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1
Web
ML
Co
nce
ptu
al
Co
nce
ptu
al
Mo
dell
ing
Mo
dell
ing
of
Web
o
f W
eb
A
pp
lica
tio
ns,
A
pp
lica
tio
ns,
W
ork
flo
ws
an
d W
eb
Serv
ices
Wo
rkfl
ow
s an
d W
eb
Serv
ices
Pie
ro F
rate
rnali
DE
I, P
oli
tecn
ico
di
Mil
an
op
iero
.fra
tern
ali
@p
oli
mi.
it
WIS
E T
uto
rial
, Rom
a,
Dec
10th
2003
Join
t w
ork
with:
Ste
fano
Cer
i, A
ldo
Bon
gio
, M
arco
Bra
mbill
a, S
ara
Com
ai,
Ioan
a M
anol
escu
2
Web
ML
Ou
tlin
e o
f th
e t
uto
rial
I.Pr
oble
m d
efin
itio
n:
des
ign a
nd c
onst
ruct
ion o
f a w
eb-
bas
ed info
rmat
ion s
yste
mII
.La
yers
of th
e so
lution
:1.
Data
mod
elin
g:
def
ine
the
conte
nt
2.
Hyp
erte
xt m
odel
ing:
def
ine
the
Web
applic
ation
3.
Pers
onal
izat
ion:
giv
e a
diffe
rent
view
to d
iffe
rent
use
rs4.
Pre
senta
tion
& c
ode
gen
erat
ion w
ith W
ebRatio
5.
Inte
gra
ting b
usi
nes
s pro
cess
es
6.
Inte
gra
ting W
eb s
ervi
ce inte
ract
ions
At
ever
y la
yer:
Exa
mple
within
the
Web
ML
fram
ewor
kCom
pari
son w
ith o
ther
tec
hniq
ues
When
applic
able
: dem
o o
f th
e co
nce
pts
3
Web
ML
I.1
Pro
ble
m d
efi
nit
ion
4
Web
ML
Web
-base
d
info
rmati
on
syst
em
s
A W
eb-e
nab
led s
oft
war
e sy
stem
whose
mai
n
purp
ose
is
to p
ublis
h a
nd m
ainta
in lar
ge
amounts
of
dat
aW
ith b
row
sing-o
rien
ted inte
rface
sSupport
ing c
onte
xt-d
epen
den
t nav
igation
Publis
hin
g c
onte
nts
on (
arbitra
rily
com
ple
x) W
eb p
ages
With d
ata
sto
red b
y m
eans
of D
BM
S t
echnol
ogy
Dyn
amic
pag
e co
mputa
tion
fro
m D
B c
onte
nt
DBs
poss
ibly
dis
trib
ute
d,
het
erogen
eous,
and
pre
-exi
stin
g
the
Web
applic
atio
n
5
Web
ML
Exam
ple
s o
f W
eb
-base
d
info
rmati
on
syst
em
sCom
mer
ce-o
rien
ted
Ele
ctro
nic
cat
alo
gs,
auct
ions,
vir
tual
mark
etpla
ces
Conte
nt-
orien
ted
Onlin
e new
spaper
s, d
igital lib
rari
es
Ser
vice
-orien
ted
Ord
er t
rack
ing s
ytem
s, r
eser
vation
sys
tem
s, t
ouri
st
info
rmat
ion s
yste
ms
Com
munity-
ori
ente
dPo
rtal
s, m
essa
ge
boa
rds,
tec
hnic
al c
om
munitie
s
6
Web
ML
Tre
nd
s in
data
-in
ten
sive
Web
sit
es
Enco
din
g b
usi
nes
s pro
cess
esw
ithin
Web
applic
ation
sM
ulti-
agen
t ap
plic
atio
ns
Inte
gra
ting t
he
not
ions
of:
pro
cess
, act
ivity,
wor
k as
signm
ent,
and t
he
clas
sica
l w
ork
flow
org
aniz
atio
ns
(seq
uen
ce,
concu
rren
t par
alle
lism
, m
utu
al e
xclu
sion
)Ach
ievi
ng inte
roper
abili
ty b
y m
eans
of W
eb s
ervi
ces
The
mod
ern w
ay
of d
eplo
ying d
istr
ibute
d a
pplic
ation
s A w
ay
for
organiz
ation
s to
make
thei
r applic
ation
s publis
hed
on t
he
Web
for
use
by
other
applic
ation
sThes
e is
sues
are
ort
hog
onal
7
Web
ML
Fu
rth
er
tren
ds
an
d c
om
ple
xit
y f
act
ors
Multi-
modal
, m
ulti-
dev
ice
applic
atio
ns
Nee
d o
f m
odel
ing b
oth t
he
gen
eric
and t
he
dev
ice-
spec
ific
par
ts o
f th
e ap
plic
atio
nTec
hnol
ogie
s: P
C,
PD
A,
WAP p
hon
es,
3rd
gen
phon
es,
Dig
ital
TV,
video
text
Pers
onal
isation a
nd o
ne-
to-o
ne
del
iver
y N
eed o
f m
odel
ing b
oth t
he
gen
eric
and t
he
use
r-sp
ecific
co
mpon
ents
of th
e applic
ation
Know
n a
pplic
ation
s: m
yYah
oo,
myC
DN
OW
,…
Pres
enta
tion
An e
ssen
tial
par
t of
any W
eb a
pplic
atio
nShou
ld b
e or
thog
onal and a
ddre
ssed
by
com
munic
ation
sp
ecia
lists
Web
ML
I.2
Pro
ble
m illu
stra
tio
n:
run
nin
g e
xam
ple
(c
ase
stu
dy)
9
Web
ML
Ru
nn
ing
exam
ple
: a b
an
k
ap
plica
tio
n
Bank
clie
nts
ow
n a
per
sonal acc
ount
Day
-to-
day
oper
atio
n:
insp
ect
last
tra
nsa
ctio
n
E-m
ail ban
k em
plo
yee
super
visi
ng t
he
acco
unt
Loan
applic
atio
n:
Vie
w loan
conditio
ns
pro
pos
ed b
y th
e bank
Supply
applic
atio
ns
with p
erso
nal
loa
n p
aram
eter
s
Ban
k em
plo
yees
Insp
ect
clie
nt
acco
unts
and s
alar
ies
Ran
k lo
an a
pplic
atio
ns
(bac
kgro
und c
hec
ks)
Res
pon
d t
o e-
mails
Bank
manager
sAppro
ve loa
n c
ontr
acts
10
Web
ML
Bu
sin
ess
pro
cess
es
an
d
Web
serv
ices
in t
he
run
nin
g e
xam
ple
Enco
din
g b
usi
nes
s pro
cess
esw
ithin
Web
applic
ations
Clie
nts
apply
for
loa
ns
Em
plo
yees
must
per
form
tw
o para
llel ch
ecks
(e
mplo
ymen
t his
tory
and s
avin
gs
his
tory
) If
bot
h c
hec
ks s
ucc
eed,
man
ager
s ap
pro
ve a
pplic
atio
n.
Web
ser
vice
support
One
or
bot
h c
hec
ks m
ay
be
pro
vided
by
exte
rnal
com
pon
ents
(w
eb s
ervi
ces)
The
whol
e lo
an a
pplic
atio
n p
roce
ss m
ay b
e ex
port
ed a
s a
Web
ser
vice
11
Web
ML
I.3
Pro
ble
ms
in t
he c
urr
en
t W
eb
ap
plica
tio
n d
evelo
pm
en
t p
ract
ice
12
Web
ML
Lack
of
meth
od
s an
d
mo
dels
Lack
of a
wel
l-fo
unded
soft
war
e en
gin
eeri
ng m
ethods
Dat
a-ce
ntr
ic m
ethods
do n
ot
cove
r th
e hyp
erte
xt fro
nt-
end
OO
met
hods
(e.g
., t
hose
bas
ed o
n U
ML
pro
file
s) d
o n
ot
captu
re m
any
esse
ntial
ingre
die
nts
of W
eb-b
ased
sys
tem
s (e
.g.,
the
front-
end)
Lack
of m
odel
-dri
ven s
uppor
t N
avi
gation
and p
rese
nta
tion
poo
rly
mod
elle
dLo
t of hand-w
ritt
en c
ode
Big
effor
ts a
re r
eques
ted e
ven for
pro
toty
pin
g
The
tota
l co
st o
f ow
ner
ship
is
dom
inate
d b
y ch
ange
managem
ent
and m
ain
tenance
13
Web
ML
Web
mo
dellin
g &
C
on
all
en
’s U
ML W
eb
exte
nsi
on
UM
L W
eb m
odel
ing e
xten
sion [
Con00]
Bas
ed o
n t
he
UM
L ex
tensi
bili
ty f
eatu
res:
st
ereo
types
Page
model
ing s
tere
oty
pes
:Ser
ver
pag
e, c
lient
pag
e, t
arget
, fr
ames
et,
form
Nav
igation m
odel
ing s
tere
oty
pes
:Li
nk,
tar
get
ed lin
k, r
edirec
ts,
subm
its
14
Web
ML
No
tati
on
s an
d e
xam
ple
15
Web
ML
Evalu
ati
on
Pros: Im
ple
men
ted in v
ari
ous
CASE t
ool
s (e
.g.,
Ration
al Ros
e XD
E)
Base
d o
n a
univ
ersa
l not
ation
(U
ML)
Use
r-ex
tensi
ble
Cons:
Low
-lev
el a
bst
ract
ions
Only
a s
ynta
x, n
o sp
ecific
Web
sem
antics
Dia
gra
ms
of r
ealis
tic
Web
applic
ation
s te
nd t
o bec
ome
quic
kly
unm
anagea
ble
Web
pag
es
are
no
t o
bje
ct-o
rien
ted!
