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r
d
er
to
im
p
r
o
v
e
d
ata
i
n
g
es
tio
n
,
q
u
er
y
p
er
f
o
r
m
a
n
ce
,
an
d
ca
p
ac
it
y
,
ab
ilit
y
to
an
al
y
ze
tr
a
f
f
ic
allo
ws
n
et
w
o
r
k
o
p
er
ato
r
s
to
id
en
t
if
y
n
et
w
o
r
k
er
r
o
r
s
,
d
etec
t
an
o
m
alo
u
s
tr
a
f
f
ic.
Net
w
o
r
k
co
n
n
ec
tio
n
lo
g
s
h
av
e
lo
n
g
b
ee
n
r
ec
o
g
n
ized
as
in
te
g
r
al
to
p
r
o
p
e
r
n
et
w
o
r
k
s
ec
u
r
it
y
,
m
ai
n
te
n
a
n
ce
,
an
d
p
er
f
o
r
m
an
ce
m
a
n
ag
e
m
e
n
t
.
T
h
e
p
u
r
p
o
s
e
o
f
p
r
esen
tin
g
t
h
e
f
o
llo
w
i
n
g
in
s
u
f
f
icie
n
t
s
to
r
ag
e
m
et
h
o
d
s
is
n
o
t
to
m
ak
e
an
ar
g
u
m
e
n
t
t
h
at
d
is
tr
ib
u
ti
n
g
d
ata
o
r
W
ODs
ar
e
th
e
o
n
l
y
t
w
o
s
o
l
u
tio
n
s
to
th
is
c
h
alle
n
g
e,
b
u
t
r
ath
er
to
f
u
r
th
er
ill
u
s
tr
ate
it
s
co
m
p
le
x
it
y
.
T
h
is
w
o
r
k
p
r
ese
n
ts
it
s
d
ata
a
m
o
n
g
m
a
n
y
n
o
d
es
in
th
e
clu
s
ter
.
T
h
is
a
llo
w
s
i
n
s
er
tio
n
s
a
n
d
q
u
er
ies
to
d
if
f
er
e
n
t s
h
ar
d
s
to
r
u
n
in
p
a
r
allel
an
d
ad
d
itio
n
all
y
allo
w
s
o
p
er
ato
r
s
p
r
o
v
id
e
th
e
s
y
s
te
m
w
it
h
m
o
r
e
r
eso
u
r
ce
s
w
it
h
o
u
t scali
n
g
u
p
o
n
e
d
ev
ice.
Scale
-
u
p
q
u
ick
l
y
b
ec
o
m
e
s
p
r
o
h
ib
itiv
el
y
e
x
p
en
s
iv
e,
b
u
t
s
ca
l
e
-
o
u
t i
s
m
u
c
h
m
o
r
e
co
s
t
ef
f
ec
ti
v
e.
Data
in
te
g
r
it
y
i
s
also
en
s
u
r
ed
b
y
d
u
p
licati
n
g
th
e
d
ata
ac
r
o
s
s
n
o
d
es.
I
f
th
e
am
o
u
n
t
o
f
d
ata
s
to
r
ed
o
n
ea
ch
n
o
d
e
is
n
o
t
to
o
lar
g
e,
th
e
d
r
aw
b
ac
k
n
o
ted
ab
o
v
e
ca
n
n
o
w
b
ec
o
m
e
an
ad
v
a
n
ta
g
e
to
th
e
s
y
s
te
m
a
s
it
in
cr
ea
s
es t
h
e
li
k
eli
h
o
o
d
th
at
d
ata
is
f
o
u
n
d
o
n
m
an
y
s
h
ar
d
s
in
s
tead
o
f
j
u
s
t o
n
e.
-
C
o
n
tr
ib
u
t
io
n
s
T
h
e
m
a
in
co
n
tr
ib
u
tio
n
s
o
f
th
is
w
o
r
k
in
v
o
lv
e
i
m
p
r
o
v
i
n
g
t
h
e
r
esu
lt
s
o
f
p
r
ev
io
u
s
s
y
s
te
m
s
in
t
h
e
d
o
m
ai
n
o
f
n
e
t
w
o
r
k
i
n
g
an
d
d
ata
b
ase,
wh
ile
w
e
co
m
p
ar
e
Di
v
e
n
ti a
n
d
E
last
icsear
c
h
h
er
e,
it i
s
w
o
r
th
n
o
tin
g
t
h
at
th
e
y
ar
e
n
o
t
m
u
tu
a
ll
y
e
x
cl
u
s
i
v
e,
an
d
b
o
th
p
r
o
v
id
e
u
n
iq
u
e
b
en
e
f
its
.
A
s
an
e
x
a
m
p
le,
u
s
e
ca
s
e
o
f
th
e
s
e
d
atab
ases
,
w
e
cr
ea
ted
a
s
i
m
p
le
an
a
l
y
t
ic
to
r
u
n
th
r
o
u
g
h
t
h
e
n
et
w
o
r
k
h
is
to
r
y
o
f
an
I
P
ad
d
r
ess
.
T
h
is
s
cr
ip
t
p
r
o
d
u
ce
s
h
is
to
g
r
a
m
s
o
f
th
e
f
o
llo
w
i
n
g
m
etr
ics:
n
u
m
b
er
o
f
p
ac
k
ets
in
an
d
o
u
t,
n
u
m
b
er
o
f
b
y
tes
i
n
an
d
o
u
t,
s
o
u
r
ce
an
d
d
esti
n
atio
n
p
o
r
t
n
u
m
b
er
s
,
an
d
n
u
m
b
er
o
f
co
n
n
ec
tio
n
s
w
it
h
e
ac
h
n
eig
h
b
o
r
.
W
e
u
tili
ze
t
h
ese
m
etr
ic
s
to
clas
s
i
f
y
th
e
I
P
ad
d
r
ess
es a
s
ex
h
ib
itin
g
o
n
e
o
r
m
o
r
e
n
et
w
o
r
k
b
eh
a
v
io
r
s
.
W
e
d
is
cu
s
s
m
et
h
o
d
s
f
o
r
s
to
r
in
g
n
et
w
o
r
k
f
lo
w
s
,
th
en
co
m
p
ar
e
an
d
co
n
tr
ast
E
l
asti
csear
c
h
an
d
Div
e
n
ti
in
s
ec
tio
n
1
.
6
.
T
h
en
,
in
s
ec
tio
n
2
,
c
u
r
r
en
t
an
d
p
o
ten
tial
n
et
w
o
r
k
f
lo
w
an
a
l
y
s
is
o
f
lar
g
e
f
lo
w
d
atas
ets
ar
e
d
is
c
u
s
s
ed
.
Fin
all
y
,
i
n
s
ec
tio
n
3
,
w
e
p
r
e
s
en
t
t
h
e
r
es
u
lt
s
o
f
an
al
y
tic
w
e
d
ev
e
lo
p
ed
,
th
e
p
e
r
f
o
r
m
an
ce
o
f
t
h
e
an
al
y
tic
r
u
n
n
in
g
i
n
r
ea
l
-
ti
m
e
at
SC
1
9
(
s
u
p
er
co
m
p
u
ti
n
g
2
0
1
9
)
,
an
d
th
e
r
es
u
lt
s
w
e
g
lea
n
ed
f
r
o
m
t
h
is
in
f
o
r
m
atio
n
.
E
last
icsear
ch
[
4
]
an
d
Div
e
n
ti
[
5
]
ar
e
t
w
o
ex
a
m
p
le
s
o
f
s
y
s
te
m
s
w
h
ic
h
p
r
o
p
er
ly
ad
d
r
ess
th
ese
co
n
ce
r
n
s
,
y
et
th
e
m
ea
n
s
b
y
w
h
ich
t
h
e
y
d
o
ar
e
v
er
y
d
if
f
er
en
t.
E
last
icsear
c
h
is
a
“
r
ea
l
-
ti
m
e
d
is
tr
ib
u
ted
s
ea
r
ch
an
d
a
n
al
y
t
ics
e
n
g
in
e.
”
w
h
ich
allo
w
s
f
o
r
b
o
th
r
ap
id
in
g
esti
o
n
an
d
q
u
er
y
r
esp
o
n
s
e
b
y
s
h
ar
d
in
g
d
ata
ac
r
o
s
s
m
an
y
n
o
d
es
in
a
clu
s
ter
.
Di
v
en
ti
o
n
th
e
o
th
er
h
a
n
d
lev
er
a
g
es
t
h
e
w
r
ite
o
p
ti
m
izatio
n
s
o
f
a
B
t
o
th
e
ep
s
ilo
n
tr
ee
(
B
ε
-
tr
ee
)
to
k
ee
p
u
p
w
ith
d
ata
in
g
esti
o
n
n
ee
d
s
w
h
ile
u
t
ilizi
n
g
t
h
e
u
n
d
er
l
y
i
n
g
B
-
tr
ee
s
tr
u
ct
u
r
e
to
en
s
u
r
e
ti
m
el
y
q
u
er
ies.
T
h
i
s
allo
w
s
Di
v
en
t
i to
s
to
r
e
a
h
ig
h
a
m
o
u
n
t o
f
d
ata
o
n
a
s
i
n
g
le
n
o
d
e.
-
Net
w
o
r
k
f
lo
w
d
atab
ase
A
n
et
w
o
r
k
f
lo
w
i
s
a
u
n
id
ir
ec
ti
o
n
al
s
tr
ea
m
o
f
p
ac
k
ets
w
it
h
c
o
m
m
o
n
s
o
u
r
ce
an
d
d
esti
n
atio
n
.
Netf
lo
w
an
d
I
P
FIX
,
tw
o
co
m
m
o
n
f
lo
w
ex
p
o
r
t
p
r
o
to
co
ls
,
ag
g
r
eg
ate
p
ac
k
ets
f
r
o
m
t
h
is
s
tr
ea
m
w
it
h
i
n
a
g
iv
e
n
w
in
d
o
w
o
f
ti
m
e
i
n
to
a
s
i
n
g
le
f
lo
w
.
