y
f
r
o
m
ac
c
es
s
i
ng al
l
t
he
dat
a
av
a
i
l
a
bl
e.
G
ener
al
l
y
,
t
he c
om
pan
y
do
es
not
ha
v
e t
he r
i
ght
t
a
l
en
t
and
pr
oc
es
s
i
ng
f
or
d
es
i
g
ni
n
g
e
x
per
i
m
ent
s
and
ex
t
r
ac
t
bus
i
nes
s
v
al
ue
f
r
om
bi
g
d
at
a,
w
hi
c
h
r
equ
i
r
es
a
c
hange
i
n
t
he
w
a
y
m
an
y
ex
ec
ut
i
v
es
w
ho
c
ur
r
ent
l
y
m
a
k
e
a
dec
i
s
i
on:
t
o
t
r
us
t
i
ns
t
i
nc
t
/
i
ns
t
i
nc
t
and
ex
p
er
i
e
nc
e
dur
i
ng
t
he
ex
per
i
m
ent
and
r
i
gor
o
us
a
nal
y
s
i
s
.
B
i
g
dat
a
o
v
er
t
i
m
e
w
i
l
l
be
a
ne
w
t
y
p
e of
c
o
m
pan
y
as
s
et
s
,
w
hi
c
h i
nd
i
c
at
es
an i
m
por
t
a
nt
k
e
y
t
o t
h
e c
om
pet
i
t
i
o
n.
I
f
i
t
i
s
t
r
ue,
t
he
c
o
m
pan
y
s
h
ou
l
d s
t
ar
t
t
h
i
nk
i
ng s
er
i
ous
l
y
w
he
t
her
t
he
y
ar
e or
g
an
i
z
ed t
o ex
p
l
o
i
t
t
he
p
ot
e
nt
i
al
of
l
ar
ge
dat
a (
bi
g d
at
a)
a
nd t
o m
anage t
h
e t
hr
e
at
s
t
ha
t
m
ay
ar
i
s
e.
S
uc
c
es
s
w
i
l
l
r
e
qui
r
e
not
on
l
y
ne
w
s
k
i
l
l
s
,
but
al
s
o a n
e
w
per
s
pec
t
i
v
e on h
o
w
t
o r
e
v
o
l
ut
i
on
i
z
e t
he er
a
of
bi
g dat
a
-
ex
p
and
i
n
g
c
i
r
c
l
e
of
m
anagem
ent
pr
ac
t
i
c
es
t
hat
c
a
n
i
nf
l
uenc
e
an
d
f
oundat
i
o
ns
r
epr
es
ent
no
v
e
l
t
y
,
pot
ent
i
a
l
bus
i
n
es
s
m
odel
s
(
di
s
r
upt
i
v
e)
[
2]
.
I
nf
or
m
at
i
on
W
ee
k
S
ur
v
e
y
(
2
012)
,
ad
v
i
s
e t
h
at
bef
or
e bu
y
i
n
g s
t
or
age
,
w
ar
eho
us
e pl
at
f
or
m
upgr
a
de,
or
ad
opt
i
ng
H
a
doop
ne
ed t
o c
hec
k
r
eal
i
t
y
.
O
t
her
s
ur
v
e
y
dat
a r
el
at
ed t
o t
h
e
c
ons
i
der
a
t
i
o
n of
I
nf
or
m
at
i
on
W
ee
k
B
i
g D
at
a ana
l
y
s
i
s
t
ool
,
w
hi
c
h s
h
o
w
s
t
hat
t
he c
ons
i
d
er
at
i
on f
or
t
he a
na
l
y
s
i
s
of
bi
g
dat
a
t
oo
l
s
onl
y
1
5%
c
ons
i
der
,
4
1%
di
d
not
k
no
w
a
nd
44%
do n
ot
c
ons
i
der
.
Mana
gem
ent
or
m
anagem
ent
of
bi
g
da
t
a
i
s
t
o
or
gani
z
e,
adm
i
ni
s
t
er
an
d
go
v
er
n
anc
e
of
l
ar
ge
v
o
l
um
es
of
dat
a bot
h
s
t
r
uc
t
ur
ed an
d uns
t
r
uc
t
ur
e
d.