16
Web
ML
Web
mo
dellin
g &
ER
Ora
cle D
esi
gn
er
E-R
bas
ed W
eb m
odel
ing:
Propriet
ary
ext
ensi
on o
f th
e data
model
ing
met
hodolo
gy
pro
pose
d b
y O
racl
eAdd-o
ns
for
model
ling W
eb inte
rfac
esM
odule
s =
gro
ups
of re
late
d p
ages
Entities
= p
ublis
hable
or
updata
ble
con
tent
Nav
igab
le R
elat
ionsh
ips
= lin
ks b
etw
een e
ntity
in
stan
ces
17
Web
ML
Exam
ple
: m
od
ule
dia
gra
m
FUN
DS
CO
DE
FUN
D_N
AME
LAU
NC
HED
HO
LDIN
GAV
_GR
OW
THM
ANAG
ER_N
AME
MAN
AGER
_HO
ME
TAR
GET
FUN
D_S
ECTO
RS
CO
DE
NAM
E TA
RG
ET_P
ERC
ENT
ACTU
AL_
PER
CEN
TFU
N_C
OD
E
FUN
D_H
OLD
ING
SFU
N_C
OD
EFU
N_3
CG
_CO
DE
LC_C
OD
EN
UM
BER
_STO
CK
PR
ICE
VALU
ED
ATE_
LAST
_CH
AN
GED
LAST
_CH
AN
GE
18
Web
ML
Evalu
ati
on
Pros: Im
ple
men
ted in a
n O
racl
e CASE t
ool (D
esig
ner
2000
and 9
i)Base
d o
n a
wel
l-kn
own n
otation
(ER)
Cod
e gen
erat
ion fro
m h
igh-l
evel
spec
ific
ation
sCon
s: Data
-cen
tric
abst
ract
ions
Rig
id in m
odel
ing W
eb-s
pec
ific
fea
ture
s (e
.g.,
ser
ver-
side
busi
nes
s lo
gic
, pre
senta
tion
)W
eb a
pplic
ation
s w
ith r
ealis
tic
requir
emen
ts n
ot
managea
ble
Web
pag
es
are
no
t d
ata
str
uct
ure
s!
19
Web
ML
Pro
cess
mod
eli
ng
w
ith
in W
eb
ap
plica
tio
ns
BEA W
ebLo
gic
Work
shop:
Vis
ual
too
l fo
r des
ignin
g W
eb a
pplic
ation
s in
cludin
g:
pag
es,
action
s, n
avig
atio
n…
Als
o W
eb s
ervi
ce s
pec
ific
atio
nCod
e gen
erat
ion for
the
Web
applic
ation
ske
leto
n,
incl
udin
g s
tate
managem
ent,
ses
sion
s et
c.
Runs
on t
op o
f an a
pplic
ation s
erve
rIn
cludes
the
tools
for
"rea
l" w
ork
flow
man
agem
ent
20
Web
ML
Pro
cess
mod
eli
ng
in
B
EA
Web
Lo
gic
Wo
rksh
op
Java
+ c
ust
om
ext
ensi
ons
for
Web
app.
pro
cess
Gra
phic
al v
iew
der
ivable
fro
m t
he
exte
nsi
ons
21
Web
ML
Pro
cess
mod
eli
ng
wit
hin
B
EA
Web
Lo
gic
(co
nt'
d)
Code
writing s
till
required
to "
fill
in"
each
act
ivity
in t
he
Web
applic
atio
n
22
Web
ML
Evalu
ati
on
BEA W
ebLo
gic
Work
shop:
inte
gra
ting laye
r on
top o
f m
any
oth
ers
Work
flow
engin
eD
ata
inte
gra
tion
lay
erTra
nsa
ctio
n m
anag
er..
.
Ben
efits
from
a h
igh-l
evel
work
flow
model
ing
Inte
rnal
s of
indiv
idual
act
ivitie
s st
ill h
ave
to b
e co
ded
by
hand
23
Web
ML
IIM
od
el-
dri
ven
desi
gn
o
f w
eb
ap
plica
tio
ns
24
Web
ML
Ad
van
tag
es
of
am
od
el-
dri
ven
ap
pro
ach
Mod
el =
tec
hnol
ogy-
indep
enden
t re
pre
senta
tion
of a
solu
tion
The
use
of hig
h lev
el m
odel
s:Red
uce
s dev
elopm
ent
effo
rts
(cost
and t
ime)
Allo
ws
a m
ore
str
uct
ure
d d
evel
opm
ent
pro
cess
Produce
s m
ore
usa
ble
and c
oher
ent
applic
atio
ns
Ensu
res
bet
ter
qual
ity
docu
men
tation
Gra
nts
im
med
iate
and low
-cost
pro
toty
pin
g t
hro
ugh
auto
mat
ic c
ode
gen
erat
ion
Succ
essf
ul ex
ample
s in
oth
er fie
lds:
ER f
or
data
des
ign
UM
L fo
r O
OA&
D
VLS
I des
ign
CAD
sys
tem
s in
the m
anufa
cturi
ng indust
ry
25
Web
ML
Co
nce
ptu
al m
od
elin
g o
f W
eb
ap
plica
tio
ns:
fo
cus
Dat
a D
esig
n:
The
applic
atio
n c
onte
nt
shou
ld b
e m
odel
ed in a
pla
tfor
m-i
ndep
enden
t w
ay,
then
mapped
to
diffe
rent
logic
al m
odel
s
Hyp
erte
xt D
esig
nH
yper
text
ual
inte
rfac
es (
conte
nt+
navi
gation
) sh
ould
als
o be
model
led a
t hig
h lev
el in a
pla
tfor
m-i
ndep
enden
t w
ay,
then
mapped
to
phys
ical st
ruct
ure
s (p
age
tem
pla
tes,
data
ext
ract
ion c
om
pon
ents
, ..
).
26
Web
ML
Develo
pm
en
t p
roce
ss
Bus
ines
s R
equi
rem
ents
HYP
ERTE
XT D
ESIG
N
DAT
A D
ESIG
N
ARC
HIT
ECTU
RE
DES
IGN
REQ
UIR
EMEN
TS S
PEC
IFIC
ATIO
N
IMPL
EMEN
TATI
ON
TEST
ING
& E
VALU
ATIO
N
MA
INTA
INAN
CE
& E
VOLU
TIO
N
27
Web
ML
Fro
m m
od
el-
dri
ven
desi
gn
to
co
de g
en
era
tio
n
3. G
ener
ate
Page
tem
plat
esC
od
eG
en
era
tor
Data
base
m
ap
per
cont
ent
Lega
cy
cont
ent
2. M
ap
1. S
peci
fy
appl
icat
ion
mod
el
Mo
del
Desi
gn
er
4. D
eplo
y
5. R
un
An
y
com
merc
ial
pla
tfo
rm
28
Web
ML
Web
Mo
delin
g L
an
gu
ag
e
(Web
ML)
Web
ML:
a c
once
ptu
al la
nguage
for
hig
h-l
evel
des
ign o
f data
-inte
nsi
ve W
eb a
pplic
ation
sD
efin
ed in 1
998,
in u
se for
mor
e th
an s
ix
year
sAdop
ted in m
any
univ
ersi
ties
wor
ldw
ide
Com
mer
cial
ly im
ple
men
ted
(ww
w.w
ebra
tio.
com
)W
idel
y publis
hed
: Cer
i, F
rate
rnal
i at
al. D
esig
nin
g
dat
a-in
tensi
ve W
eb a
pplic
atio
ns,
Morg
an K
auffm
an,
Dec
. 2002
Use
d f
or d
evel
opin
g s
ever
al a
pplic
atio
ns:
w
ww
.ace
r-eu
ro.c
om,
ww
w.a
cera
dva
nta
ge.
com
, w
ww
.pat
tich
iari.it,
w
ww
.ele
t.pol
imi.it,
ww
w.b
itsy
stem
s.it,
ww
w.im
age.
co.u
k,..
.