B
r
o
-
co
n
n
lo
g
s
[
6
]
ar
e
n
o
t
tr
u
l
y
f
l
o
w
s
,
a
s
ea
c
h
lo
g
r
e
f
er
s
to
a
s
in
g
le
b
id
ir
ec
tio
n
al
co
n
n
ec
tio
n
w
h
ich
m
a
y
b
e
c
o
m
p
o
s
ed
o
f
m
u
ltip
le
p
ac
k
ets
to
o
p
en
th
e
co
n
n
ec
tio
n
,
s
e
n
d
d
ata,
an
d
clo
s
e
th
e
co
n
n
ec
tio
n
.
All
t
h
e
p
ac
k
et
s
in
t
h
i
s
co
n
n
ec
tio
n
ar
e
a
g
g
r
e
g
ated
,
an
d
f
o
r
t
h
e
p
u
r
p
o
s
es
o
f
th
is
r
e
s
ea
r
ch
p
ap
er
w
e
w
i
ll
r
e
f
er
to
b
r
o
-
co
n
n
lo
g
s
as
f
lo
w
s
f
o
r
t
h
e
s
a
k
e
o
f
s
i
m
p
licit
y
.
P
ac
k
et
a
g
g
r
e
g
atio
n
in
h
er
e
n
tl
y
r
es
u
lt
s
i
n
th
e
lo
s
s
o
f
p
ac
k
et
s
p
ec
i
f
ic
in
f
o
r
m
at
io
n
[
7
]
.
T
h
i
s
w
as
an
ea
r
l
y
s
ac
r
i
f
ice
m
ad
e
to
ad
d
r
ess
th
e
ex
tr
e
m
el
y
h
i
g
h
v
o
lu
m
e
o
f
ev
e
n
ts
cr
ea
ted
b
y
lo
g
g
i
n
g
ev
er
y
p
ac
k
et.
Ho
w
ev
er
,
in
s
p
ite
o
f
th
is
r
ed
u
ctio
n
i
n
v
o
l
u
m
e,
n
e
t
w
o
r
k
f
lo
w
d
ata
s
till
s
u
f
f
er
s
f
r
o
m
B
ig
Data
co
m
p
lex
i
t
y
.
“
B
ig
Data
is
d
ata
w
h
o
s
e
co
m
p
lex
i
t
y
h
i
n
d
er
s
it
f
r
o
m
b
ein
g
m
a
n
a
g
ed
,
q
u
er
ied
an
d
a
n
al
y
ze
d
t
h
r
o
u
g
h
tr
ad
itio
n
a
l
d
ata
s
to
r
ag
e
ar
c
h
itect
u
r
es,
al
g
o
r
ith
m
s
,
an
d
q
u
er
y
m
ec
h
a
n
is
m
s
.
”
T
h
is
co
m
p
lex
i
t
y
is
d
e
f
in
ed
b
y
th
e
d
ata
’
s
v
o
lu
m
e
-
th
e
q
u
an
t
it
y
o
f
d
ata
to
b
e
s
to
r
ed
;
v
ar
iet
y
-
t
h
e
s
y
s
te
m
m
u
s
t
s
i
m
u
lta
n
eo
u
s
l
y
h
o
ld
u
n
-
s
tr
u
ct
u
r
ed
,
s
e
m
i
-
s
tr
u
ctu
r
ed
,
an
d
s
tr
u
ctu
r
ed
d
ata;
an
d
v
elo
cit
y
-
th
e
p
ac
e
at
w
h
ic
h
d
ata
is
g
e
n
er
ated
f
o
r
th
e
e
n
ter
p
r
is
e
n
et
w
o
r
k
f
o
r
NI
DS
in
Fi
g
u
r
e
1
.
[
8
]
T
h
an
k
f
u
ll
y
,
v
ar
iet
y
i
s
n
o
t
o
f
co
n
ce
r
n
as
n
et
w
o
r
k
f
lo
w
s
all
f
o
llo
w
a
s
i
m
ilar
s
tr
u
ct
u
r
e.
Un
f
o
r
tu
n
atel
y
,
an
y
s
y
s
te
m
ta
s
k
ed
w
it
h
s
to
r
in
g
t
h
is
d
ata
w
il
l
s
till
h
av
e
to
co
n
ten
d
w
it
h
h
i
g
h
v
o
l
u
m
e
a
n
d
v
elo
cit
y
.
T
h
is
co
m
p
l
ex
it
y
h
i
n
d
er
s
u
n
s
o
p
h
is
t
icate
d
ef
f
o
r
t
s
to
m
a
n
ag
e,
q
u
er
y
,
an
d
an
al
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(
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[
8
,
1
3
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to
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[
1
4
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.
Ho
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al
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Scale
-
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p
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t is m
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ch
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e
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ef
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ec
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e.
Data
i
n
te
g
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it
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i
s
also
en
s
u
r
ed
b
y
d
u
p
licati
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ata
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es.
I
f
th
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a
m
o
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n
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o
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ata
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to
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e
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e,
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e
d
r
a
w
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ac
k
n
o
ted
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o
v
e
ca
n
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o
w
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o
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e
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n
ad
v
a
n
ta
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te
m
a
s
it
i
n
cr
ea
s
es
t
h
e
l
ik
eli
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o
o
d
th
at
d
ata
is
f
o
u
n
d
o
n
m
an
y
s
h
ar
d
s
in
s
te
ad
o
f
j
u
s
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e.
-
Div
e
n
t
i
W
r
ite
o
p
tim
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d
atastru
ct
u
r
e
s
ar
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d
esi
g
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ed
to
p
r
o
v
id
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e
f
f
ic
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t
w
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ite
p
er
f
o
r
m
a
n
ce
at
t
h
e
ex
p
en
s
e
o
f
a
li
m
ited
q
u
er
y
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er
f
o
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m
a
n
ce
p
en
alt
y
.
A
B
ε
-
tr
ee
is
a
B
-
tr
ee
w
i
th
an
in
s
er
tio
n
b
u
f
f
er
p
lace
d
at
ea
ch
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o
d
e.
Data
is
Evaluation Warning : The document was created with Spire.PDF for Python.
T
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2415
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Fi
g
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r
e
3
[
1
5
]
.
T
h
is
s
tr
u
ct
u
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e
h
as
a
f
e
w
k
e
y
b
en
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ay
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ize
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et
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ata.
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f
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=
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s
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h
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e
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th
e
m
a
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en
t
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y
w
it
h
r
esp
ec
t to
th
e
b
u
f
f
er
s
ize
[
1
6
]
.
Fig
u
r
e
2
.
C
o
n
s
tr
u
cti
n
g
a
n
in
v
e
r
ted
in
d
ex
f
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r
th
e
d
atab
ase,
th
e
in
d
ex
ca
n
b
e
i
m
ag
in
ed
a
m
a
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e
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n
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a
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ized
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all
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ick
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y
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d
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’
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e
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k
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.
W
h
en
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f
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,
d
o
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d
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n
clu
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f
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d
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s
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c
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as t
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r
ig
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atin
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I
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ad
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[
4
]
Fig
u
r
e
3
.
B
ε
-
tr
ee
: I
n
s
er
t
io
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o
f
r
ed
d
ata
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g
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u
s
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to
th
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ch
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ld
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d
ata
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in
s
er
ted
to
th
e
r
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t b
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f
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w
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w
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n
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illed
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f
l
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ten
t
s
to
its
ch
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r
en
t
h
at
m
ak
e
u
p
a
tr
ee
[
5
]
As
an
o
th
er
p
er
f
o
r
m
a
n
ce
b
en
ef
it,
w
r
ites
to
d
is
k
ar
e
am
o
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tiz
ed
b
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au
s
e
b
u
f
f
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s
h
ig
h
er
in
t
h
e
tr
ee
ar
e
h
eld
w
ith
in
ca
c
h
es
an
d
R
AM
.
T
h
is
m
ea
n
s
th
at
o
n
l
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t
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o
s
e
f
lu
s
h
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lo
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ee
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d
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k
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Os.
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h
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cr
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s
es
th
e
r
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o
f
in
g
e
s
tio
n
.
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h
e
tim
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alt
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to
q
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f
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eq
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o
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th
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s
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r
ch
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g
h
t
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b
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o
f
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ch
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o
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e
v
is
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ted
w
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ile
tr
av
er
s
in
g
th
e
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ee
.
T
h
is
m
ea
n
s
th
at
t
h
er
e
is
a
p
o
s
itiv
e
co
r
r
elatio
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b
etw
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n
b
u
f
f
er
s
ize
an
d
q
u
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y
late
n
c
y
.
Fo
r
m
o
r
e
d
etails
s
ee
R
aize
s
et
al.
[
1
7
]
.
Div
en
t
i
o
r
d
er
s
d
ata
f
ir
s
t
u
p
o
n
t
h
e
s
o
u
r
ce
ip
ad
d
r
ess
es
a
n
d
th
e
n
th
e
ti
m
esta
m
p
.
T
h
e
b
en
e
f
it
o
f
t
h
i
s
i
s
t
h
at
q
u
er
ie
s
to
I
P
ad
d
r
ess
es
an
d
s
u
b
n
et
s
ar
e
q
u
ick
.
T
h
e
lo
g
s
w
h
ich
m
atc
h
th
e
q
u
er
y
w
ill
b
e
co
n
tig
u
o
u
s
w
ith
in
th
e
d
atab
a
s
e
an
d
q
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ick
to
id
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t
if
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as
a
r
es
u
lt
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t
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e
B
-
tr
ee
s
tr
u
ctu
r
e.
T
h
e
s
y
s
te
m
ca
n
tak
e
ad
v
an
ta
g
e
o
f
s
p
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lo
ca
lit
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w
h
e
n
p
er
f
o
r
m
in
g
th
e
s
e
q
u
er
ies
in
ad
d
itio
n
to
q
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ick
l
y
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en
ti
f
y
in
g
th
e
m
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n
g
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s
.