T
he pur
pos
e of
t
he m
anagem
ent
of
bi
g
dat
a
i
s
t
o
e
ns
ur
e
a
h
i
gh
l
e
v
e
l
of
dat
a
qu
al
i
t
y
a
nd
ac
c
es
s
i
bi
l
i
t
y
f
or
bus
i
nes
s
pur
p
os
es
,
i
nc
l
u
di
ng
b
u
s
in
e
s
s
in
t
e
ll
ig
e
nc
e an
d bi
g d
at
a a
na
l
y
s
i
s
a
ppl
i
c
at
i
ons
.
C
or
por
a
t
i
o
ns
,
go
v
er
n
m
ent
agenc
i
es
and
ot
her
or
ga
ni
z
at
i
o
ns
us
i
ng
a
l
ar
ge
dat
a
m
anagem
ent
s
t
r
at
eg
i
es
t
o
h
el
p
t
hem
c
o
m
pet
e
w
i
t
h
r
api
d
l
y
gr
o
w
i
n
g
da
t
a
s
et
s
,
us
ual
l
y
i
n
v
ol
v
es
a
l
o
t
of
t
e
r
ab
y
t
es
or
e
v
e
n pe
t
ab
y
t
es
of
i
nf
or
m
at
i
on
and
v
ar
i
ous
t
y
p
es
of
dat
a.
MG
I
m
ent
i
ons
t
h
e p
ot
e
nt
i
al
b
enef
i
t
s
of
bi
g
dat
a be
t
w
een
pr
i
v
at
e an
d p
ub
l
i
c
s
ec
t
or
s
,
id
e
n
t
if
ie
d
f
iv
e
w
a
y
s
b
i
g
dat
a c
an c
r
eat
e a
v
al
u
e
:
S
eg
m
ent
i
ng t
h
e
au
di
enc
e
t
o
a
dj
us
t
a
c
t
i
v
it
ie
s
,
c
r
eat
e
t
r
ans
par
e
nc
y
,
S
up
por
t
/
r
ep
l
ac
e
h
um
an
j
udgm
ent
w
i
t
h
a
ut
om
at
ed
al
g
or
i
t
hm
,
E
nab
l
es
ex
per
i
m
ent
at
i
on,
I
nno
v
at
e
bus
i
n
es
s
m
odel
s
,
ne
w
pr
od
uc
t
s
and s
er
v
i
c
es
[
9]
.
O
f
s
o
m
e i
ndus
t
r
i
es
,
MG
I
d
em
ons
t
r
at
ed t
he
b
enef
i
t
s
o
f
bi
g
dat
a
:
U
S
hea
l
t
h
c
ar
e (
$300
bi
l
l
i
on v
al
ue per
y
e
ar
)
,
E
ur
o
pe P
u
bl
i
c
S
ec
t
or
adm
i
ni
s
t
r
a
t
i
on (
€ 250 b
i
l
l
i
on v
al
u
e per
y
ear
)
,
V
l
oba
l
per
s
ona
l
l
oc
at
i
on
dat
a
(
$10
0 bi
l
l
i
o
n)
,
U
S
r
et
a
i
l
(
6
0
+
%
i
nc
r
eas
e i
n ne
t
m
ar
gi
n)
[
9]
.
B
ec
aus
e of
t
he
w
a
y
t
o s
ec
u
r
e a c
o
m
pet
i
t
i
v
e a
dv
ant
a
ge
of
bi
g dat
a i
s
s
t
i
l
l
ev
ol
v
i
ng,
s
o
m
e
C
E
O
s
bel
i
e
v
e t
hat
b
i
g
dat
a
i
ni
t
i
a
t
i
v
es
(
bi
g dat
a)
s
hou
l
d
be t
he r
es
p
ons
i
bi
l
i
t
y
of
t
he I
T
depar
t
m
ent
or
m
ar
k
et
i
ng
s
pec
i
a
l
i
z
e
d
c
om
pani
es
-
l
ar
ge
-
s
c
al
e
f
un
c
t
i
ona
l
gr
ou
p
w
her
e
i
n
t
h
e
am
ount
of
dat
a
t
hat
i
s
m
os
t
f
r
equent
l
y
g
at
her
ed,
an
al
y
z
ed,
an
d
i
m
pl
em
ent
ed.