29
Web
ML
Hyp
ert
ext
con
cep
tual
mo
dell
ing
wit
hW
eb
ML
Vis
ual
Web
applic
atio
nm
odel
ing lan
guag
eW
ebM
L sp
ecific
atio
ns
consi
sts
of:
One
dat
a sc
hem
a(E
-R,
UM
L cl
ass
dia
gra
ms)
One
or
more
hyp
erte
xt s
chem
as
(site
view
s)Pre
senta
tion
is
dea
lt w
ith a
rule
-base
d a
ppro
ach u
sing a
st
andard
lan
guage
(XSL)
stru
ctur
e
entities
,re
lation
ship
s
navi
gatio
n +
com
posi
tion
units,
pag
es,
links
, si
te v
iew
s
pres
enta
tion
styl
eru
les
30
Web
ML
II.1
Data
Mo
dellin
g
31
Web
ML
Data
mo
delin
g
con
cep
ts
Entity
:a
clas
s of obje
cts
in t
he
applic
atio
n d
om
ain
Att
ribute
:a
pro
per
ty o
f an
entity
Rel
atio
nsh
ip:
a bin
ary
connec
tion
bet
wee
n e
ntities
(w
ith c
ardin
ality
const
rain
ts)
IS-A
hie
rarc
hy:
use
d for
clas
sifica
tion
and g
roupin
g (
not
use
d in t
he
tuto
rial
)
32
Web
ML
Sim
ple
ban
k a
pp
lica
tio
n
data
sch
em
a
Gro
up
Nam
e
Tra
nsa
ctio
nD
ate
Am
ount
Typ
e
Loan
Typ
eIn
tere
st
Applic
atio
nD
ate
Nam
ePas
swd
Use
r
Em
ail
Typ
e
SiteV
iew
CliT
oAcc
ount
Acc
ToT
ransa
c
ApplT
oLoa
n
Use
rToA
ppl
Use
rToG
roup
Gro
upToS
iteV
iew
0:n
1:n
0:n
Acc
ount
Num
ber
Typ
eBal
ance
1:1
0:1
1:1
1:1
0:n
1:1
0:n
0:n
Em
pToAcc
ount 0:1
0:n
1:1
33
Web
ML
II.2
Hyp
ert
ext
Mo
dell
ing
34
Web
ML
Hyp
ert
ext
Mo
deli
ng
:p
urp
ose
Hig
h-l
evel
model
ing o
f:The
front-
end o
f a d
ynam
ic W
ebap
plic
atio
nIn
tera
ctio
ns
with t
he
bac
k en
d,
busi
ness
logic
, an
d d
ata
Usi
ng a
sim
ple
, ye
t fo
rmal
, vi
sual
nota
tion
Enab
ling a
uto
matic
gen
erat
ion o
f:Spec
ific
ation c
orr
ectn
ess
report
sD
ynam
ic p
age
tem
pla
tes
and
Dat
a ac
cess
and m
anip
ula
tion q
uer
ies
Docu
men
tation
35
Web
ML
Co
nte
nt
Un
its
A c
onte
nt
unit
is t
he
atom
ic info
rmation p
ublis
hin
g
elem
ent
It is
a “
view
” def
ined u
pon a
conta
iner
of obje
cts
incl
udin
g:
All
the
inst
ance
s of an
entity
(if se
lect
or
is m
issi
ng)
Only
the
inst
ance
s of an
entity
that
mee
t a
sele
ctio
n
conditio
n (
if se
lect
or is
spec
ifie
d)
Als
o u
sed t
o d
enote
input
form
s
unitX
cont
aine
r[s
elec
tor]
36
Web
ML
DAT
AUN
ITIN
DEX
UN
ITM
ULT
IDAT
AUN
IT
ENTR
YUN
ITSC
RO
LLER
UN
IT
entit
y[S
elec
tor]
Cont
ent:
•in
stan
ces
of
an e
ntity
Basi
c co
nte
nt
un
its
entit
y[S
elec
tor]
entit
y[S
elec
tor]
entit
y[S
elec
tor]
MU
LTIC
HO
ICE
entit
y[S
elec
tor]
Sele
ctor
:•
set
of
cond
ition
s
HIE
RAR
CH
ICAL
entit
y[S
elec
tor]
37
Web
ML
Inse
rt Y
our D
ata
•Fna
me
•Lna
me
Auth
or
first
nam
e:XX
Xla
st n
ame:
YYY
phot
o:
Inde
x of
Aut
hors
•Tho
mas
Man
n•G
unth
erG
rass
•Ger
dW
eiku
m
All A
utho
rs
DAT
AUN
ITIN
DEX
UN
ITM
ULT
IDAT
AUN
IT
ENTR
YUN
ITSC
RO
LLER
UN
IT
Basi
c co
nte
nt
un
its
Bro
wse
Aut
hors
5/12
: go
to1/
12
MU
LTIC
HO
ICE
Cho
ose
Auth
ors
Man
nG
rass
Wei
kum
HIE
RAR
CH
ICAL
1. W
eb A
pplic
at.
Cer
iFr
ater
nali
2. S
yste
ms
Tann
enba
um
Boo
ks&
Auth
ors
38
Web
ML
Un
it in
pu
t an
d o
utp
ut
Eac
h u
nit e
xpos
es input
and o
utp
ut
para
met
ers
Input
is r
equir
ed t
o co
mpute
the
unit
itse
lfPara
met
ers
pre
-def
ined
for
the
unit +
Oth
er p
ara
met
ers
requir
ed b
y th
e se
lect
or
of t
he
unit
Outp
ut
can b
e use
d t
o c
ompute
oth
er u
nit(s
)dep
endin
g o
n t
he
curr
ent
unit
unitX
entit
y[s
elec
tor(
par 1
, ..,
parN
)]
INO
UT
39
Web
ML
Co
nte
xtu
al lin
ks
Auth
orAuth
or
sour
ce u
nit
targ
et u
nit
p1
A c
onte
xtual
lin
kis
an o
rien
ted c
onnec
tion b
etw
een t
wo
units
(sourc
e unit a
nd t
arget
unit),
poss
ibly
ren
der
ed b
y m
eans
of in
tera
ctiv
e an
chors
or
subm
it b
utt
ons
Purp
ose
of a
conte
xtual
lin
k:
Allo
win
g t
he
use
r to
move
fro
m o
ne
pla
ce t
o a
noth
erTra
nsp
ort
ing info
rmat
ion fro
m o
ne
pla
ce t
o a
noth
er
(in t
he
form
of
link
para
met
ers)
May
act
ivat
e a c
om
puta
tion (
see
late
r)
40
Web
ML Au
thor
first
nam
e:Ja
mes
last
nam
e:Jo
yce
phot
o:
Auth
or[O
ID=
p1]
Book
[aut
hor2
book
(p2)
]Bo
ok[O
ID=
p3]
Book
Title
:Uly
sses
Pric
e:23
$C
over
:
Whic
h a
uth
or’s
books
?W
hic
h b
ook?
Boo
ks o
f J.J
oyce
•Uly
sses
•The
Dub
liner
s•P
ortr
ait..
.