As
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is
k
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O
p
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d
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r
in
g
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ies
s
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ld
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m
i
n
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m
ize
d
.
T
h
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d
r
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b
ac
k
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s
th
at
t
h
e
s
y
s
te
m
ca
n
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t
ef
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q
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o
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ield
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w
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,
th
e
w
o
r
k
lo
ad
w
e
a
r
e
co
n
ce
r
n
ed
w
ith
i
s
p
r
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m
ar
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y
i
n
v
esti
g
atio
n
s
i
n
to
in
d
iv
id
u
al
ip
ad
d
r
ess
es
o
r
s
u
b
n
e
ts
.
An
o
th
er
d
r
a
w
b
ac
k
i
s
th
a
t
i
n
o
r
d
er
to
r
etr
iev
e
lo
g
s
w
h
e
n
t
h
e
q
u
er
ied
I
P
ad
d
r
ess
m
atc
h
es
e
ith
er
th
e
s
o
u
r
ce
o
r
r
esp
o
n
d
in
g
I
P
ad
d
r
ess
,
t
w
o
lo
g
s
o
f
ea
ch
ev
e
n
t
m
u
s
t
b
e
i
n
s
er
ted
.
On
e
w
it
h
n
o
r
m
al
I
P
o
r
d
er
in
g
a
n
d
th
e
o
th
er
r
ev
er
s
ed
.
Di
v
en
t
i
p
r
o
v
id
es
ef
f
icie
n
t
u
s
e
o
f
r
eso
u
r
ce
s
to
allo
w
a
lar
g
e
a
m
o
u
n
t
o
f
d
ata
to
b
e
s
to
r
e
d
o
n
a
s
in
g
le
n
o
d
e
w
h
i
le
m
ai
n
tai
n
i
n
g
h
i
g
h
p
er
f
o
r
m
an
ce
.
M
u
ltip
l
e
Div
en
ti
n
o
d
es
r
esp
o
n
s
ib
le
f
o
r
d
if
f
er
en
t
n
et
w
o
r
k
tap
s
o
r
f
o
r
d
ata
th
at
is
s
p
lit
b
etw
ee
n
th
e
m
b
y
an
o
t
h
er
p
r
o
ce
s
s
m
a
y
b
e
d
ep
lo
y
ed
if
e
v
en
m
o
r
e
ca
p
ac
it
y
is
r
eq
u
ir
ed
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
1
6
9
3
-
6930
T
E
L
KOM
NI
K
A
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elec
o
m
m
u
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C
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m
p
u
t E
l
C
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n
tr
o
l
,
Vo
l.
1
8
,
No
.
5
,
Octo
b
e
r
2
0
2
0
:
2
4
1
2
-
2420
2416
2.
M
E
T
H
O
DO
L
O
G
Y
Net
w
o
r
k
f
lo
w
s
d
o
n
o
t
co
n
t
ain
p
ac
k
et
p
a
y
lo
ad
s
i
n
f
o
r
m
a
tio
n
,
an
d
d
o
n
o
t
p
r
o
v
id
e
p
a
ck
et
lev
e
l
g
r
an
u
lar
it
y
f
o
r
f
ield
s
s
u
ch
a
s
th
e
n
u
m
b
er
o
f
b
y
tes
p
er
p
ac
k
et
o
r
T
C
P
f
lag
s
.
I
n
s
tead
,
f
lo
w
s
co
n
tai
n
a
g
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r
e
g
at
e
to
tals
o
f
t
h
e
n
u
m
b
er
o
f
p
ac
k
e
t
s
,
b
y
tes,
f
lag
s
u
s
ed
in
a
n
y
p
ac
k
et,
an
d
m
o
r
e.
De
s
p
ite
t
h
is
lo
s
s
o
f
s
p
ec
i
f
ics,
f
lo
w
s
s
till
co
n
tai
n
s
u
f
f
icien
t
i
n
f
o
r
m
atio
n
to
id
en
ti
f
y
n
et
w
o
r
k
i
n
tr
u
s
io
n
s
[
1
8
]
.
T
h
is
s
ec
tio
n
d
es
cr
ib
es
ex
is
tin
g
a
n
d
p
o
ten
tial n
et
w
o
r
k
f
lo
w
a
n
al
y
s
i
s
to
b
e
im
p
r
o
v
ed
o
r
en
ab
led
b
y
n
et
w
o
r
k
f
lo
w
d
atab
ases
.
2
.
1
.
Curre
nt
m
et
ho
ds
a
n
d r
esea
rc
h
-
B
lack
li
s
t a
p
p
r
o
ac
h
A
t
th
e
m
o
s
t
b
as
ic
lev
e
l
N
f
s
e
n
an
d
Flo
w
s
ca
n
(
p
er
f
o
r
m
a
n
ce
d
is
cu
s
s
ed
in
s
u
b
s
ec
tio
n
1
.
4
)
p
r
o
v
id
e
f
o
r
an
al
y
s
is
o
f
f
lo
w
s
tatis
tics
an
d
tr
af
f
ic.
T
h
ey
cr
ea
te
s
tatis
t
i
ca
l
s
u
m
m
ar
ie
s
an
d
g
r
ap
h
ical
d
is
p
lay
s
o
f
d
ata
in
ad
d
itio
n
to
p
r
o
v
id
in
g
th
e
ab
ilit
y
to
f
il
ter
r
esu
lts
b
y
a
v
ar
iet
y
o
f
f
ield
s
.
N
f
s
e
n
ad
d
itio
n
all
y
p
r
o
v
id
es
th
e
ab
ilit
y
to
d
ef
in
e
aler
t
s
.
T
h
ese
aler
ts
ca
n
ac
t
u
p
o
n
a
f
ilter
ed
s
u
b
s
et
o
f
th
e
o
v
er
al
l
tr
af
f
ic,
tr
i
g
g
er
o
n
u
p
to
6
ch
ain
ed
co
n
d
itio
n
s
,
an
d
tak
e
a
s
et
o
f
ac
tio
n
s
[
1
9
]
.
S
im
i
lar
l
y
,
th
e
B
r
o
-
I
DS
p
r
o
v
id
es
th
e
ab
ilit
y
to
tr
ig
g
er
ac
tio
n
s
,
s
u
ch
as
b
lo
ck
in
g
tr
af
f
ic
an
d
cr
ea
tin
g
a
ler
ts
,
b
ased
u
p
o
n
th
e
co
n
ten
t
o
f
th
e
p
ac
k
ets4
co
llected
b
y
t
h
e
tap
[
2
0
]
.
T
h
is
is
o
f
ten
ac
co
m
p
l
is
h
ed
u
s
i
n
g
I
P
b
lack
li
s
ts
a
n
d
ch
ec
k
in
g
p
ac
k
et
co
n
ten
t
s
ag
ai
n
s
t c
o
m
m
o
n
m
al
w
ar
e
p
atter
n
s
.
-
Z
er
o
-
d
a
y
at
tack
s
Statis
t
ical
an
al
y
s
is
a
n
d
m
ac
h
i
n
e
lear
n
-
i
n
g
ar
e
e
m
p
lo
y
ed
f
o
r
d
etec
tin
g
a
n
d
clas
s
if
y
i
n
g
a
w
i
d
e
v
ar
iet
y
o
f
n
et
w
o
r
k
tr
a
f
f
ic
p
atter
n
s
.
Ho
w
e
v
er
,
th
e
y
co
m
m
o
n
l
y
s
h
ar
e
th
e
g
o
als o
f
r
ed
u
ci
n
g
th
e
f
alse
p
o
s
itiv
es
g
en
er
ated
b
y
s
y
s
te
m
s
lik
e
t
h
o
s
e
d
is
c
u
s
s
ed
ab
o
v
e,
an
d
d
etec
tin
g
ze
r
o
-
d
ay
attac
k
s
.
T
h
er
e
ar
e
m
a
n
y
e
x
a
m
p
le
s
o
f
u
s
in
g
n
et
w
o
r
k
f
lo
w
s
to
d
etec
t
an
d
cla
s
s
i
f
y
ac
tio
n
s
tak
e
n
b
y
n
et
w
o
r
k
p
ar
ticip
an
ts
.
Mo
u
s
ta
f
a
et
al.
p
r
esen
t
an
e
n
s
e
m
b
le
-
b
ased
tech
n
iq
u
e
f
o
r
d
etec
tin
g
e
x
p
lo
its
o
f
I
o
T
s
y
s
te
m
s
,
p
ar
ticu
l
ar
l
y
b
o
tn
ets,
u
s
in
g
s
tatis
t
ical
s
u
m
m
ar
ie
s
p
r
o
v
id
ed
b
y
th
e
B
r
o
-
ID
S
[
2
]
.
Ma
lC
las
s
if
ier
,
a
to
o
l
d
ev
elo
p
ed
b
y
r
e
s
ea
r
ch
er
s
at
Ox
f
o
r
d
[
2
1
]
,
u
s
es
th
e
n
et
w
o
r
k
f
lo
w
b
eh
av
io
r
o
f
m
a
l
w
ar
e
to
cla
s
s
i
f
y
it
in
to
v
ar
io
u
s
m
al
w
ar
e
f
a
m
ilie
s
w
i
th
o
u
t
r
eq
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ir
i
n
g
s
a
n
d
b
o
x
ex
ec
u
tio
n
[
2
2
]
.
Ma
lC
als
s
i
f
ier
ad
d
itio
n
all
y
h
a
s
th
e
ab
il
it
y
to
d
eter
m
i
n
e
i
f
th
e
m
al
w
ar
e
d
o
es
n
o
t
f
it
p
r
e
v
io
u
s
l
y
estab
lis
h
ed
m
al
w
ar
e
f
a
m
ilie
s
,
allo
w
in
g
s
e
cu
r
it
y
o
p
er
ativ
es
to
p
r
o
p
o
s
e
n
e
w
f
a
m
i
lies
.