T
her
ef
or
e as
s
oc
i
at
e
d
w
i
t
h
bi
g
dat
a,
h
er
e
ar
e s
om
e t
hi
ngs
t
hat
c
o
ul
d
be
t
h
e C
E
O
an
d
hi
s
t
e
am
v
i
e
w
,
a)
c
an
be
de
r
i
v
e
d f
r
om
t
he
per
f
ec
t
oppor
t
u
ni
t
y
c
or
e
op
er
at
i
o
ns
t
o c
r
eat
e ne
w
bus
i
nes
s
l
i
n
es
-
ev
en
i
n t
he s
a
m
e i
ndus
t
r
y
.
b)
T
o be us
ef
ul
,
t
h
e d
at
a
m
us
t
t
r
av
er
s
e b
et
w
e
en
t
he
or
ga
ni
z
at
i
on
-
b
u
t i
t
of
t
en c
aus
e
s
f
r
i
c
t
i
on.
O
n
l
y
t
he s
en
i
or
t
e
am
of
dedi
c
at
e
d and
f
oc
us
ed c
an e
l
i
m
i
nat
e v
ar
i
ous
p
ur
pos
es
/
o
bj
ec
t
i
o
ns
,
c
)
w
h
et
her
a
c
o
m
pan
y
p
l
an
ni
n
g
i
n
i
t
i
at
i
v
e,
a
s
i
n
gl
e
l
ar
ge
or
s
m
al
l
er
t
hat
m
uc
h,
s
eni
or
t
eam
s
houl
d
ac
t
i
v
el
y
pl
a
n
t
o
t
ak
e
adv
a
nt
a
ge
of
t
he
opp
or
t
u
n
i
t
i
es
gen
er
at
e
d
.
S
t
a
y
a
w
ar
e
of
t
he
nec
es
s
ar
y
r
es
our
c
es
(
t
ec
hnol
og
y
a
nd
v
i
c
e
v
er
s
a)
qui
c
k
l
y
s
h
i
f
t
ed i
nt
o
a m
ode of
i
m
pl
e
m
ent
at
i
on of
t
h
e p
i
l
ot
.
A
l
s
o,
t
he
ot
her
ad
v
ant
a
ge
of
t
he
bi
g
dat
a
i
s
our
s
m
ar
t
pl
an
et
b
ec
om
es
m
o
r
e
and
m
or
e
i
nt
e
l
l
i
g
ent
.
B
es
i
des
t
he c
h
al
l
eng
es
pos
ed
b
y
s
uc
h
v
as
t
am
ount
of
dat
a,
t
her
e
ar
e al
s
o m
uc
h
oppor
t
un
i
t
i
es
f
or
t
he
w
or
l
d
as
i
t
b
ec
om
es
m
or
e and m
or
e di
gi
t
al
i
z
ed
[
13]
.
N
oS
Q
L D
at
a
bas
e pr
o
v
i
des
a
m
ec
hani
s
m
f
or
t
he s
t
or
age a
nd r
et
r
i
ev
al
of
dat
a bei
ng
m
odel
ed
i
n ot
h
er
w
a
y
s
b
es
i
des
t
h
e r
el
at
i
ons
h
i
p
t
a
bl
e
us
ed i
n
r
el
at
i
on
al
d
at
abas
es
.
T
he
m
ot
i
v
at
i
o
n f
or
t
h
i
s
a
ppr
o
ac
h i
nc
l
u
de s
i
m
pl
i
c
i
t
y
of
des
i
g
n,
h
or
i
z
ont
al
s
c
al
e
an
d
bet
t
er
c
o
nt
r
o
l
o
v
er
av
a
i
l
ab
i
l
i
t
y
.
D
at
a s
t
r
uc
t
ur
e
s
(
e.
g.
t
r
ees
,
gr
aphs
,
k
e
y
-
v
a
l
ue)
i
s
di
f
f
er
ent
f
r
o
m
t
he R
D
B
M
S
,
and
t
her
ef
or
e s
om
e oper
at
i
ons
f
as
t
er
i
n
N
o
S
Q
L a
nd s
om
e i
n t
he
R
D
B
MS
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SSN
:
1
6
9
3
-
6
930
T
E
L
KO
M
NI
K
A
V
o
l.