Exam
ple
of
lin
ks
p1p2
p3
41
Web
ML
Defa
ult
lin
k a
nd
sele
cto
r p
ara
mete
rs
Auth
or[B
ookT
oAuth
or]
Boo
k
When
ever
poss
ible
, lin
k an
d s
elec
tor
par
amet
ers
are
infe
rred
fro
m t
he
dia
gra
m a
nd n
eed n
ot
be
explic
itly
spec
ifie
d
Dia
gra
ms
bec
om
e si
mple
r an
d m
ore
rea
dab
leExa
mple
:
42
Web
ML
Tra
nsp
ort
lin
ks
Auth
orBoo
k[A
uth
or2Boo
k]
sour
ce u
nit
targ
et u
nit
A t
ransp
ort
lin
k has
a d
efau
lt c
onte
xt t
hat
is
pas
sed
to t
he
targ
et u
nit im
med
iate
ly a
fter
the
dis
pla
y of
the
sourc
e unit,
without
the
use
r in
terv
ention
The
use
r ca
nnot
chan
ge
the
def
ault c
onte
xt a
nd
ther
efore
the
link
is n
ot
render
ed w
ith a
n a
nch
or
The
sole
purp
ose
of
the
link
is d
enoting t
he
nee
ded
par
amet
er p
assi
ng
43
Web
ML
Data
an
d m
ult
idata
un
its
Dat
a units
Publis
h info
rmat
ion a
bou
t one
single
inst
ance
one
single
inst
ance
Multid
ata
units
Pres
ent
multip
le inst
ance
s of an
entity
(se
t of
obje
cts)
(set
of
obje
cts)
Entit
y[s
elec
tor(
para
ms)
]
para
ms
OID
Entit
y[S
elec
tor(
para
ms)
]
para
ms
{OID
s}
44
Web
ML
Ind
ex a
nd
scr
oll
er
un
it
Index
unit:
Publis
h a
n index
of el
emen
ts (
set
of o
bje
cts)
(s
et o
f ob
ject
s)
Outp
ut
para
met
er:
OID
of th
e obje
ct s
elec
ted
by
the
use
r
Scr
olle
r unit:
For
bro
wsi
ng a
set
(blo
ck)
of
obje
cts
Outp
ut
para
met
er:
the
set
of
OID
s(p
ossi
bly
1)
of t
he
curr
ent
blo
ck o
f obje
cts
Entit
y[S
elec
tor(
para
ms)
]
para
ms
sele
cted
OID
Entit
y[S
elec
tor(
para
ms)
]
para
ms
{sel
OID
s}
45
Web
ML
En
try u
nit
Unit for
des
crib
ing input
form
s th
at a
llow
info
rmat
ion
subm
issi
on b
y th
e use
rConsi
sts
of one
or
mor
e fiel
ds,
whic
h c
an b
e:Editab
le/n
on e
ditab
le/h
idden
Pre
load
ed w
ith o
ne/
man
y va
lues
, st
atic
ally
or
dyn
am
ical
lyD
ata
ente
red b
y th
e use
r are
supplie
d t
o ot
her
units
via
outg
oing
links
(nor
mal
ly o
ne)
Typ
ical
ly t
ransl
ate
d into
HTM
L usi
ng t
he
<fo
rm>
tag a
nd
the
ass
ocia
ted s
ubm
it b
utt
on
para
ms
46
Web
ML
Mu
ltic
ho
ice a
nd
h
iera
rch
ical
ind
ex U
nit
Multic
hoi
ce u
nits:
Publis
h index
es o
f el
emen
ts (
set
of o
bje
cts)
(s
et o
f ob
ject
s) a
mon
g w
hic
h t
he
use
r to
sel
ect
one
or m
ore
elem
ents
(w
ith c
hec
kbox
es)
Hie
rarc
hic
al units:
Publis
h a
n index
of el
emen
ts,
with e
ntr
ies
organ
ized
hie
rarc
hic
ally
usi
ng e
ntities
con
nec
ted b
y re
lation
ship
s Allo
w t
he
use
r to
sel
ect
one
elem
ent
from
any
leve
l of
the
hie
rarc
hy
Entit
y[S
elec
tor(
para
ms)
]
para
ms
{Sel
OID
s}
para
ms
SelO
ID
Entit
y[S
elec
tor(
para
ms)
]
47
Web
ML
Rati
on
ale
PUBLI
SH
ING
UN
ITS
Dat
a vs
Multid
ata
units:
show
one
vssh
ow
man
yIn
dex
vs
Scr
olle
runits:
choose
a k
eyw
ord
vssc
an a
lis
tIn
dex
var
iants
: In
dex
unit for
choo
sing o
ne
in a
lis
tM
ulti-
choic
e in
dex
unit for
choos
ing m
any
in a
lis
tH
iera
rchic
al index
unit for
choos
ing o
ne
in a
tre
eEN
TRY U
NIT
Provi
des
the
capab
ility
for
dat
a en
try
EXTEN
SIB
ILIT
YD
evel
oper
s ca
n d
efin
e o
ther
units,
spec
ify
thei
r in
put/
outp
ut
sem
antics
and u
se t
hem
in c
onju
nct
ion w
ith t
he
pre
def
ined
ones
48
Web
ML
Desi
gn
by c
om
po
siti
on
:sc
roll
er
+ d
ata
Dis
pla
y one
item
in a
n o
rder
set
and t
hen
ord
erly
sc
roll
thro
ugh t
he
oth
ers
The
bas
e en
tity
is
the
sam
e fo
r th
e sc
rolle
r and t
he
dat
a unit
Artis
tAr
tist
49
Web
ML
Desi
gn
by c
om
po
siti
on
:en
try +
scr
oll
er
+ i
nd
ex
Albu
mAl
bum
[Titl
eco
ntai
nst]
[Yea
r > y
]
t,y
Pagin
g t
he
resu
lt o
f a
sear
ch
50
Web
ML
Hyp
ert
exts
in t
he larg
e:
site
vie
w,
are
a,
pag
e
Sitev
iew
:a
set
of
pag
es a
nd/o
r ar
eas
form
ing a
co
her
ent
view
of th
e si
te.
Multip
le s
ite
view
s ca
n
be
def
ined
on t
he
sam
e dat
a m
odel
for
diffe
rent
use
rs o
r public
ation m
edia
Are
a:a
set
of
logic
ally
hom
ogen
eous
pages
Exa
mple
s: S
ections
of a p
ort
al:
Sport
, M
usi
c, H
iTec
h,
…
Are
as c
an b
e nes
ted,
so t
hat
sub-a
reas
can
be
def
ined
in
side
area
s
Page:
a co
nta
iner
of
one
or
more
pie
ces
of
info
rmation s
how
n t
o t
he
use
r at
the
sam
e tim
e
51
Web
ML
Exam
ple
Cus
tom
er s
ite v
iew
Prod
uct a
rea
Buy
are
a
Hom
e pa
geC
onta
ct
us
Prod
uct
Gro
ups
Prod
uct
Det
ails
Stor
esO
n-lin
eAc
cess
orie
s
52
Web
ML
Pag
es
A p
age
is a
conta
iner
of
one
or
more
pie
ces
of
info
rmation s
how
n t
o t
he
use
r at
the
sam
e tim
eN
esting o
f pages
is
allo
wed
: a p
age
can h
ave
sub-
pag
esThe
use
r bro
wse
s a
site
made
of
pag
es
Logi
nBo
ok In
dex
Cat
alog
53
Web
ML
A n
on c
onte
xtual lin
kis
a lin
k bet
wee
n p
ages
No c
onte
xt (
info
rmation)
is t
ransp
ort
ed
The
use
r ca
n b
row
se f
rom
a p
age
to a
noth
er o
ne
via
an a
nch
or
(e.g
., >
>Boo
ks)
No
n c
on
textu
al lin
ks
Hom
ePag
eBo
ok In
dex
54
Web
ML
Albu
mSe
arch
Mat
chin
g Al
bum
sD
etai
ls
Exam
ple
of
pag
ed
h
yp
ert
ext
Albu
mAl
bum
[Titl
eco
ntai
nst]
[Yea
r > y
]
t,y
Albu
m
55
Web
ML
Ho
me P
ag
e
Eac
h s
itev
iew
must
conta
in a
page
mar
ked a
s “H
om
e”,
serv
ed b
y def
ault w
hen
the
site
vie
w is
acce
ssed
Hom
ePag
eH
Book
Ind
ex
56
Web
ML
Lan
dm
ark
pag
es
Landm
ark
pages
:glo
bally
vis
ible
pages
. The
use
r ca
n jum
p t
o th
em fro
m e
very
wher
ein
the
site
vie
wEquiv
alen
t to
a n
on c
onte
xtual lin
kim
plic
itly
def
ined
fro
m e
very
ot
her
page
in t
he
site
vie
w t
o th
e la
ndm
ark
page
Auth
ors
Book
s
L
Book
Det
ails
Stor
e Pa
ge
Auth
ors
Book
s
Book
Det
ails
Stor
e Pa
ge
57
Web
ML
Sit
e v
iew
s
A s
ite
view
is a
set
of pages
and/o
r ar
eas
form
ing a
co
her
ent
view
of th
e si
te
Multip
le s
ite
view
s ca
n b
e def
ined
on t
he
sam
e dat
a m
odel
Diffe
rent
site
view
s ca
n b
e publis
hed
for
diffe
rent
types
of
use
rs a
nd f
or
diffe
rent
types
of
outp
ut
dev
ices
Site
view
s ca
n b
ePu
blic
: ev
eryo
ne
can e
nte
rPri
vate
: acc
ess
contr
ol w
ith p
ass
wor
d p
rote
ctio
n is
enfo
rced
58
Web
MLB
an
k a
pp
lica
tio
n h
yp
ert
ext
Clie
nt's
view
of
his
acc
ount
and t
ransa
ctio
ns
Tra
nsa
ct
Tra
nsa
ctio
n[A
ccToTra
ns]
TrD
etai
ls
Tra
nsa
ctio
n
Tra
nsa
ctio
ns
pag
eBan
k Applic
atio
n H
om
e Pa
ge
Acc
ount
Acc
Dat
aAcc
ounts
Acc
ount
[CliT
oAcc
ount]
Loan
Apps
Applic
atio
n[U
serT
oApp(C
urr
Use
r)]
Usr
Deta
ils
Use
r[ O
ID=
Curr
Use
r]
LH
59
Web
ML
Oth
er
Ap
pro
ach
es
Ara
neu
s [A
MM
98]
Data
Mod
el =
ER M
odel
, w
ith e
ntities
, re
lation
ship
s,
att
ribute
s, a
nd g
ener
alis
atio
ns.
Entities
can
have
com
ple
x att
ribute
s.Fi
rst
Hyp
erte
xt M
odel
expre
ssin
g n
avig
atio
ns
from
entities
to
entities
alo
ng r
elat
ionsh
ips.
PA
GE-S
CH
EM
ES t
o p
ublis
h m
ultip
le inst
ance
s, a
nd U
NIQ
UE
PAG
E-S
CH
EM
ES t
o p
ublis
h a
giv
en s
ingle
page.
AG
GREG
ATIO
NS a
s co
nce
pts
that
aggre
gate
entities
or
oth
er a
ggre
gat
es;
may
hav
e si
mple
sel
ecto
rsTyp
ical
ly a
ssoci
ates
pag
e-sc
hem
es t
o en
tities
or
to
aggre
gate
s.
UN
ION
NO
DES r
epre
sent
het
erog
eneo
us
entities
.LI
STS r
epre
sent
IND
EXES.
60
Web
ML
Oth
er
Ap
pro
ach
es
STRU
DEL
[FFK
LS98]
Dat
a M
odel
= O
EM
(gra
ph)
Nod
es =
iden
tifier
s or
valu
esN
ame-
label
led lin
ksO
bje
cts
are
gro
uped
in c
olle
ctio
ns.