Fi
n
all
y
,
R
o
d
r
ig
u
ez
et
al.
p
r
esen
t w
o
r
k
in
u
s
i
n
g
ti
m
e
s
er
ies
d
atab
ase
s
s
t
u
d
y
i
n
g
h
i
s
to
r
ical
p
atter
n
s
to
p
r
ed
ict
f
u
tu
r
e
b
eh
a
v
io
r
an
d
d
ete
ct
an
o
m
alies
.
T
h
ey
s
tate
th
a
t th
e
m
o
r
e
d
ata
th
at
is
u
s
ed
in
t
h
e
ti
m
e
s
er
ie
s
t
h
e
m
o
r
e
ac
cu
r
ate
t
h
e
p
r
ed
ictio
n
s
w
il
l b
e.
2
.
1
.
1
.
P
o
t
ent
ia
l
a
pp
lica
t
io
n
T
h
is
p
o
r
tio
n
o
f
t
h
e
r
esear
c
h
p
ap
er
atte
m
p
ts
to
ad
d
r
ess
t
h
e
p
o
s
s
ib
le
u
s
es
o
f
a
f
u
ll
y
o
p
er
atio
n
al
elastics
ea
r
c
h
o
r
d
iv
en
ti d
atab
a
s
e
-
L
ater
al
n
e
t
w
o
r
k
m
o
v
e
m
en
t
L
ater
al
n
et
w
o
r
k
m
o
v
e
m
e
n
t
is
a
p
r
o
ce
s
s
b
y
w
h
ic
h
an
attac
k
er
t
ak
es
ad
v
an
ta
g
e
o
f
ac
ce
s
s
to
o
n
e
m
ac
h
in
e
in
t
h
e
n
et
w
o
r
k
to
g
ai
n
ac
ce
s
s
t
o
an
o
th
er
m
ac
h
i
n
e.
T
h
is
is
d
o
n
e
f
o
r
th
e
p
u
r
p
o
s
e
o
f
r
ec
o
n
n
a
i
s
s
a
n
ce
to
f
in
d
f
u
tu
r
e
tar
g
ets,
to
r
ea
ch
a
n
o
b
j
ec
tiv
e,
o
r
g
ain
a
h
ig
h
er
le
v
el
o
f
ac
c
ess
to
t
h
e
n
et
w
o
r
k
.
Dete
cti
n
g
h
o
w
a
b
ad
ac
to
r
o
r
p
iece
o
f
m
al
w
ar
e
h
a
s
m
o
v
ed
w
ith
i
n
o
n
e’
s
n
et
w
o
r
k
is
ess
e
n
tial
f
o
r
a
p
r
o
p
e
r
r
esp
o
n
s
e
to
an
in
t
r
u
s
io
n
.
Ot
h
er
w
is
e,
m
al
w
ar
e
m
a
y
r
e
m
ai
n
w
it
h
i
n
th
e
n
et
w
o
r
k
,
co
n
ti
n
u
i
n
g
to
ca
u
s
e
d
am
a
g
e
af
ter
ac
tio
n
h
as
b
ee
n
tak
e
n
to
ad
d
r
ess
th
e
co
m
p
r
o
m
i
s
e.
A
d
d
itio
n
all
y
,
th
is
t
y
p
e
o
f
m
o
n
ito
r
in
g
m
a
y
allo
w
s
ec
u
r
it
y
o
p
er
ato
r
s
to
d
etec
t
an
o
m
alie
s
.
A
c
h
ai
n
o
f
s
s
h
lo
g
in
s
m
a
y
b
e
i
n
d
icativ
e
o
f
a
n
attac
k
.
I
n
o
r
d
er
to
id
en
ti
f
y
t
h
i
s
m
o
v
e
m
en
t,
it
i
s
n
ec
e
s
s
ar
y
to
h
o
ld
a
lar
g
e
a
m
o
u
n
t o
f
n
et
w
o
r
k
d
at
a
[
2
3
]
.
T
h
is
r
eq
u
ir
e
m
e
n
t n
ec
e
s
s
itates t
h
e
u
s
e
o
f
a
p
r
o
p
er
n
et
w
o
r
k
f
lo
w
d
atab
ase.
A
d
d
itio
n
al
l
y
,
s
en
s
o
r
s
th
a
t
m
o
n
ito
r
lo
ca
l
tr
af
f
ic
ar
e
r
eq
u
ir
ed
.
T
h
e
m
o
r
e
n
et
w
o
r
k
v
is
ib
il
it
y
t
h
e
s
y
s
te
m
i
s
g
iv
e
n
th
e
b
etter
,
i
f
t
h
e
s
y
s
te
m
h
a
s
n
o
v
ie
w
o
f
t
h
e
co
n
n
ec
tio
n
b
et
w
e
en
co
m
p
u
ter
A
a
n
d
B
th
a
n
i
t c
an
n
o
t d
etec
t
later
al
m
o
v
e
m
e
n
t
b
et
w
ee
n
th
e
m
.
T
r
ac
k
in
g
la
ter
al
m
o
v
e
m
en
t
w
as
co
n
s
id
er
ed
f
o
r
t
h
is
r
esear
c
h
p
ap
er
b
u
t
w
as
u
lti
m
atel
y
f
o
r
g
o
n
e
as a
r
es
u
lt o
f
li
m
ited
n
et
w
o
r
k
v
i
s
ib
ilit
y
.
-
Ma
ch
i
n
e
lear
n
i
n
g
,
h
u
m
a
n
i
n
te
r
ac
tio
n
an
d
v
er
i
f
icatio
n
T
h
e
cu
r
r
en
t
w
o
r
k
p
r
esen
ted
in
s
u
b
s
ec
tio
n
2
.
2
is
u
s
ef
u
l
in
d
etec
tin
g
an
o
m
alo
u
s
n
et
w
o
r
k
b
eh
av
io
r
an
d
ze
r
o
-
d
ay
attac
k
s
.
Ho
w
ev
er
,
f
o
r
co
m
p
licated
u
s
e
ca
s
e
s
,
a
h
u
m
an
s
ec
u
r
it
y
o
p
er
ato
r
w
ill
li
k
el
y
h
a
v
e
to
in
ter
ac
t
w
it
h
t
h
e
m
ac
h
in
e
lear
n
i
n
g
al
g
o
r
ith
m
to
v
er
if
y
t
h
at
t
h
e
co
r
r
ec
t
ac
tio
n
s
w
er
e
ta
k
en
o
r
t
o
in
ter
p
r
et
r
esu
lt
s
.
T
o
f
ac
ilit
ate
th
is
,
it
m
a
y
b
ec
o
m
e
n
ec
ess
ar
y
f
o
r
th
e
u
s
er
t
o
lo
o
k
in
to
th
e
h
is
to
r
y
o
f
ip
ad
d
r
ess
es
w
h
ic
h
th
e
al
g
o
r
ith
m
h
as
f
la
g
g
ed
.
T
h
ese
q
u
er
ies
b
y
th
e
h
u
m
an
o
p
er
ato
r
n
ee
d
to
co
m
p
lete
q
u
ic
k
l
y
an
d
h
a
v
e
ac
ce
s
s
to
a
lar
g
e
a
m
o
u
n
t o
f
d
ata
i
n
o
r
d
er
to
f
ac
ilit
ate
th
e
i
n
ter
ac
tio
n
an
d
s
av
e
v
al
u
ab
le
an
al
y
s
t ti
m
e.
2
.
1
.
2
.
I
P
f
lo
w
a
na
ly
s
is
a
t
s
up
er
co
m
p
uting
T
o
b
eg
in
to
e
v
alu
a
te
th
e
p
er
f
o
r
m
a
n
ce
a
n
d
u
s
e
o
f
a
n
et
w
o
r
k
f
lo
w
d
atab
ase,
w
e
d
e
v
elo
p
ed
an
an
a
l
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t
ic
to
p
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d
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f
lo
w
m
etr
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in
r
ea
l
-
ti
m
e
at
S
C
1
9
(
s
u
p
er
co
m
p
u
tin
g
2
0
1
9
)
.
Div
en
ti
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d
ex
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co
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s
r
a
a
A
l B
a
r
a
z
a
n
ch
i
)
2417
2
.
1
.
3
.
Descript
io
n
T
h
e
an
al
y
tic
is
d
esi
g
n
ed
w
it
h
th
e
g
o
al
o
f
g
a
th
er
i
n
g
b
asic
f
lo
w
s
tatis
t
i
cs
ab
o
u
t
an
I
P
ad
d
r
ess
.
W
e
co
llected
th
e
to
tal
n
u
m
b
er
o
f
co
n
n
ec
tio
n
s
,
n
u
m
b
er
o
f
p
ac
k
ets
i
n
an
d
o
u
t,
n
u
m
b
er
o
f
b
y
te
s
in
an
d
o
u
t,
s
o
u
r
ce
an
d
d
esti
n
atio
n
p
o
r
t
n
u
m
b
er
s
,
an
d
n
eig
h
b
o
r
s
.
Div
e
n
ti
r
ec
o
r
d
s
th
e
m
a
g
n
i
tu
d
e
o
f
t
h
e
p
ac
k
ets
a
n
d
b
y
tes
b
y
s
to
r
in
g
lo
g
2
x
r
a
th
er
th
a
n
th
e
e
x
ac
t
n
u
m
b
er
x
.
Fo
r
ex
a
m
p
le,
r
ec
o
r
d
s
w
i
th
b
y
te
co
u
n
t
s
b
et
w
ee
n
1
an
d
3
ar
e
r
ec
o
r
d
ed
u
s
i
n
g
m
a
g
n
it
u
d
e
1
an
d
r
ec
o
r
d
s
w
i
th
a
b
y
te
co
u
n
t
f
r
o
m
4
to
7
r
ec
o
r
d
ed
w
it
h
m
ag
n
it
u
d
e
2
.