1
4
,
N
o
.
3
,
S
ept
em
ber
201
6
:
10
83
–
1
089
1086
B
en
ef
i
t
s
of
N
oS
Q
L dat
a
ba
s
es
[
7]
:
1.
S
c
al
e el
as
t
i
c
,
2
.
B
i
g D
at
a,
3
.
Li
t
t
l
e
m
ai
nt
e
nanc
e
(
s
l
i
ght
l
y
adm
i
ni
s
t
r
at
i
on an
d
t
uni
ng)
,
4.
C
h
ea
p,
5.
F
l
ex
i
b
l
e dat
a m
odel
.
F
i
v
e c
hal
l
e
n
ges
t
o N
oS
Q
L
dat
a
bas
e
i
s
1.
M
at
ur
i
t
y
,
2.
S
up
por
t
(
s
up
por
t
)
,
3.
A
na
l
y
t
i
c
s
a
nd
B
us
i
nes
s
I
nt
el
l
i
genc
e
,
4
.
A
dm
i
ni
s
t
r
at
i
on
a
nd
5.
E
x
pe
r
t
i
s
e.
N
o
S
Q
L
da
t
ab
as
es
be
c
o
m
e
an
i
m
por
t
ant
par
t
of
t
he
l
a
nds
c
ap
e
dat
a
bas
e,
and
w
he
n us
ed
pr
oper
l
y
,
c
an of
f
er
r
eal
benef
i
t
s
.
H
o
w
e
v
er
,
t
he c
o
m
pan
y
s
h
ou
l
d
pr
oc
eed
w
i
t
h
t
he
f
ul
l
at
t
en
t
i
on
of
t
he
l
i
m
i
t
at
i
o
ns
of
l
egi
t
i
m
ac
y
and
pr
o
bl
em
s
as
s
oc
i
at
ed
w
i
t
h
t
h
i
s
dat
a
bas
e.
H
ado
op
D
i
s
t
r
i
b
ut
ed
F
i
l
e
S
y
s
t
em
(
H
D
F
S
)
i
s
a
d
i
s
t
r
i
b
ut
ed
f
i
l
e
s
y
s
t
em
des
i
gne
d
t
o
r
un
on
c
o
m
m
odi
t
y
har
d
w
ar
e.
I
t
ha
s
m
an
y
s
i
m
i
l
ar
i
t
i
es
w
i
t
h
ex
i
s
t
i
ng
d
i
s
t
r
i
b
ut
ed
f
i
l
e
s
y
s
t
em
.
H
o
w
e
v
er
,
t
h
e
di
f
f
er
enc
es
f
r
o
m
ot
her
di
s
t
r
i
but
e
d f
i
l
e s
y
s
t
em
i
s
s
i
gni
f
i
c
ant
.
H
D
F
S
h
i
gh
l
y
f
au
l
t
-
t
ol
er
a
nt
an
d i
s
d
es
i
g
ned f
or
us
e
on l
o
w
-
c
os
t
har
d
w
ar
e
.
H
D
F
S
pr
o
v
i
des
hi
g
h t
hr
ou
gh
put
ac
c
es
s
t
o
appl
i
c
at
i
on
dat
a
and
i
s
s
ui
t
ab
l
e f
or
ap
pl
i
c
at
i
ons
t
h
at
h
av
e a
l
ar
g
e d
at
a s
et
(
b
i
g
dat
a)
[
14
]
.
2.
R
e
sea
r
ch
M
et
h
o
d
T
hi
s
s
t
ud
y
be
gi
ns
w
i
t
h
a
l
i
t
er
at
ur
e
s
t
u
d
y
f
ur
t
her
a
bout
bi
g
dat
a
,
c
ol
l
ec
t
i
ng
i
nf
or
m
at
i
on
t
o
c
r
eat
e a m
odel
/
pr
ot
ot
y
p
e
bi
g d
at
a
app
l
i
c
at
i
o
ns
.
N
ex
t
i
s
t
o c
ol
l
ec
t
s
am
pl
es
of
s
t
r
u
c
t
ur
ed dat
a t
o
r
es
ear
c
h
obj
ec
t
,
t
he
dat
a
s
am
pl
e
i
s
a
s
am
pl
e
of
dat
a
f
r
o
m
a
S
Q
L
S
er
v
er
d
at
ab
a
s
e
t
hat
i
s
us
e
d
i
n
t
h
e
op
er
at
i
on.