Colle
ctio
ns
pop
ula
te
repos
itor
ies.
Quer
y La
nguag
e=
Str
uQ
L w
ith a
quer
y part
for
bin
din
g n
odes
and
arcs
and a
const
ruct
ion p
art
for
build
ing n
ew g
raphs
(with p
rim
itiv
es
crea
te,
link
and c
olle
ct).
The
quer
y dec
lara
tive
ly d
efin
es S
ITE G
RAPH
Sw
ith c
onte
nt
whic
h is
eith
er n
ew o
r ex
tract
ed fro
m r
eposi
tori
es.
HTM
L is
pro
duce
d b
y re
nder
ing t
he
site
gra
phs
with g
ener
ic
tem
pla
tes
Sep
ara
tion
of co
nte
nt
and n
avi
gation
fro
m p
rese
nta
tion
Dec
lara
tive
spec
ific
atio
n o
f si
te v
iew
s
61
Web
ML
Oth
er
Ap
pro
ach
es
HD
M [
GPS93]:
one
of t
he
firs
t pro
pos
als
advo
cating h
igh-
leve
l m
odel
ing o
f hyp
erm
edia
applic
atio
ns.
Intr
oduce
s des
ign-i
n-t
he-
small
and d
esig
n-i
n-t
he-
larg
e pri
mitiv
esRM
M [
ISB95]:
pro
pos
es a
mod
elin
g lan
guage
built
upon
th
e Entity
-Rel
atio
nsh
ip m
odel
and a
sev
en-s
teps
appro
ach
to h
yper
med
ia d
esig
n in t
he
traditio
n o
f so
ftw
are
en
gin
eeri
ng.
RM
M a
lso
giv
es g
uid
elin
es for
typ
ical
hyp
erm
edia
des
ign t
ask
s.O
OH
DM
[SR95]:
advo
cate
s th
e use
of
obje
ct-o
rien
tation
to
mod
el a
dva
nce
d n
avi
gation
and inte
rface
fea
ture
s of
hyp
erm
edia
applic
atio
ns.
Man
y ot
her
: Auto
web
, W
eave
, U
WE,
OO
-H,
W2000,
..
62
Web
ML
Ad
van
ced
featu
res:
g
lob
al p
ara
mete
rs
Glo
bal par
am
eter
s m
odel
info
rmation s
tore
d
glo
bal
ly o
r in
the
use
r se
ssio
nA c
onte
xt p
aram
eter
is
def
ined
by:
Nam
eID
D
ura
tion
(U
ser
sess
ion o
r Applic
atio
n)
Valu
e ty
pe:
can
be
eith
er:
An a
tom
ic t
ype
(inte
ger
, s
trin
g,
…)
An E
ntity
(th
e par
amet
er c
an s
tore
an O
ID r
efer
enci
ng a
n
inst
ance
of th
at e
ntity
)
Def
ault v
alu
e [o
ption
al]
63
Web
ML
Set
an
d G
et
Un
its
SET:
allo
ws
one
to s
et t
he
valu
e of a p
ara
met
er
GET:
allo
ws
one
to r
etriev
e th
e va
lue
of a
par
amet
er
Valu
e/O
ID
Para
mN
ame
Valu
e/O
ID
Para
mN
ame
64
Web
MLB
an
k a
pp
lica
tio
n h
yp
ert
ext:
Clie
nt's
view
of re
cent
tran
sact
ion in a
n a
ccount
MyA
cc's
Acc
ount
[Use
rToA
ccou
nt(
Curr
USer
)]
Acc
ount
Acc
ount[
aID
]
Rec
entT
r
Tra
nsa
ctio
n[A
ccToTra
ns]
[Dat
e>=
Crt
Dat
e-30]
[Dat
e<=
Crt
Dat
e]
TrD
etai
ls
Tra
nsa
ctio
n
My
acco
unts
pag
eRec
ent
tran
sact
ions
pag
e
Crt
Dat
e
65
Web
ML
Op
era
tio
n U
nit
s
Thes
e units
model
busi
nes
s ac
tions
(ei
ther
built
-in
in W
ebM
L or
use
r-def
ined
).
Eac
h o
per
atio
n h
as:
input
from
one
or m
ore
inco
min
g lin
ks
(one
is a
nor
mal lin
k, w
hic
h t
rigger
s th
e op
eration
ex
ecution
, th
e ot
her
s are
transp
ort
lin
ks,
whic
h
transp
ort
para
mate
rs)
two
kinds
of o
utp
ut
links
OK
link
if t
he
oper
atio
n c
omple
tes
corr
ectly
KO
link
if t
he
oper
atio
n fai
ls
66
Web
ML
Bu
ilt-
in o
pera
tio
ns
CREATE
DELETE
MODIFY
CONNECT
DISCONNECT
Con
nect
uni
t
Rel
atio
nshi
p
Dis
conn
ect
unit
Rel
atio
nshi
p
Cre
ate
unit
Entit
y
Del
ete
unit
Entit
y
Mod
ify u
nit
Entit
y
67
Web
ML
In/
ou
t fl
ow
: cr
eate
valu
e1→
attri
bute
1
valu
e2 →
attri
bute
2
OID
of
the
new
obj
ect
KO
OKN
othi
ngC
reat
e un
it
Entit
y
68
Web
ML
In/
ou
t fl
ow
: m
od
ify
valu
e2 →
attri
bute
1 va
lue1
→at
tribu
te2
OID
(s) o
f the
m
odifi
ed o
bjec
t(s)
KO
OK
OID
(s) o
f the
ob
ject
(s) n
ot m
odifi
ed
OID
s of
obj
ects
to m
odify
Mod
ify u
nit
Entit
y
69
Web
ML
Ban
k a
pp
lica
tio
n h
yp
ert
ext:
Em
plo
yees
reg
iste
r new
clie
nts
or
new
acc
ounts
New
clie
nt p
age
Clie
nt in
foN
ame
Logi
n
Add
clie
nt
Use
r
New
clie
nt
Use
r
New
acc
ount
pag
e
All c
lient
s
Use
r[ty
pe="
clie
nt"]
Acc'
tinf
oTy
peD
epo.
Add
acc'
t
Acco
unt
Con
nect
CliT
oAcc
ount
Acco
unts
Acco
unt
[CliT
oAcc
ount
]
New
acc
't
Acco
unt
Type
[“cl
ient
”]
ok
ko
okokko
ko
70
Web
ML B
an
k a
pp
lica
tio
n h
yp
ert
ext
Man
ager
s as
sign e
mplo
yees
to m
anag
e ac
counts
All
acc'
ts
Acc
ount
Acc
ount
Acc
ount[
aID
]
Acc
ount
man
ager
ass
ignm
ent
pag
e
All
emp's
Use
r[t
ype=
"Em
plo
yee"
]
Assi
gn
EmpT
oAcc
ount
Chos
enEm
p
Use
r
MgAcc
'ts
Acc
ount
[Em
pToAcc
ount]
Web
ML
II.3
Pers
on
aliza
tio
n
72
Web
ML
Pers
on
aliza
tio
n
Giv
ing t
o e
ach u
ser
a diffe
rent
view
of
the
WEB,
dep
endin
g o
n:
Who
is t
he
use
r W
her
e is
the
use
r W
hic
h d
evic
e is
bei
ng u
sed
When
the
acc
ess
take
s pla
ces
How
the
use
r beh
aves
Sco
pe
of ac
tion:
A d
iffe
rent
site
vie
wD
iffe
rent
item
s of in
form
atio
n o
n a
pag
eA d
iffe
rent
page
layo
ut
73
Web
ML
Mo
delin
g P
ers
on
aliza
tio
nin
Web
ML
Pers
onal
izat
ion h
as t
hre
e fa
cets
:Acc
ess
contr
ol:
login
/log
out
oper
atio
ns
for
use
r re
cognitio
nSite
view
ass
ignem
ent:
base
d o
n t
he
gro
up t
he
use
r bel
ong t
o, s
om
e si
te v
iew
s are
acc
essi
ble
(1 o
r m
ore
site
vie
w p
er G
roup)
Conte
nt
cust
om
izat
ion:
use
r-or
gro
up-d
epen
den
t co
nte
nt
assi
gnm
ent
[Pre
senta
tion c
an b
e cu
stom
ized
as
wel
l, b
ut
we
skip
this
asp
ect]
74
Web
ML
Use
r /
gro
up
mo
del
Eac
h U
ser
can b
elong t
o o
ne
or
more
Gro
ups
(pre
def
ined
entities
in t
he
stru
ctura
l m
odel
)Eac
h u
ser
has
one
def
ault G
roup
Eac
h g
roup h
as o
ne
asso
ciate
d S
itev
iew
MO
DEL
Use
rG
roup
1:N
1:N
1:1
1:N
Site
View1:
N1:
1us
er2g
roup
user
2def
aultG
roup
75
Web
ML
Oth
er
use
r/g
rou
p m
od
els
BEA W
ebLo
gic
: U
sers
Rol
esand O
rgan
izat
ions:
Sev
eral
Use
rs p
artici
pat
e to
a R
ole
A R
ole
is
def
ined
within
the
conte
xt o
f an
org
aniz
atio
n
An O
rgan
izat
ion is
a c
onte
xt w
ithin
whic
h t
he b
ehav
ior
of
seve
ral ro
les
is d
efin
ed
Str
udel
, W
eave
, Ara
neu
s:
"Use
r" m
ay
be
def
ined
as
one
or s
ever
al o
rdin
ary
entity
(c
lass
es);
no
spec
ial m
odel
ing
Per
sonaliz
ation
=para
met
eriz
ing o
f co
nte
nt
extr
act
ion
quer
ies
76
Web
ML
Lo
gin
/ lo
go
ut
Lead
s th
e use
r to
the
hom
e pag
e of
the
site
vie
w
of his
def
ault g
roup
Eac
h s
ecure
d s
ite-
view
should
allo
w u
sers
to
logout
Chan
gin
g R
ole
(i.e.