T
h
e
n
u
m
b
er
o
f
p
ac
k
e
ts
an
d
b
y
tes
is
t
h
er
ef
o
r
e
alr
ea
d
y
b
u
c
k
eted
s
o
as
to
o
b
s
c
u
r
e
s
m
al
l
d
if
f
er
en
ce
s
an
d
h
i
g
h
lig
h
t
lar
g
e
o
n
es
[
2
4
]
.
T
h
e
in
f
o
r
m
atio
n
w
a
s
co
llected
in
r
ea
l
tim
e
an
d
th
e
n
p
r
o
ce
s
s
ed
to
s
ee
w
h
at
b
asic
co
n
cl
u
s
i
o
n
s
w
e
co
u
ld
r
ea
ch
f
r
o
m
t
h
e
d
ata.
W
e
class
if
y
ea
c
h
I
P
ad
d
r
ess
as f
o
llo
w
s
:
-
A
cti
v
e
i
f
it
h
as
m
o
r
e
t
h
an
1
0
0
co
n
n
ec
tio
n
s
w
it
h
i
n
o
u
r
n
et
w
o
r
k
an
d
in
ac
tiv
e
i
f
it
h
as le
s
s
t
h
a
n
2
0
.
-
Hig
h
d
eg
r
ee
(
n
u
m
b
er
o
f
n
eig
h
b
o
r
s
)
if
th
e
d
eg
r
ee
is
g
r
ea
ter
t
h
an
3
0
an
d
a
s
m
all
d
eg
r
ee
i
f
les
s
th
a
n
5
.
-
R
ec
eiv
er
i
f
it r
ec
eiv
e
s
t
w
ice
t
h
e
n
u
m
b
er
o
f
p
ac
k
ets i
t sen
d
s
an
d
a
s
en
d
er
if
t
h
e
o
p
p
o
s
ite
is
t
r
u
e
-
E
lep
h
an
t
if
th
e
a
v
er
ag
e
n
u
m
b
e
r
o
f
b
y
tes
s
e
n
t
o
r
r
ec
eiv
ed
p
er
c
o
n
n
ec
tio
n
is
g
r
ea
ter
t
h
an
1
0
,
0
0
0
b
y
tes,
a
m
o
u
s
e
if
b
o
th
ar
e
less
t
h
a
n
1
0
0
0
b
u
t
g
r
ea
ter
th
a
n
8
0
,
an
d
a
g
n
at
i
f
b
o
th
ar
e
less
t
h
an
8
0
.
-
T
h
ese
ca
teg
o
r
ies
w
er
e
d
ef
i
n
e
d
s
o
m
e
w
h
at
ar
b
itra
r
i
l
y
w
it
h
th
e
g
o
al
o
f
d
e
m
o
n
s
tr
atin
g
th
e
ab
ilit
y
to
q
u
ick
l
y
ca
teg
o
r
ize
th
e
b
eh
av
io
r
o
f
an
I
P
ad
d
r
ess
.
A
s
d
is
c
u
s
s
ed
ea
r
lier
,
d
ee
p
in
s
p
ec
tio
n
in
to
th
e
h
is
to
r
y
o
f
I
P
s
o
r
s
u
b
n
e
ts
is
t
h
e
p
u
r
p
o
s
e
o
f
t
h
is
w
o
r
k
.
2
.
1
.
4
.
Q
uery
perf
o
r
m
a
nce
W
e
f
ir
s
t
d
i
s
co
v
er
ed
t
h
at
t
h
e
p
e
r
f
o
r
m
an
ce
o
f
q
u
er
ies
ac
r
o
s
s
a
r
an
g
e
o
f
lo
g
s
w
a
s
f
air
l
y
co
n
s
ta
n
t
e
v
en
as
th
e
s
ize
o
f
th
e
d
atab
ase
g
r
e
w
m
u
c
h
lar
g
er
.
W
e
p
o
s
it
th
at
th
i
s
is
b
ec
au
s
e
th
e
m
aj
o
r
ity
o
f
a
q
u
er
y
w
o
r
k
lo
ad
is
co
m
p
o
s
ed
later
al
s
ca
n
s
th
r
o
u
g
h
th
e
lea
v
es
o
f
t
h
e
tr
ee
.
T
h
er
ef
o
r
e,
in
cr
ea
s
es
in
th
e
s
m
al
ler
co
s
t
to
tr
av
er
s
e
d
o
w
n
th
e
tr
ee
an
d
f
in
d
th
e
f
ir
s
t
m
atc
h
in
g
k
e
y
ar
e
r
elativ
el
y
i
n
s
ig
n
i
f
ican
t.
E
s
p
ec
iall
y
as th
e
co
s
t o
f
tr
av
er
s
al
in
cr
ea
s
es
as
th
e
lo
g
o
f
th
e
n
u
m
b
er
o
f
r
ec
o
r
d
s
.
W
e
s
h
o
w
th
i
s
tr
en
d
i
n
T
ab
le
1
[
9
]
.
T
ab
le
1
s
h
o
w
n
s
er
v
er
s
id
e
late
n
c
y
,
av
er
ag
ed
o
v
er
3
q
u
er
ies.
Q
u
er
y
late
n
c
y
i
n
cr
ea
s
ed
b
y
2
0
%
wh
ile
t
h
e
s
ize
o
f
t
h
e
d
atab
ase
i
n
cr
ea
s
ed
b
y
n
ea
r
l
y
1
0
0
0
%
o
v
er
th
e
s
a
m
e
p
er
io
d
,
s
h
o
w
i
n
g
th
e
r
elati
v
el
y
f
lat
late
n
c
y
.
I
n
o
r
d
er
to
en
s
u
r
e
u
n
i
f
o
r
m
r
es
u
lts
,
t
h
e
q
u
er
y
ti
m
e
i
s
an
a
v
er
ag
e
o
f
3
q
u
er
ies
[
9
]
.
On
e
m
i
llio
n
lo
g
s
w
er
e
q
u
er
ied
at
in
ter
v
als
w
h
e
n
t
h
e
d
atab
ase
h
ad
s
to
r
ed
b
etw
ee
n
1
m
il
lio
n
an
d
1
b
illi
o
n
lo
g
s
.
Q
u
er
y
late
n
c
y
i
n
cr
ea
s
ed
b
y
2
0
%
w
h
ile
t
h
e
s
iz
e
o
f
th
e
d
atab
ase
in
cr
ea
s
ed
b
y
n
ea
r
l
y
1
0
0
0
%
o
v
er
th
e
s
a
m
e
p
er
io
d
,
s
h
o
w
i
n
g
t
h
e
r
elativ
el
y
f
lat
late
n
c
y
.
I
n
o
r
d
e
r
to
en
s
u
r
e
u
n
i
f
o
r
m
r
esu
lt
s
,
th
e
q
u
er
y
ti
m
e
is
an
av
er
ag
e
o
f
t
h
r
ee
q
u
er
ies
an
d
th
e
q
u
es
y
p
r
o
ce
s
s
in
g
an
d
o
p
ti
m
i
za
tio
n
w
ith
h
i
g
h
le
v
el
lan
g
u
ag
e
as
s
h
o
w
n
i
n
Fi
g
u
r
e
4
[
1
2
]
.
T
h
e
s
er
v
er
w
as
s
h
u
t
d
o
w
n
b
et
w
ee
n
ea
c
h
q
u
er
y
to
p
r
ev
en
t
th
e
r
e
s
u
l
ts
f
r
o
m
b
ein
g
ca
ch
ed
.
A
Dell
P
o
w
er
E
d
g
e
R
5
2
0
w
i
th
1
6
5
GB
o
f
R
AM
an
d
3
2
co
r
es w
as u
s
e
d
f
o
r
th
i
s
test
,
h
o
w
e
v
er
,
th
e
s
iz
e
o
f
t
h
e
R
AM
w
as
n
o
t a
s
ig
n
i
f
ica
n
t
f
ac
to
r
,
as Div
en
ti d
o
es n
o
t p
r
ee
m
p
ti
v
el
y
lo
a
d
d
ata
f
r
o
m
t
h
e
u
n
d
er
l
y
i
n
g
s
to
r
ag
e
in
to
it
s
ca
c
h
e.
T
ab
le
2
[
1
1
]
s
h
o
w
s
t
h
e
p
er
f
o
r
m
an
ce
o
f
t
h
e
m
etr
ics
a
n
al
y
tic
r
u
n
n
i
n
g
u
p
o
n
d
i
v
en
t
i
at
s
u
p
er
co
m
p
u
ti
n
g
.
An
al
y
tic
p
er
f
o
r
m
a
n
ce
f
o
r
cr
ea
tin
g
m
etr
ic
s
en
d
clien
t
’
s
e
n
d
,
R
ea
l
r
ef
er
s
to
th
e
to
tal
tim
e
f
r
o
m
t
h
e
s
tar
t
o
f
th
e
p
r
o
g
r
a
m
to
co
m
p
letio
n
,
U
s
er
to
th
e
a
m
o
u
n
t
o
f
ti
m
e
th
e
p
r
o
ce
s
s
w
a
s
e
x
ec
u
t
in
g
o
n
th
e
C
P
U,
an
d
S
y
s
te
m
to
th
e
a
m
o
u
n
t
o
f
ti
m
e
th
e
p
r
o
ce
s
s
w
as
ex
ec
u
ti
n
g
s
y
s
te
m
ca
ll
s
[
1
1
]
.
T
h
e
Un
ix
u
tili
t
y
ti
m
e
w
as
u
s
ed
to
m
ea
s
u
r
e
th
e
to
tal
a
m
o
u
n
t
o
f
ti
m
e
r
eq
u
i
r
ed
to
cr
ea
te
th
e
m
etr
ic
s
o
n
th
e
clien
t’
s
en
d
.
T
o
e
s
tab
lis
h
th
e
p
er
f
o
r
m
an
ce
an
d
g
en
er
ate
d
ata
w
e
q
u
er
ied
a
s
i
n
g
le
I
P
ad
d
r
ess
,
a
2
5
5
.