P
r
ot
ot
y
p
e
t
hat
c
r
eat
ed
us
i
ng
a
d
at
a
b
as
e
t
hat
a
bl
e
s
t
or
e
d
i
f
f
er
ent
t
y
p
es
of
dat
a
(
uns
t
r
uc
t
ur
ed)
i
s
N
oS
Q
L
(
Mong
oD
B
)
.
E
x
p
er
i
m
ent
s
hav
e be
en
don
e
w
i
t
h
t
he
i
ns
t
a
l
l
at
i
on
m
ongodb
(
N
oS
Q
L)
and
p
er
f
or
m
dat
a
c
onv
er
s
i
on
f
r
o
m
s
t
r
uc
t
ur
ed
dat
a
bas
es
t
o
m
o
ngod
b
f
or
m
at
(
doc
um
ent
)
[
12]
.
3
.
R
e
su
l
t
s
a
n
d
A
n
a
l
y
s
i
s
B
as
ed
on t
he
ex
per
i
m
ent
[
1
2]
,
t
h
e pr
ot
ot
y
pe s
t
eps
ar
e:
1.
I
ns
t
al
l
i
ng t
he O
p
er
at
i
ng
S
y
s
t
em
(
W
i
ndow
s
S
er
v
er
20
12
)
2.
I
ns
t
al
l
i
ng a
nd
S
et
t
i
n
g a
dat
abas
e N
oS
Q
L (
Mo
ngoD
B
)
F
i
gur
e
2.
I
ns
t
al
l
M
ong
oD
B
S
er
v
i
c
e
3.
C
onn
ec
t
i
o
n t
es
t
t
o M
ong
oD
B
4.
Mi
gr
a
t
i
o
n f
r
om
S
Q
L S
er
v
er
dat
a
bas
e t
o M
ong
oD
B
F
or
pr
ot
ot
y
pe
Mo
ngoD
B
dat
a
bas
e,
t
he d
at
ab
as
e u
s
ed i
s
t
he r
es
ul
t
of
m
i
gr
at
i
on
of
ex
i
s
t
i
n
g
dat
a
bas
e
i
s
S
Q
L
S
er
v
er
.
T
he
t
ool
us
e
d
i
s
s
ql
2m
ongodb
(
Mong
oD
B
S
Q
L
S
er
v
er
I
m
por
t
er
)
.
T
hi
s
t
ool
w
as
d
e
v
e
l
op
ed
i
n C
#.
Sq
l
2m
ongo "
S
er
v
er
=
10
.
10.
5
0.
16
;
D
at
abas
e
=
d
at
a
_t
es
t
;
U
s
er
I
d
=
us
er
;
P
as
s
w
or
d
=
pas
s
w
or
d"
"
m
ongod
b:
/
/
l
oc
a
l
hos
t
:
270
01
"
T
he
P
r
ogr
es
s
of
m
i
gr
at
i
o
n
i
dent
i
f
i
ed as
f
o
llo
w
:
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KO
M
NI
K
A
I
S
S
N
:
1
693
-
6
930
B
i
g
D
at
a A
na
l
y
s
i
s
w
i
t
h
M
o
n
goD
B
f
or
D
ec
i
s
i
o
n S
upp
or
t
S
y
s
t
em
(
S
ul
i
s
t
y
o H
er
i
pr
ac
o
y
o
)
1087
F
i
gur
e 4.
T
he
M
i
gr
at
i
o
n P
r
o
gr
es
s
f
r
o
m
S
Q
L S
er
v
er
t
o
Mong
oD
B
F
i
gur
e 3.
T
es
t
i
ng C
on
nec
t
i
on
M
ong
oD
B
5.
C
hec
k
t
he r
es
ul
t
s
of
m
i
gr
at
i
on o
n Mo
ng
oD
B
6.
A
pp
l
i
c
at
i
o
n P
r
ot
ot
y
p
e
B
as
ed
on
t
he
r
es
ul
t
of
m
i
gr
at
i
o
n,
her
e
i
s
a
v
ar
i
et
y
of
t
er
m
s
and t
er
m
i
nol
o
g
y
of
S
Q
L
Mong
oD
B
.