gro
up)
dyn
am
ically
is
allo
wed
Entr
y U
nit
MO
DEL
Logo
ut
Logi
n
Cha
nge
grou
p
77
Web
ML
Cu
rren
tUse
r an
dC
urr
en
tGro
up
Eac
h W
ebM
L pro
ject
has
tw
o p
redef
ined
glo
bal para
met
ers:
Curr
entU
ser:
the
OID
of
the
curr
ently
logged
Use
rCurr
entG
roup:
the
OID
of th
e G
roup o
f th
e cu
rren
tly
logged
use
rLo
gin
and L
ogout
oper
atio
ns
auto
mat
ical
ly s
et /
unse
t th
ese
two
par
amet
ers
The
para
met
ers
can b
e use
d t
o p
ublis
h indiv
idual/
gro
up c
onte
nt
Syn
tact
ic s
hort
cut:
MO
DEL
Curr
Use
rU
srD
eta
ils
Use
r[ O
ID=
Curr
Use
r]
Usr
Deta
ils
Use
r[ O
ID=
Curr
Use
r]
78
Web
ML
pref
eren
ce
Pag
e p
ers
on
aliza
tio
n
(use
r-le
vel)
user
artic
les
Afte
r lo
gin
Curr
entU
ser
is
iden
tifie
d, t
hus
the
inde
x sh
ows
user
’s
pref
erre
d ar
ticle
s
MO
DEL
user
artic
le[p
refe
renc
e]
Rec
omm
enda
tions
Cur
rent
Use
r
Pers
onal
izat
ion c
an b
e ac
hie
ved w
ith a
ppro
priate
dat
a des
ign
Hyp
erte
xt c
an r
efle
ct s
truct
ure
, and t
hus
pro
vide
per
sonal
izat
ion
79
Web
ML
Rela
ted
wo
rk:
exp
ress
ing
p
ers
on
aliza
tio
n b
y a
ctiv
e r
ule
s Eve
nt:
Use
r’s
login
Acc
ess
to g
iven
pag
e (i
ncl
. hom
e)Con
ditio
n:
Quer
yAct
ion
One-
to-o
ne
del
iver
y of
con
tent/
pre
senta
tion
Sel
ective
tra
ckin
g o
f use
r’s
beh
avi
orAva
ilable
in m
any
pro
duct
s, incl
udin
g D
YN
AM
O A
ND
I.S
ELL
, BRO
AD
VIS
ION
, M
ICRO
SO
FT S
ITE S
ERVER,
IBM
W
EBSPH
ERE
(support
ed b
y des
ign t
ools
and w
izar
ds)
80
Web
ML
Up-
selli
ng(b
ehav
iora
l attr
ibut
es)
Ru
les
sup
port
ed
by
Dyn
am
o a
nd
I-s
ell
When
use
rs m
ake
acce
ss t
o u
ltra
-res
ista
nt
dre
sses
, pro
pose
sport
shoes
Web
ML
II.4
P
ract
ical M
od
ellin
g a
nd
C
od
e G
en
era
tio
n w
ith
W
eb
rati
o
82
Web
ML
Web
Rati
o
1.
A s
imple
yet
expre
ssiv
e W
eb
CASE t
ool
with a
GU
I fo
r dat
a an
d
hyp
erte
xt d
esig
ner
s2.
A X
ML-
XSL
code
gen
erat
or
transf
orm
ing W
ebM
L sp
ecific
atio
ns
(cod
ed in X
ML)
into
dyn
am
ic p
age
tem
pla
tes
3.
An e
xten
dib
leru
ntim
e fr
amew
ork
optim
ised
for
lar
ge
applic
ation
s and a
ccep
ting n
ew
com
pon
ents
(plu
g-i
n a
rchitec
ture
)4.
RAD
funct
ions
for
data
base
cr
eation fro
m E
R,
mod
el c
hec
king,
doc
um
enta
tion
gen
erat
ion
83
Web
ML
Web
rati
o A
rch
itect
ure
Dat
a D
esig
nH
yper
text
Des
ign
Pres
enta
tion
Des
ign
XML
XSL
Dat
a M
appi
ngXM
L
Auto
mat
icco
de g
ener
atio
n
Depl
oym
ent
info
rmat
ion
-
JSP
tem
plat
es
- D
eplo
ymen
t con
fig fi
les
-
Page
& o
pera
tion
actio
ns
- XM
L de
scrip
tors
XML
Dat
aSo
urce
s
Uni
t lib
rary
XSL
styl
e sh
eet
libra
ry
Java
cla
ss li
brar
yTa
g lib
rary
XSL
for X
ML
desc
ripto
rsPr
esen
tatio
nre
finm
ents
(Thi
rd p
arty
tool
s)
JSP
tem
plat
es
HTM
LTh
ird p
arty
pres
enta
tion
tool
s
XX
XX
84
Web
ML
IDE &
mo
dellin
g f
eatu
res
Spec
ific
atio
ns
Editin
gStr
uct
ure
mod
elH
yper
text
model
Conte
nt
Mappin
gPoi
nt
and c
lick
dat
a so
urc
e cr
eation a
nd
mappin
gPr
esen
tation
Impor
t of
HTM
L ex
ample
s Auto
matic
gen
erat
ion
of X
SL
pre
senta
tion
ru
les
Model
editin
g s
upport
Con
sist
ency
chec
ks(b
uilt
-in a
nd/o
r cu
stom
)M
odel
docu
men
tation
Auto
matic
gen
erat
ion
of s
yste
m
doc
um
enta
tion
(W
ebM
LDoc
)
85
Web
ML
Pre
sen
tati
on
desi
gn
XSL
rule
s (u
nit c
ores
)
HTM
L m
ocku
p (p
age
layo
ut)
HTM
L m
ocku
p (u
nit f
ram
e)Pa
ge te
mpl
ate
(JSP
& .N
ET)
86
Web
ML
Pre
sen
tati
on
co
nce
pts
Easy
Sty
ler:
a t
ool fo
r tr
ansf
orm
ing H
TM
L m
ocku
ps
into
XSL
tem
pla
te g
ener
ator
sPa
ge
layo
ut
An a
nnota
ted H
TM
L file
that
spec
ifie
s th
e ove
rall
layo
ut
of
the
page
Can
conta
in a
ny
med
ia o
bje
ct (
e.g.,
FLA
SH
movi
es)
Unit fra
me
An H
TM
L file
that
spec
ifie
s th
e boundin
g b
ox
of units
Can
conta
in a
ny
med
ia o
bje
ct
XSL
unit c
ore
Sim
ple
XSL
rule
s sp
ecifyi
ng h
ow
to r
ender
the
conte
nt
of
units
Thes
e in
puts
are
auto
matica
lly t
ransf
orm
ed into
XSL
shee
ts t
hat
transf
orm
Web
ML
spec
ific
ation
s in
to p
age
tem
pla
tes
with t
he
des
ired
loo
k&fe
el
87
Web
ML
Web
Ratio
SD
S g
ener
ates
fully
oper
ation
al dyn
amic
Web
sites
w
ithou
t an
y pro
gra
mm
ing
The
gen
erat
ed c
ode
incl
udes
:SQ
L quer
ies
Ser
ver-
side
scri
pting
HTM
L m
ark
-up
Suppor
ted p
latf
orm
sAny
JSP 1
.1co
mplia
nt
applic
ation
ser
ver
(incl
udin
g T
omca
t,
Res
in,
Ori
on,
BEA W
ebLo
gic
, O
racl
e 9iA
S,
IBM
Web
Spher
e)Any
JDBC
com
plia
nt
rela
tion
al d
ata
sou
rce
(incl
udin
g O
racl
e,
Mic
roso
ft S
QL
Ser
ver,
Acc
ess,
MyS
QL,
Post
gre
s)ASP.N
ET /
AD
O.N
ET
applic
ation
sIn
tegra
tion
of W
eb S
ervi
ces
(dep
loym
ent
and c
onsu
mption
)
Co
de g
en
era
tio
n
88
Web
ML
Ru
nti
me f
ram
ew
ork
Mod
ula
r arc
hitec
ture
, base
d o
n
inte
rnat
ional
sta
ndar
ds
(MVC)
Busi
nes
s co
mponen
ts
optim
ized
for
lar
ge
applic
atio
ns
Full
suppor
t fo
r add-o
n c
ust
om
com
pon
ents
Ava
ilable
for
J2EE a
nd
.NET
pla
tfor
ms
Busi
ness
tier
Pres
enta
tion
tier
Serv
ice
man
ager
Pres
enta
tion
serv
ices
(s
ervl
et, s
crip
let,
serv
er-
side
tag)
Com
pone
nts
Com
pone
nt
fact
ory
XML
desc
ripto
rs
Web
ML
II.5
Inte
gra
tin
g b
usi
ness
p
roce
sses
into
Web
ap
plica
tio
ns
90
Web
ML
Sam
ple
bu
sin
ess
pro
cess
Ban
k cl
ients
req
uire
a lo
an f
rom
the
bank
Supply
per
sonal in
form
ation
and d
esir
ed loa
n t
ype
Bank
emplo
yees
must
per
form
tw
o c
hec
ks o
n t
he
loan
applic
atio
nClie
nt's
emplo
ymen
t (s
ala
ry)
his
tory
Clie
nt's
savi
ngs
his
tory
Chec
ks m
ay
be p
erfo
rmed
in p
ara
llel
Ban
k m
anag
ers
use
the
resu
lt o
f ch
ecks
to
dec
ide
loan a
ppro
val or
refu
sal.