2
5
5
.
2
5
5
.
0
s
u
b
n
et
m
a
s
k
,
a
2
5
5
.
2
5
5
.
0
.
0
s
u
b
n
et
m
as
k
,
an
d
th
e
e
n
tire
d
atab
ase
[
2
5
]
.
R
ea
l
r
ef
er
s
to
t
h
e
to
tal
ti
m
e
f
r
o
m
th
e
s
tar
t
o
f
t
h
e
p
r
o
g
r
a
m
to
co
m
p
let
io
n
,
U
s
er
to
th
e
a
m
o
u
n
t
o
f
ti
m
e
th
e
p
r
o
ce
s
s
w
as e
x
ec
u
ti
n
g
o
n
th
e
C
P
U
(
c
en
tr
al
p
r
o
ce
s
s
in
g
u
n
it),
an
d
S
y
s
te
m
to
th
e
a
m
o
u
n
t
o
f
ti
m
e
t
h
e
p
r
o
ce
s
s
w
as
e
x
ec
u
ti
n
g
s
y
s
te
m
ca
lls
.
W
e
s
ee
a
n
in
cr
ea
s
in
g
a
m
o
u
n
t
ti
m
e
s
p
en
t
o
f
f
th
e
C
P
U
in
T
ab
le
2
lik
el
y
b
ec
au
s
e
o
f
t
h
e
in
cr
ea
s
i
n
g
s
ize
an
d
co
m
p
l
e
x
it
y
o
f
th
e
d
ata
s
to
r
ed
o
n
th
e
cli
en
t
en
d
,
ca
u
s
in
g
I
O
b
lo
ck
in
g
w
h
en
p
r
e
f
o
r
m
i
n
g
a
n
al
y
s
i
s
.
T
h
e
an
al
y
tic
w
as
ab
le
to
v
er
y
q
u
ick
l
y
e
s
tab
lis
h
t
h
e
s
tatis
tical
h
i
s
to
r
y
o
f
an
ip
ad
d
r
ess
o
r
s
u
b
n
et
b
y
ta
k
in
g
ad
v
an
tag
e
o
f
t
h
e
B
ε
-
tr
ee
’
s
s
tr
u
c
tu
r
e.
T
h
is
p
r
o
v
id
es
ev
i
d
en
ce
th
a
t
n
e
t
w
o
r
k
f
lo
w
d
atab
ases
w
ill al
lo
w
co
m
p
lex
an
al
y
s
i
s
to
co
m
p
lete
r
ap
id
ly
.
T
ab
le
1
.
Ser
v
er
s
id
e
laten
c
y
,
av
er
ag
ed
o
v
er
3
q
u
er
ies
S
t
o
r
e
d
L
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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N
:
1
6
9
3
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6930
T
E
L
KOM
NI
K
A
T
elec
o
m
m
u
n
C
o
m
p
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l
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l.
1
8
,
No
.
5
,
Octo
b
e
r
2
0
2
0
:
2
4
1
2
-
2420
2418
F
i
g
u
r
e 4
.
Qu
es
y
p
r
o
cess
in
g
an
d
o
p
tim
izatio
n
with
h
ig
h
lev
el l
an
g
u
ag
e q
u
er
y
th
r
o
u
g
h
th
r
ee lev
el
s
o
f
q
u
er
y
o
p
tim
izatio
n
i.
e.
p
ar
s
er
an
d
tr
an
s
lato
r
,
q
u
er
y
o
p
tim
izer
an
d
q
u
er
y
ev
alu
atio
n
en
g
in
e to
g
en
er
ate th
e rea
l
tim
e
r
esu
lts
o
f
o
p
tim
ized
q
u
er
y
b
y
in
s
er
tin
g
d
ata f
r
o
m
th
e d
atab
ase
[
1
2
]
3.
RE
SU
L
T
S
T
h
e
r
esu
lt
s
h
o
w
s
g
r
ap
h
ica
l
r
ep
r
esen
tatio
n
s
o
f
t
h
e
m
etr
ics
co
ll
ec
ted
u
p
o
n
a
s
in
g
le
I
P
ad
d
r
ess
s
h
o
w
n
i
n
Fig
u
r
es
5
-
9
.
T
h
is
I
P
h
ad
6
0
3
co
n
n
ec
tio
n
s
,
t
h
e
s
o
u
r
ce
p
o
r
t
w
a
s
s
ca
tter
ed
,
b
u
t
t
h
e
d
esti
n
at
io
n
p
o
r
t
w
as
al
w
a
y
s
1
3
5
6
8
.
Fro
m
t
h
is
in
f
o
r
m
a
tio
n
th
e
I
P
ad
d
r
ess
i
s
cla
s
s
i
f
ied
as
ac
ti
v
e,
o
f
s
m
al
l
d
eg
r
ee
,
n
eith
er
a
s
e
n
d
er
n
o
r
r
ec
eiv
er
,
an
d
a
m
o
u
s
e.
T
h
is
I
P
ad
d
r
ess
w
as
lik
e
l
y
r
ec
ei
v
i
n
g
a
n
d
s
en
d
i
n
g
d
ata
to
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s
m
al
l
n
u
m
b
er
o
f
o
th
er
I
P
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d
r
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es f
r
o
m
a
p
r
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ce
s
s
r
u
n
n
i
n
g
o
n
p
o
r
t 1
3
5
6
8
.
T
h
e
n
eig
h
b
o
r
h
is
to
g
r
a
m
i
n
d
icate
s
t
h
at
t
h
e
b
eh
av
io
r
o
f
th
i
s
I
P
ad
d
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ess
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lik
el
y
m
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t d
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en
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g
h
b
o
r
4
.
A
t
t
h
e
ti
m
e
th
e
an
al
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tic
w
as
r
u
n
1
,
4
8
0
,
0
2
4
I
P
ad
d
r
ess
es
w
er
e
s
to
r
ed
w
it
h
in
t
h
e
d
atab
ase.
B
ased
u
p
o
n
th
e
s
tat
is
tical
s
u
m
m
ar
y
r
et
u
r
n
ed
w
h
e
n
t
h
e
an
al
y
tic
w
a
s
r
u
n
ac
r
o
s
s
th
e
en
t
ir
e
d
atab
ase
ea
ch
I
P
ad
d
r
ess
w
a
s
m
atc
h
ed
w
it
h
t
h
e
clas
s
i
f
icati
o
n
s
d
escr
ib
ed
in
s
u
b
s
ec
tio
n
2
.
4
.
T
h
e
n
u
m
b
er
o
f
I
P
ad
d
r
ess
es
th
a
t
m
atch
ea
c
h
ca
teg
o
r
y
ar
e
s
h
o
w
n
i
n
T
ab
le
3
alo
n
g
w
it
h
t
h
e
p
er
ce
n
tag
e
o
f
t
h
e
to
tal
I
P
s
w
h
ic
h
m
atc
h
ed
.
U
s
in
g
t
h
i
s
tab
le,
it
is
o
b
s
er
v
ed
th
at
a
v
as
t
m
aj
o
r
ity
o
f
I
P
s
w
er
e
class
if
ied
a
s
i
n
ac
t
iv
e,
s
m
all
d
eg
r
ee
,
s
e
n
d
er
s
,
o
r
g
n
at
s
.
B
ased
u
p
o
n
th
is
i
n
f
o
r
m
atio
n
,
w
e
co
u
ld
p
o
s
it
th
at
a
v
ast
a
m
o
u
n
t
o
f
th
e
tr
af
f
ic
co
llected
w
as
co
m
p
o
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ed
o
f
s
i
m
p
le
in
ter
ac
tio
n
s
w
h
ic
h
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id
n
’
t
r
eq
u
ir
e
m
u
c
h
d
a
ta
to
b
e
tr
an
s
f
er
r
ed
b
ac
k
to
t
h
e
r
ec
eiv
er
t
h
er
ef
o
r
e
m
o
s
t
p
a
ck
ets
w
er
e
s
en
t
b
y
th
e
o
r
ig
i
n
ato
r
.
So
m
e
ex
a
m
p
le
s
o
f
th
i
s
t
y
p
e
o
f
tr
a
f
f
ic
in
c
lu
d
e
DNS
(
d
o
m
ai
n
n
a
m
e
s
er
v
i
ce
)
lo
o
k
u
p
s
,
I
C
MP
(
in
ter
n
et
co
n
tr
o
l
m
es
s
ag
e
p
r
o
to
co
l)
m
es
s
ag
e
s
,
an
d
SYN
s
ca
n
n
i
n
g
.
F
i
g
u
r
e 5
.
Pack
et
-
Ou
t
f
o
r
s
in
g
le I
P
ad
d
r
ess
o
n
X
-
ax
is
.
L
o
g
s
cale f
o
r
lo
g
g
in
g
with
q
u
er
y
in
g
n
etwo
r
k
f
lo
ws
an
d
m
atch
in
g
co
n
n
ectio
n
s
o
n
Y
-
ax
is
.
F
i
g
u
r
e 6
.
Pack
et
-
I
n
f
o
r
s
in
g
le I
P
ad
d
r
ess
o
n
X
-
ax
is
.
L
o
g
s
cale f
o
r
lo
g
g
in
g
with
q
u
er
y
in
g
n
etwo
r
k
f
lo
ws
an
d
m
atch
in
g
co
n
n
ectio
n
s
o
n
Y
-
ax
i
s.
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KOM
NI
K
A
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l
C
o
n
tr
o
l
Da
ta
b
a
s
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tech
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r
r
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en
t n
etw
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k
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d
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p
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tio
n
(
I
s
r
a
a
A
l B
a
r
a
z
a
n
ch
i
)
2419
F
i
g
u
r
e 7
.
B
y
tes
-
I
n
f
o
r
s
in
g
le
I
P
ad
d
r
ess
o
n
X
-
ax
is
.