T
her
ef
or
e,
t
he
us
e of
t
he t
er
m
pr
ev
i
ous
l
y
us
ed i
n t
he D
B
MS
c
a
n f
ol
l
o
w
t
er
m
s
i
n
Mong
oD
B
.
T
abl
e 1
.
T
er
m
i
n S
Q
L a
nd
Mong
oD
B
C
onc
ept
/
S
Q
L
T
er
m
C
onc
ept
/
M
ongoD
B
T
er
m
D
at
abas
e
D
at
abas
e
T
abl
e
T
abl
e
Ro
w
D
oc
u
m
ent
or
B
S
O
N
D
o
c
u
m
ent
C
ol
um
n
F
ie
ld
I
ndex
I
ndex
T
abl
e J
oi
n
E
m
bedded D
oc
u
m
ent
and Li
n
k
i
ng
P
r
i
m
ar
y
K
ey
D
et
er
m
i
ni
ng
U
ni
que
eac
h
c
o
l
u
m
n
o
r
c
o
m
bi
na
t
i
on
o
f
c
ol
u
m
n
s
a
s
t
he P
r
i
m
ar
y
K
ey
I
n
M
ongoD
B
,
P
r
i
m
ar
y
k
ey
s
ar
e
au
t
o
m
at
i
c
al
l
y
s
et
t
o
t
he i
d
f
i
e
l
d.
A
ggr
egat
i
on (
eg G
r
oup by
)
A
ggr
egat
i
on pi
pel
i
ne
T
he f
ol
l
o
w
i
ng s
ho
w
s
s
om
e ex
ec
ut
a
bl
es
Mon
goD
B
dat
a
bas
e a
nd t
he c
or
r
es
p
ond
i
ng
ex
ec
ut
ab
l
e.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SSN
:
1
6
9
3
-
6
930
T
E
L
KO
M
NI
K
A
V
o
l.
1
4
,
N
o
.
3
,
S
ept
em
ber
201
6
:
10
83
–
1
089
1088
T
abl
e 2
.
T
er
m
o
f
E
x
ec
ut
ab
l
e dat
abas
e
M
ong
oD
B
M
yS
Q
L
O
r
acl
e
In
f
o
r
mix
D
at
abas
e S
er
v
er
m
ongod
m
ys
q
l
d
or
ac
l
e
I
DS
D
at
abas
e C
l
i
en
t
m
ongo
m
ys
q
l
s
ql
pl
us
DB
-
A
c
c
e
s
s
T
he A
dv
ant
age
of
Mon
goD
B
:
1.
A
bl
e t
o
ens
ur
e
ac
c
es
s
s
peed b
y
U
s
er
.
Mong
oD
B
i
s
t
he
er
a
of
C
l
o
ud
d
at
a
bas
e
des
i
g
ned
f
or
bi
g
dat
a
s
t
or
a
ge
and
qu
er
y
,
a
nd
app
l
i
c
at
i
ons
of
S
oc
i
a
l
N
et
w
or
k
(
F
ac
ebook
,
et
c
)
.
Mo
ngoD
B
us
es
t
h
e d
oc
um
ent
as
t
he
bas
i
c
s
t
or
age
un
i
t
.
A
d
oc
um
ent
i
s
a s
i
m
pl
e J
S
O
N
as
a
n o
bj
ec
t
.
Q
uer
y
t
h
e
v
a
l
ue
t
o
be f
as
t
er
t
h
an
r
el
at
i
on
al
quer
i
es
,
i
f
us
i
ng
R
D
B
M
S
t
hen
ha
v
e
t
o p
er
f
or
m
a pr
oc
es
s
denor
m
al
i
z
at
i
on.
2.
D
oc
um
ent
Mode
l
Mong
o D
B
t
her
e
i
s
no
c
o
nc
ept
of
a
t
ab
l
e,
r
o
w
,
S
Q
L,
s
c
hem
a,
al
s
o s
om
e qu
er
i
es
.