91
Web
ML
Mo
deli
ng
pro
cess
es
Man
y w
ork
flow
languag
es/m
odel
sSom
e are
quite
com
ple
x [A
HK+
02]
Com
mon b
asis
: W
ork
flow
Man
agem
ent
Coal
itio
n
[WfM
C]
Use
rsAct
ivitie
sSeq
uen
cePr
e-an
d p
ost-
conditio
ns
AN
D-s
plit
, AN
D-j
oin
OR-s
plit
, O
R-j
oin
Loop
Web
ML
Sam
ple
bu
sin
ess
pro
cess
es
to e
nfo
rce v
ia a
Web
ap
plica
tio
n
Applic
atio
n
Sal
aryC
hec
k
Sav
ingsC
hec
k
Appro
val
Ban
k cl
ient
Ban
k em
plo
yee
Ban
k m
anag
er
AN
Dsp
litAN
Djo
in
Web
ML
Sp
litt
ing
a b
usi
ness
pro
cess
am
on
g s
evera
l cl
ass
es
of
use
rs
Ban
k cl
ient
Ban
k em
plo
yee
Ban
k m
anag
er
Ban
k cl
ient
site
vie
w
Provi
de
•Pe
rsonal
info
•Lo
an info
Ban
k em
plo
yee
site
vie
w
Pick
an a
ppl.,
chec
k sa
lary
OR
Pick
an a
ppl.,
chec
k sa
vings
Ban
k em
plo
yee
site
vie
w
Pick
an a
ppl.,
appro
ve o
rre
fuse
loan
.
Web
ML
Syn
chro
niz
ati
on
req
uir
ed
by
the s
am
ple
bu
sin
ess
pro
cess
Ban
k cl
ient
Ban
k em
plo
yee
Ban
k m
anag
er
Ban
k cl
ient
site
vie
w
Provi
de
•Pe
rsonal
info
•Lo
an info
Ban
k em
plo
yee
site
vie
w
Pick
an a
ppl.,
chec
k sa
lary
OR
Pick
an a
ppl.,
chec
k sa
vings
Ban
k em
plo
yee
site
vie
w
Pick
an a
ppl.,
appro
ve o
rre
fuse
loan
.
Rec
ord
the
new
loan
ap
plic
atio
n.
Web
ML
Syn
chro
niz
ati
on
req
uir
ed
by
the s
am
ple
bu
sin
ess
pro
cess
Ban
k cl
ient
Ban
k em
plo
yee
Ban
k m
anag
er
Ban
k cl
ient
site
vie
w
Provi
de
•Pe
rsonal
info
•Lo
an info
Ban
k em
plo
yee
site
vie
w
Pick
an a
ppl.,
chec
k sa
lary
OR
Pick
an a
ppl.,
chec
k sa
vings
Ban
k em
plo
yee
site
vie
w
Pick
an a
ppl.,
appro
ve o
rre
fuse
loan
.
Only
if:
•
The a
pplic
atio
n h
as n
ot
bee
n a
ppro
ved o
r re
fuse
d•
The s
alar
y has
not
bee
n
chec
ked y
et
Aft
er c
om
ple
tion:
•M
ark
the
applic
atio
n a
s hav
ing c
om
ple
ted t
he
sala
ry c
hec
k
Web
ML
Syn
chro
niz
ati
on
req
uir
ed
by
the s
am
ple
bu
sin
ess
pro
cess
Ban
k cl
ient
Ban
k em
plo
yee
Ban
k m
anag
er
Ban
k cl
ient
site
vie
w
Provi
de
•Pe
rsonal
info
•Lo
an info
Ban
k em
plo
yee
site
vie
w
Pick
an a
ppl.,
chec
k sa
lary
OR
Pick
an a
ppl.,
chec
k sa
vings
Ban
k em
plo
yee
site
vie
w
Pick
an a
ppl.,
appro
ve o
rre
fuse
loan
.O
nly
if:
•
The a
ppl. h
as n
ot
bee
n a
ppro
ved o
r re
fuse
d•
The s
avin
gs
hav
e not
bee
n c
hec
ked y
et
Aft
er c
om
ple
tion:
•M
ark
the
applic
atio
n a
s hav
ing c
om
ple
ted t
he
savi
ngs
chec
k
Web
ML
Syn
chro
niz
ati
on
req
uir
ed
by
the s
am
ple
bu
sin
ess
pro
cess
Ban
k cl
ient
Ban
k em
plo
yee
Ban
k m
anag
er
Ban
k cl
ient
site
vie
w
Provi
de
•Pe
rsonal
info
•Lo
an info
Ban
k em
plo
yee
site
vie
w
Pick
an a
ppl.,
chec
k sa
lary
OR
Pick
an a
ppl.,
chec
k sa
vings
Ban
k em
plo
yee
site
vie
w
Pick
an a
ppl.,
appro
ve o
rre
fuse
loan
.
Only
if:
•The a
pplic
atio
n h
as n
ot
bee
n a
ppro
ved o
r re
fuse
d y
et•
Both
chec
ks a
re c
om
ple
ted
Mar
k th
e final
ap
plic
atio
n s
tatu
s.
98
Web
ML
Hyp
ert
ext
mo
dell
ing
vs
pro
cess
mo
dell
ing
A “
typic
al”
hyp
erte
xt is
set
of
linke
d b
row
ser-
bas
ed n
avi
gab
le inte
rfac
esfo
r ex
plo
ring
info
rmat
ion
Ther
e is
not
a p
redef
ined
seq
uen
ce o
f act
ions
nor
a pro
cess
to b
e fo
llow
edThe
only
pre
ceden
ces
are
due
to d
ata
d
ep
en
den
cies
(to s
ee t
he
item
s of a c
ate
gory
, firs
t se
lect
the
cate
gory
)Ther
e is
not
a d
irec
tion in r
eadin
g t
he
hyp
erte
xt
model
99
Web
ML
Wh
at
is a
“ty
pic
al”
WF
man
ag
em
en
t sy
stem
A s
yste
m for
super
imposi
ng c
on
train
ts(l
ogic
al,
pre
ceden
ce,
tem
pora
l...
) on t
he
per
form
ance
of a
set
of ac
tivi
ties
The
WFM
S ignore
s th
e det
ails
of
the
contr
olle
d
activi
ties
(use
r in
terf
ace,
data
dep
enden
cies
et
c..)
100
Web
ML
Hyp
ert
ext
vs
WF
desi
gn
go
als
Hyp
erte
xt:
pro
vide
rich
nav
igation f
acili
ties
and
info
rmation a
ids
for
lett
ing t
he
use
r “
free
ly
bro
wse
”W
F: p
rovi
de
contr
ol ove
r flow
of act
ivitie
s, a
nd
only
as
little
extr
a in
form
atio
n a
s nec
essa
ry t
o g
o
to t
he
nex
t ta
sk
Ques
tion:
how
to inte
gra
te t
he
two d
esig
n
per
spec
tive
s?