L
o
g
s
cale f
o
r
lo
g
g
in
g
with
q
u
er
y
in
g
n
etwo
r
k
f
lo
ws
an
d
m
atch
in
g
co
n
n
ectio
n
s
o
n
Y
-
ax
is
.
F
i
g
u
r
e 8
.
Neig
h
b
o
r
h
is
t
o
g
r
am
w
ith
f
o
u
r
d
i
f
f
er
en
t
n
eig
h
b
o
r
s
o
n
X
-
ax
is
.
L
o
g
s
cale
f
o
r
lo
g
g
in
g
with
q
u
er
y
in
g
n
etwo
r
k
f
lo
ws
an
d
m
atch
in
g
co
n
n
ectio
n
s
o
n
Y
-
ax
is
F
i
g
u
r
e 9
.
Neig
h
b
o
r
h
is
t
o
g
r
am
w
ith
f
o
u
r
d
i
f
f
er
en
t
n
eig
h
b
o
r
s
o
n
X
-
ax
is
.
L
o
g
s
cale f
o
r
lo
g
g
in
g
w
ith
q
u
er
y
in
g
n
etwo
r
k
f
lo
ws
an
d
m
atch
in
g
co
n
n
ectio
n
s
o
n
Y
-
ax
is
T
ab
le
3
.
C
lass
if
icatio
n
s
o
f
s
u
p
er
co
m
p
u
ti
n
g
tr
a
f
f
ic
C
l
a
ssi
f
i
c
a
t
i
o
n
N
u
mb
e
r
o
f
M
a
t
c
h
e
s
%
o
f
T
o
t
a
l
I
n
a
c
t
i
v
e
1
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1
7
6
,
0
9
7
7
9
.
4
6
A
c
t
i
v
e
1
2
8
,
2
1
8
8
.
6
6
S
mal
l
d
e
g
r
e
e
9
9
5
,
7
4
1
6
7
.
2
8
H
i
g
h
d
e
g
r
e
e
2
0
4
,
4
9
1
1
3
.
8
2
S
e
n
d
e
r
s
1
,
1
8
7
,
9
7
9
8
0
.
2
7
R
e
c
e
i
v
e
r
s
7
7
,
5
4
1
5
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2
4
El
e
p
h
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n
t
s
1
4
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7
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1
.
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M
i
c
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0
G
n
a
t
s
1
,
3
4
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1
.
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0
4.
DIS
CU
SS
I
O
N
First,
it
s
h
o
u
ld
b
e
n
o
ted
t
h
e
a
n
al
y
tic
p
r
o
v
id
es
n
o
ab
ilit
y
to
d
e
ter
m
i
n
e
t
h
e
p
o
r
tio
n
o
f
I
P
s
w
h
i
ch
m
atch
ed
m
u
ltip
le
ca
te
g
o
r
ies.
E
ac
h
s
e
t
o
f
class
if
icatio
n
s
:
i
n
ac
ti
v
e
a
n
d
ac
tiv
e,
s
m
a
ll
d
eg
r
ee
an
d
h
ig
h
d
e
g
r
ee
,
etc.
is
ca
lcu
lated
i
n
is
o
latio
n
.
A
s
i
m
p
le
i
m
p
r
o
v
e
m
en
t
to
th
e
a
n
al
y
tic
w
o
u
ld
b
e
to
ad
d
th
is
ca
p
ab
ilit
y
allo
w
i
n
g
th
e
u
s
er
to
ze
r
o
in
o
n
p
ar
ticu
lar
ly
r
ar
e
b
eh
av
io
r
s
.
Div
en
ti
w
a
s
g
r
a
n
ted
o
n
l
y
li
m
ited
ac
ce
s
s
to
th
e
s
u
p
er
co
m
p
u
tin
g
n
et
w
o
r
k
.
A
s
a
co
n
s
eq
u
e
n
ce
o
f
th
is
li
m
ited
n
et
w
o
r
k
v
i
s
ib
ilit
y
,
s
o
m
e
r
esu
lts
m
a
y
b
e
in
co
m
p
l
ete.
I
t
is
im
p
o
r
tan
t
th
at
a
n
y
o
r
g
a
n
izatio
n
s
ee
k
in
g
t
o
e
m
p
lo
y
n
et
w
o
r
k
m
o
n
ito
r
in
g
ca
r
ef
u
ll
y
co
n
s
id
er
t
h
e
i
m
p
licat
io
n
s
o
f
t
h
e
v
is
ib
ili
t
y
p
r
o
v
id
ed
b
y
th
eir
n
e
t
w
o
r
k
tap
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
1
6
9
3
-
6930
T
E
L
KOM
NI
K
A
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l
C
o
n
tr
o
l
,
Vo
l.
1
8
,
No
.
5
,
Octo
b
e
r
2
0
2
0
:
2
4
1
2
-
2420
2420
5.
CO
NCLU
SI
O
NS
Usi
n
g
d
atab
ases
d
esi
g
n
ed
f
o
r
th
e
b
ig
d
ata
ch
alle
n
g
e
s
ass
o
ci
ated
w
it
h
lo
g
g
in
g
an
d
q
u
er
y
in
g
n
et
w
o
r
k
f
lo
w
s
is
n
ec
e
s
s
ar
y
i
n
o
r
d
er
to
p
r
o
v
id
e
n
et
w
o
r
k
o
p
er
ato
r
s
w
it
h
a
lar
g
er
,
m
o
r
e
ef
f
icie
n
t w
in
d
o
w
in
to
th
e
n
et
w
o
r
k
tr
af
f
ic
o
f
b
o
th
p
a
s
t
a
n
d
p
r
esen
t.
I
ts
i
m
p
er
ati
v
e
f
o
r
t
h
e
d
ev
e
lo
p
m
e
n
t
o
f
au
to
m
ated
a
n
al
y
ti
cs
an
d
f
o
r
ef
f
ec
ti
v
e
p
o
s
t
-
i
n
tr
u
s
io
n
in
v
es
tig
a
tio
n
s
t
h
at
th
e
s
e
m
et
h
o
d
s
ar
e
ad
o
p
ted
w
h
e
n
lo
g
g
i
n
g
n
e
t
w
o
r
k
f
lo
w
s
.
T
h
is
r
esear
ch
p
ap
er
s
h
o
w
s
t
h
at
Div
e
n
ti
w
as a
b
le
to
in
g
es
t o
v
er
a
b
illi
o
n
lo
g
s
w
h
i
l
e
p
r
o
v
id
in
g
r
ap
id
q
u
er
y
r
esp
o
n
s
e
s
w
it
h
r
elativ
e
l
y
f
lat
late
n
c
y
.
T
h
ese
q
u
er
ies
ar
e
ca
p
ab
le
o
f
q
u
ick
l
y
co
llecti
n
g
in
f
o
r
m
a
tio
n
r
e
g
ar
d
in
g
I
P
a
d
d
r
ess
es
to
class
i
f
y
th
e
m
i
n
to
v
ar
io
u
s
ca
teg
o
r
ies.
R
u
n
n
i
n
g
th
i
s
an
al
y
s
i
s
at
s
u
p
er
co
m
p
u
t
in
g
2
0
1
9
r
ev
ea
led
th
at
a
m
aj
o
r
it
y
o
f
th
e
tr
af
f
ic
co
llected
w
a
s
co
m
p
o
s
ed
o
f
s
i
m
p
le
in
ter
ac
tio
n
s
s
u
c
h
as
DNS
(
d
o
m
ain
n
a
m
e
s
er
v
ice)
lo
o
k
u
p
s
,
I
C
MP
(
in
ter
n
et
co
n
tr
o
l
m
es
s
a
g
e
p
r
o
to
co
l)
m
es
s
ag
e
s
,
an
d
SY
N
s
ca
n
n
i
n
g
.
I
n
co
n
j
u
n
ct
io
n
w
it
h
m
ac
h
i
n
e
lear
n
i
n
g
an
d
o
th
er
cu
tti
n
g
-
ed
g
e
tec
h
n
i
q
u
es,
th
ese
d
atab
ases
allo
w
s
ec
u
r
it
y
p
er
s
o
n
n
el
to
u
s
e
t
h
eir
ti
m
e
to
ef
f
icien
tl
y
id
en
ti
f
y
t
h
r
ea
ts
a
n
d
r
esp
o
n
d
to
aler
ts
in
s
tead
o
f
w
a
iti
n
g
f
o
r
in
f
o
r
m
atio
n
.
RE
F
E
R
E
NC
E
S
[1
]
B.
A
.
A
lah
m
a
d
i
a
n
d
I.
M
a
rti
n
o
v
ic,
“
M
a
lCl
a
ss
if
ier:
M
a
l
w
a
r
e
f
a
m
il
y
c
l
a
ss
i
f
ica
ti
o
n
u
sin
g
n
e
tw
o
rk
f
lo
w
se
q
u
e
n
c
e
b
e
h
a
v
io
u
r,
”
2
0
1
8
AP
W
G S
y
mp
o
si
u
m o
n
El
e
c
tro
n
ic Crime
Res
e
a
rc
h
(
e
Crim
e
)
,
n
o
.
1
,
p
p
.
1
-
1
3
,
M
a
y
2
0
1
8
.
[2
]
S
.
Am
b
ro
g
io
e
t
a
l.
,
“
Ne
u
ro
m
o
rp
h
ic
lea
rn
in
g
a
n
d
re
c
o
g
n
it
i
o
n
w
it
h
o
n
e
-
tran
sisto
r
-
one
-
re
sisto
r
sy
n
a
p
se
s
a
n
d
b
istab
le
m
e
tal
Ox
id
e
RR
A
M
,
”
IEE
E
T
ra
n
s.
El
e
c
tro
n
De
v
ice
s
,
v
o
l.
6
3
,
n
o
.
4
,
p
p
.
1
5
0
8
-
1
5
1
5
,
A
p
ril
2
0
1
6
.