T
her
ef
or
e,
bas
ed on
t
he
e
x
per
i
m
ent
t
he d
at
ab
as
e m
i
gr
at
i
o
n f
r
om
S
Q
L S
er
v
er
c
o
nv
er
t
ed i
nt
o
doc
um
ent
.
3.
F
l
ex
i
b
l
e S
c
hem
a
A
c
t
ua
l
l
y
,
no
s
c
hem
a
i
n
M
o
ngoD
B
,
a
d
oc
um
ent
c
an
h
av
e
a
f
i
e
l
d,
t
h
e
f
i
e
l
d
c
a
n
b
e
add
ed
t
o
t
he
ex
i
s
t
i
n
g
doc
um
ent
at
an
y
t
i
m
e
d
y
nam
i
c
al
l
y
.
N
o
A
LT
E
R
T
A
B
L
E
,
no
r
e
bu
i
l
d
I
ndex
i
ng.
T
he
doc
um
ent
ex
ac
t
l
y
as
J
S
O
N
,
P
H
P
ar
r
a
y
,
or
d
i
c
t
i
onar
y
P
y
t
hon
.
V
er
y
na
t
ur
a
l
t
o
c
o
m
m
uni
c
at
e
w
i
t
h
Mong
oD
B
w
i
t
h d
y
n
am
i
c
l
an
guag
es
s
uc
h as
J
a
v
a
S
c
r
i
pt
,
P
H
P
,
or
P
y
t
ho
n.
4.
D
oes
no
t
s
upp
or
t
t
r
ans
ac
t
i
o
ns
5.
D
oes
no
t
s
upp
or
t
J
O
I
N
Mong
oD
B
does
no
t
s
uppor
t
f
eat
ur
es
s
uc
h
as
r
el
at
i
o
n
al
dat
abas
es
s
uc
h
T
r
ans
ac
t
i
on
or
J
O
I
N
,
but
h
av
e t
he
ab
i
l
i
t
y
t
o m
or
e eas
i
l
y
and f
l
ex
i
b
l
e
s
c
he
m
a t
hat
i
s
e
as
y
t
o m
ani
pul
at
e
w
i
t
h
J
S
O
N
as
t
he
dat
a f
or
m
at
.
B
as
ed
on
t
h
e ex
p
er
i
m
ent
a
l
r
es
u
l
t
s
an
d t
he
be
nef
i
t
s
Mo
ng
oD
B
d
es
c
r
i
bed
ab
ov
e,
t
h
e
r
es
ul
t
i
s
t
hat
s
t
r
uc
t
ur
ed d
at
a c
an be
de
v
el
ope
d i
nt
o u
ns
t
r
uc
t
ur
ed
dat
a
and
t
h
e e
as
e
of
ac
c
e
s
s
t
hat
c
an b
e do
ne t
hr
o
ugh
an ap
pl
i
c
at
i
on.
M
ong
oD
B
i
s
t
he dat
a
i
n t
h
e f
or
m
o
f
doc
um
ent
s
and
an
y
dat
a
n
o
l
o
ng
er
l
i
nk
ed
w
i
t
h
o
t
her
d
at
a
t
hr
o
ug
h
a
r
el
at
i
o
ns
hi
p
/
F
or
e
i
gn
K
e
y
(
o
n
E
nt
i
t
y
R
el
a
t
i
o
ns
hi
p D
i
agr
am
)
,
but
r
at
her
t
hr
o
ug
h a
nod
e.
4
.
C
o
n
c
l
u
s
i
o
n
T
he R
D
B
M
S
r
el
a
t
i
o
na
l
d
a
t
abas
e
(
e.
g.
S
Q
L
S
er
v
er
)
c
an
be c
o
nv
er
t
ed
i
nt
o
o
bj
ec
t
s
dat
a
bas
e
m
anagem
ent
s
y
s
t
em
(
O
D
B
MS
)
.
T
he
dat
a
t
hat
ar
e
us
ed
i
n
O
D
B
M
S
w
i
l
l
be
a
n
obj
ec
t
/
doc
um
ent
an
d
it
c
an
be
f
l
ex
i
b
l
y
ac
c
es
s
ed.
W
i
t
h
t
he
Mo
ngo
D
B
dat
a
bas
e
,
t
he
c
or
r
es
pondi
ng
da
t
a
i
s
hi
s
t
o
r
i
c
al
dat
a
i
n
l
ar
ge
qu
ant
i
t
i
e
s
,
do
es
not
c
ont
a
i
n
t
r
ans
ac
t
i
on
d
at
a.
T
he
Mong
oD
B
da
t
ab
as
e s
ui
t
a
bl
e f
or
us
i
ng
as
a
dat
a s
t
or
age
and
c
an b
e a
s
ana
l
y
s
i
s
s
our
c
e
.
E
s
pec
i
a
l
l
y
,
d
at
a i
n
t
he
Mo
n
goD
B
dat
abas
e
is
s
ui
t
ab
l
e
f
or
us
in
g
as
a d
ec
i
s
i
o
n s
up
por
t
da
t
a s
uc
h
as
dat
a
m
i
ni
ng
or
t
ex
t
m
i
ni
n
g.
R
ef
er
en
ces
[1
]
Ba
ra
r
K,
Su
n
i
t
a
B
,
R
ek
s
hev
eenay
B
.
F
u
t
ur
e o
f
C
l
ou
d C
om
pu
t
i
ng
.
I
nt
er
nat
i
on
al
J
our
n
al
of
L
at
e
s
t
T
r
end
s
i
n
E
ngi
neer
i
ng and T
e
c
hno
l
og
y
(I
J
L
T
ET
)
.
201
3
;
2
(
3
)
.
[2
]
Bro
w
n
B
,
M
i
c
hael
,
M
any
i
k
a
J
.
A
r
e
y
ou
r
ea
dy
f
or
t
h
e er
a
of
'
B
i
g dat
a'
?
M
c
K
i
ns
ey
G
l
oba
l
I
n
s
t
i
t
ut
e
.
20
1
1.
[3
]
B
ughi
n J
,
C
hui
M
,
M
any
i
k
a J
.
C
l
oud
s
,
bi
g dat
a,
and S
m
ar
t
as
s
et
s
:
T
en T
ec
h
-
enab
l
ed bu
s
i
nes
s
t
r
e
nds
to
w
a
tc
h
.
M
c
K
i
ns
ey
Q
uar
t
er
l
y
.
2010
.
[4
]
H
eal
ey
M
.
6 Li
es
A
bou
t
B
i
g D
a
t
a.
I
nf
or
m
a
t
i
o
n
W
e
e
k
.
2012
.
[5
]
M
any
i
k
a J
,
C
hui
M
,
B
r
ow
n B
,
B
ughi
n
J
,
D
obb
s
R
,
R
ox
bur
gh C
,
et
al
.
B
i
g D
at
a:
T
he N
ex
t
f
r
ont
i
er
f
o
r
i
nnov
at
i
on,
c
o
m
pet
i
t
i
on,
a
nd pr
oduc
t
i
v
i
t
y
.
M
c
K
i
ns
ey
G
l
ob
al
I
n
s
ti
tu
te
.
2
011
.
[6
]
R
u
sso
m P
.
B
i
g D
at
a an
al
i
t
i
c
s
.
T
D
W
I
R
es
e
ar
c
h,
T
D
W
I
B
es
t
P
r
at
i
c
es
R
e
por
t
,
F
our
t
h Q
uar
t
er
.
2011
.
[7
]
H
ar
r
i
s
o
n G
uy
.
1
0 t
hi
ng
s
y
ou
s
h
oul
d k
now
about
N
oS
Q
L
dat
a
bas
e
s
ht
t
p:
/
/
w
w
w
.
t
ec
hr
epub
l
i
c
.
c
om
/
bl
og/
10
-
t
hi
n
gs
/
10
-
t
hi
n
gs
-
yo
u
-
s
h
oul
d
-
k
now
-
about
-
no
s
ql
-
da
t
a
bas
e
s
/
#
.
2010
.
[8
]
T
ec
ht
er
m
s
.
c
om
.
w
w
w
.
t
ec
ht
er
m
s
.
c
om
/
def
i
ni
t
i
on
/
bi
g
_dat
a.
R
et
r
i
ev
ed
N
ov
em
ber
13
,
2013,
f
r
o
m
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T
he
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10]
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et
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ongod
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2014.
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H
er
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[
13]
B
agher
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adhy
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om
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J
-
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.
2013
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.
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