[3
]
V
e
rizo
n
b
u
sin
e
ss
re
a
d
y
,
“
2
0
1
9
Da
ta Brea
c
h
In
v
e
stig
a
ti
o
n
s Re
p
o
rt,
”
[
On
l
in
e
]
A
v
a
il
a
b
le:
h
tt
p
s:/
/en
terp
rise
.
v
e
rizo
n
.
c
o
m
/
re
so
u
rc
e
s/re
p
o
rts/2
0
1
9
-
d
a
ta
-
b
re
a
c
h
-
in
v
e
stig
a
ti
o
n
s
-
re
p
o
rt.
p
d
f
.
[4
]
El
a
stics
e
a
rc
h
Re
fe
re
n
c
e
,
“
T
h
e
De
f
in
it
iv
e
G
u
id
e
,
”
[
O
n
li
n
e
]
A
v
a
il
a
b
le:
h
tt
p
s://
w
ww
.
e
l
a
stic.c
o
/g
u
id
e
/en
/ela
stics
e
a
rc
h
/
g
u
id
e
/m
a
ste
r/i
n
d
e
x
.
h
tm
l
[5
]
N.
P
.
Do
n
o
g
h
u
e
,
e
t
a
l
.
,
“
T
ra
c
k
in
g
n
e
tw
o
rk
e
v
e
n
ts
w
it
h
w
rit
e
o
p
ti
m
ize
d
d
a
ta
str
u
c
tu
re
s:
T
h
e
d
e
sig
n
a
n
d
im
p
le
m
e
n
tatio
n
o
f
T
W
I
A
D:
T
h
e
w
rit
e
-
o
p
ti
m
ize
d
IP
a
d
d
re
ss
d
a
tab
a
se
,
”
Pro
c
.
2
0
1
5
4
th
In
t.
W
o
r
k
.
Bu
i
ld
.
A
n
a
l
.
Da
ta
se
ts Ga
t
h
e
r.
Exp
.
Retu
rn
s
S
e
c
u
r.
BA
DG
ER
S
2
0
1
5
,
p
p
.
1
-
7
,
2
0
1
7
.
[6
]
Zee
k
,
“
Ne
t
w
o
rk
S
e
c
u
rit
y
M
o
n
it
o
r
,
”
[
O
n
li
n
e
]
A
v
a
il
a
b
le:
h
tt
p
s://
ww
w
.
z
e
e
k
.
o
rg
/.
[7
]
T
.
M
a
h
m
o
o
d
a
n
d
U.
A
f
z
a
l,
“
S
e
c
u
rit
y
a
n
a
l
y
ti
c
s:
Big
d
a
ta
a
n
a
l
y
ti
c
s
fo
r
c
y
b
e
rse
c
u
rit
y
:
A
re
v
ie
w
o
f
tre
n
d
s,
tec
h
n
iq
u
e
s
a
n
d
t
o
o
ls,”
2
0
1
3
2
n
d
N
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
I
n
fo
rm
a
ti
o
n
Assu
r
a
n
c
e
(
NCIA)
,
p
p
.
1
2
9
-
1
3
4
,
De
c
e
m
b
e
r
2
0
1
3
.
[8
]
P
.
Ha
a
g
Nfs
e
n
,
“
Ne
tf
lo
w
se
n
so
r,
”
[
O
n
li
n
e
]
A
v
a
il
a
b
le:
n
f
se
n
.
so
u
rc
e
f
o
rg
e
.
n
e
t.
[9
]
D.
P
l
o
n
k
a
F
lo
w
sc
a
n
,
“
Ne
tw
o
rk
T
ra
ff
ic
F
lo
w
V
is
u
a
li
z
a
ti
o
n
a
n
d
Re
p
o
rti
n
g
T
o
o
l
”
[
O
n
li
n
e
]
A
v
a
il
a
b
le:
ww
w
.
c
a
id
a
.
o
rg
/t
o
o
ls/
u
ti
li
ti
e
s/f
lo
w
s
c
a
n
/.
[1
0
]
T
.
Oe
ti
k
e
r,
J.
Bru
tl
a
g
,
a
n
d
A
.
Bo
g
a
e
rd
t
R,
“
A
b
o
u
t
RRDt
o
o
l
,
”
[
On
li
n
e
]
A
v
a
il
a
b
le:
h
tt
p
s://
o
ss
.
o
e
ti
k
e
r.
c
h
/rrd
t
o
o
l/
.
[1
1
]
D.
Hu
tch
iso
n
a
n
d
J.
C.
M
i
tch
e
ll
,
“
IP
O
p
e
ra
ti
o
n
s a
n
d
M
a
n
a
g
e
m
e
n
t,
”
9
th
IEE
E
In
ter
b
a
ti
o
n
a
l
W
o
rk
sh
o
p
,
2
0
0
8
.
[1
2
]
F
.
M
a
tern
it
y
e
t
a
l.
,
“
N
o
主
観
的健康感を中心とした在宅高
齢
者における
健康
関
連
指標に
関
する共分散構造
分析
T
i
t
l
e
,”
I
n
t.
Rev
.
Imm
u
n
o
l.
,
v
o
l.
6
6
,
n
o
.
1
,
p
p
.
1
-
1
5
,
2
0
1
8
.
[1
3
]
N.
J.
Qa
si
m
,
e
t
a
l
.
,
“
Re
a
c
ti
v
e
p
ro
to
c
o
ls
f
o
r
u
n
if
ied
u
se
r
p
r
o
f
il
in
g
f
o
r
a
n
o
m
a
l
y
d
e
tec
ti
o
n
in
m
o
b
il
e
A
d
Ho
c
n
e
tw
o
rk
s,”
Per
io
d
ica
ls
o
f
En
g
in
e
e
rin
g
a
n
d
N
a
tu
ra
l.
S
c
ien
c
e
,
v
o
l.
7
,
n
o
.
2
,
p
p
.
8
4
3
-
8
5
2
,
A
u
g
u
st 2
0
1
9
.
[1
4
]
J.
L
iu
,
e
t
a
l
.
,
“
S
o
f
tw
a
re
-
d
e
f
in
e
d
in
tern
e
t
o
f
th
i
n
g
s
f
o
r
sm
a
rt
u
rb
a
n
se
n
sin
g
,
”
IEE
E
Co
mm
u
n
ica
ti
o
n
s
M
a
g
a
zi
n
e
,
v
o
l.
5
3
,
n
o
.
9
,
p
p
.
5
5
-
6
3
,
S
e
p
tem
b
e
r
2
0
1
5
.
[1
5
]
O.
S
a
lm
a
n
,
e
t
a
l
.
,
“
Id
e
n
ti
ty
-
b
a
se
d
a
u
t
h
e
n
ti
c
a
ti
o
n
sc
h
e
m
e
f
o
r
th
e
I
n
tern
e
t
o
f
T
h
in
g
s,”
Pro
c
.
-
IE
EE
S
y
mp
.
C
o
mp
u
t.
Co
mm
u
n
.
,
p
p
.
1
1
0
9
-
1
1
1
1
,
Ju
n
e
2
0
1
6
.
[1
6
]
S
.
C
h
a
k
ra
b
a
rty
,
e
t
a
l
.
,
“
Blac
k
S
DN
f
o
r
th
e
in
tern
e
t
o
f
th
i
n
g
s,”
2
0
1
5
IE
EE
1
2
th
In
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
M
o
b
il
e
Ad
Ho
c
a
n
d
S
e
n
so
r
S
y
ste
ms
,
p
p
.
1
9
0
-
1
9
8
,
Oc
t
2
0
1
5
.
[1
7
]
T
.
T
h
e
o
d
o
ro
u
,
e
t
a
l
.
,
“
A
m
u
lt
i
-
p
ro
to
c
o
l
so
f
tw
a
re
-
d
e
f
in
e
d
n
e
tw
o
rk
in
g
so
lu
ti
o
n
f
o
r
t
h
e
I
n
tern
e
t
o
f
T
h
in
g
s,”
IEE
E
Co
mm
u
n
.
M
a
g
.
,
v
o
l.
5
7
,
n
o
.
1
0
,
p
p
.
4
2
-
4
8
,
Oc
t
2
0
1
9
.
[1
8
]
A
.
K.
T
ra
n
,
e
t
a
l
.
,
“
S
DN
c
o
n
tro
ll
e
r
p
lac
e
m
e
n
t
in
Io
T
n
e
tw
o
rk
s:
A
n
o
p
ti
m
ize
d
su
b
m
o
d
u
larity
-
b
a
se
d
a
p
p
ro
a
c
h
,
”
S
e
n
so
rs
(S
w
it
z
e
rlan
d
),
v
o
l
.
1
9
,
n
o
.
2
4
,
p
p
.
1
-
1
2
,
De
c
e
m
b
e
r
2
0
1
9
.
[1
9
]
I
.
A
l
Ba
r
a
z
a
n
c
h
i,
e
t
a
l
.
,
“
In
n
o
v
a
ti
v
e
tec
h
n
o
lo
g
ies
o
f
w
irele
ss
se
n
so
r
n
e
tw
o
rk
:
T
h
e
a
p
p
li
c
a
ti
o
n
s
o
f
W
BA
N
s
y
ste
m
a
n
d
e
n
v
iro
n
m
e
n
t,
”
S
u
st
a
in
.
En
g
.
I
n
n
o
v
.
,
v
o
l.
1
,
n
o
.
2
,
p
p
.
9
8
-
1
0
5
,
2
0
2
0
.
[2
0
]
K.
S
o
o
d
,
e
t
a
l
.
,
“
S
o
f
tw
a
re
-
d
e
f
i
n
e
d
w
irele
ss
n
e
t
w
o
rk
in
g
o
p
p
o
rt
u
n
it
ies
a
n
d
c
h
a
ll
e
n
g
e
s
f
o
r
In
tern
e
t
o
f
T
h
in
g
s:
A
re
v
ie
w
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R.
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.
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.
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