101
Web
ML
WF-d
riven
hyp
ert
ext
(WF+
HT)
An h
yper
text
with “
som
e” c
ontr
aints
on t
he
use
of th
e hyp
erte
xtual in
terf
ace
sCon
stra
ints
are
oth
er t
han t
hos
e due
to d
ata
dep
enden
cies
to
be
pre
serv
ed b
y nav
igat
ion
Const
rain
ts s
pan
the
site
vie
ws
of m
ultip
le a
ctor
s
WF-
driv
en hy
perte
xts
Info
rmat
ion
& n
avig
atio
n ric
hnes
s
Contraints
Pure
hyp
erte
xts
Pure
WFM
S
102
Web
ML
Go
als
of
WF+
HT m
od
elin
g
Ort
hogonal des
ign o
f data
, hyp
erte
xt,
and
const
rain
tsChange
WF
const
rain
t w
ithou
t ch
angin
g d
ata
or
hyp
erte
xt s
chem
a (o
r m
inim
al,
wel
l-del
imited
chan
ge)
Rea
dab
ility
of nota
tion
Sep
ara
te a
s m
uch
as
poss
ible
data
, hyp
erte
xt,
and
const
rain
tsCon
trol
-rel
ated
asp
ects
shou
ld b
e ea
sily
iden
tified
Perm
it c
orre
ctnes
s ch
ecki
ng o
f co
nst
rain
s-re
late
d
featu
res,
lik
e fo
r data
and h
yper
text
(e.
g.,
when
you
dro
p a
n e
ntity
, in
valid
units
are
sig
nalle
d)
103
Web
ML
Mo
deli
ng
WF+
HT u
sin
g
on
ly H
T p
rim
itiv
es
Model
ing c
onst
rain
ed n
avi
gat
ion w
ith t
he
prim
itiv
es f
or
data
-inte
nsi
ve W
eb a
pplic
atio
ns
seen
so far
is
poss
ible
:Exp
loitin
g lin
k to
polo
gy:
a lin
k to
a p
age
for
activi
ty X
is
pro
vided o
nly
in t
he
pag
e in
whic
h
you c
om
ple
te a
ctiv
ity
Y
Exp
loitin
g d
ata
: dat
a item
s ca
n b
e cr
eate
d a
nd
connec
ted t
o u
sers
to m
imic
work
ite
ms
and
work
queu
es,
statu
s flag c
an b
e em
bed
ded
in
obje
cts
to h
ide/
show
the
obje
cts
on w
hic
h u
sers
ca
n w
ork
104
Web
ML
Hyp
ert
ext
To
po
log
y
Exa
mple
: a
wiz
ard
Ste
p 2
can b
e done
only
aft
er s
tep 1
is
com
ple
teSuitab
le for
linea
r, m
ono-a
gen
tw
ork
flow
s (q
ues
tionnaires
, se
lf-r
egis
trat
ion,
etc)
Step
1
En
ter
pers
on
al
data
Step
2
En
ter
com
pan
y
data
Do t
he
appro
pri
ate
busi
nes
s ac
tions
105
Web
ML
Wo
rkfl
ow
-rela
ted
data
Applic
ation d
ata a
re e
xten
ded
with c
ontr
ol data
nec
essa
ry t
o s
tore
the
work
flow
sta
teH
yper
text
pag
es s
how
the
“rig
ht”
dat
a an
d lin
ks
for
each
act
ivity
Nam
ePas
swd
Use
r
Em
ail
Typ
e
Use
rToD
oc
0:n
1:N
Docu
men
t
Num
ber
Title
Sta
tus:
{new
, ed
ited
, appro
ved,
arch
ived}
Cle
rk’s
hom
e pag
e
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eEdit
Docu
men
t[s
tatu
s=“n
ew"]
Chos
enEm
p
Docu
men
t
ToB
eArc
h
Docu
men
t[s
tatu
s=“a
ppro
ved"]
Chos
enEm
p
Docu
men
t
Proce
ss d
oc,
ch
ange
stat
us,
update
activi
ty
106
Web
ML
Evalu
ati
on
Implic
it w
ork
flow
model
ling u
sing h
yper
text
and
dat
a m
odel
ling is
sim
ple
but:
Vio
late
s se
para
tion
of co
nce
rns
Ham
per
s or
thog
onal
evo
lution
of pro
cess
, dat
a, a
nd
hyp
erte
xt m
odel
(e.
g.,
intr
oduci
ng a
new
act
ivity
withou
t affec
ting t
he
pre
viou
s on
es)
Mak
es t
he
spec
ific
atio
n les
s re
adab
le,
since
the
thre
e as
pec
ts a
re m
ixed
Web
ML
II.5
.2In
teg
rati
ng
bu
sin
ess
p
roce
sses
into
Web
ap
plica
tio
ns:
Exp
lici
t p
roce
ss c
on
tro
l u
sin
g a
wo
rkfl
ow
meta
-m
od
el
108
Web
ML
Pro
cess
man
ag
em
en
t u
sin
g a
w
ork
flo
w m
eta
-mo
del
The
idea
: en
code
a g
ener
ic w
ork
flow
met
a-m
odel
in t
he
Web
applic
ation d
ata m
odel
Cas
e ad
vance
men
t is
rec
ord
ed in inst
ance
s of
the
work
flow
met
a-m
odel
Work
flow
enact
men
t en
capsu
late
d in w
ell
def
ined
oper
atio
ns,
whic
h o
per
ate
on
work
flow
met
a-d
ata
Work
flow
-dep
enden
t applic
ation d
ata
are
cl
early
mar
ked
109
Web
ML
Data
mo
dellin
g +
WF
App
licat
ion
data
mod
el
WF
met
a-m
odel
110
Web
ML
HT m
od
ellin
g +
WF
LL
L
Are
as/P
ages
= inte
rfac
es
for
exec
uting a
ctiv
itie
s+
Ort
hog
onal
“dec
isio
n p
oints
” and (
few
!)W
F-aw
are
units
for
expre
ssin
g c
ontr
ol
111
Web
ML
Wo
rkfl
ow
meta
-mo
del
for
Web
ML
Activ
ityTy
pe
Nam
e
Wor
kflo
wD
ata
Mod
el
Use
r
Gro
up
Activ
ityIn
stan
ce
Stat
usSt
artT
imeS
tam
pEn
dTim
eSta
mp
Cas
e
Stat
usN
ame
Star
tTim
eSta
mp
EndT
imeS
tam
p
0:n
1:1
1:n
1:1
0:N
0:1
Proc
ess
Nam
e1
:11
:n
0:n
1:1
Ass
ign
ed
To
Typ
e
Part
Of
Part
Of
ApplicationData
Use
dB
y
Ass
ign
ed
To
0:n
0:n
0:n
0:n
Use
rToG
rou
p0:n
0:1
Ass
ign
ed
To
W
Sem
antics
of W
:Entity
is
"tra
cked
"Connec
ted t
o ac
tivi
ty inst
ance
s th
at
crea
te a
nd h
andle
it
1:1
0:N
Typ
e
112
Web
ML
Activ
ityTy
peN
ame
Wor
kflo
wD
ata
Mod
el
Use
r
Gro
up
Activ
.Inst
.C
ase
Stat
usN
ame
Star
tTim
eSta
mp
EndT
imeS
tam
p
0:n
0:n
0:n
1:1
1:n
Ass
ign
ed
To
1:1
0:n
1:1
Proc
ess
Nam
e1
:11
:n
0:n
1:n
Ass
ign
ed
To
Type
Part
Of
Part
Of
Appl
ic.
Dat
e
Acc
To
Tra
nsa
c
Ap
plT
oLo
an
Acco
unt
Num
ber
Type
Bala
nce
Loan
Type
Inte
rest
1:1
0:1
Tran
sac
Dat
eAm
ount
Type 1:n
1:1
0:n
1:n
1:1
0:n
0:1
Use
rTo
Ap
pl
Cli
To
Acc
t
Em
pT
oA
cct
0:n
WWSt
atus
Star
tTim
eSta
mp
EndT
imeS
tam
p
Co
mp
lete
data
mo
del fo
r th
e
ban
k lo
an
exam
ple
Site
View
0:n
1:1
0:n
Type
113
Web
MLExte
nd
ing
th
e W
eb
ML h
yp
ert
ext
mo
del
to s
up
po
rt c
on
dit
ion
al
execu
tio
n
If-t
hen
-els
e prim
itiv
e:
Cas
e/sw
itch
pri
mitiv
e:If-th
en-e
lse
true
fals
e
Sw
itch
Conditio
n
Exp
ress
ion
114
Web
ML
Web
ML o
pera
tio
ns
for
exp
lici
t w
ork
flo
w e
nfo
rcem
en
t
Sta
rt/e
nd a
n a
ctiv
ity
Sta
rt/e
nd a
cas
e (m
ay b
e: s
ucc
ess,
or
abort
)
Star
t Act
ivity
Activ
ityN
ame
End
Activ
ity
Activ
ityN
ame
Star
tAct
ivity
Activ
ityN
ame
StartCase
End
Activ
ity
Activ
ityN
ame
EndCase
Thes
e op
erat
ions
are
mac
ros
–e.
g.,
"S
tart
Act
ivity/
Sta
rtCas
e":
Cre
ate
a n
ew C
ase
, co
nnec
t it t
o th
e re
spec
tive
pro
cess
Cre
ate
an A
ctiv
ityI
nst
ance
Con
nec
t it t
o th
e re
spec
tive
Act
ivity
Con
nec
t th
e Act
ivityI
nst
ance
to t
he
resp
ective
cas
e
115
Web
ML
Apply
Chec
kSal
ary
Chec
kSav
ings
Appro
ve
Illu
stra
tio
n in
Web
ML:
loan
ap
plica
tio
n p
roce
ss
Clie
nts
apply
for
a lo
an
Bank
emplo
yees
per
form
tw
o c
hec
ks in p
aralle
l
A m
anager
appro
ves
or
reje
cts
the
applic
ation
116
Web
ML
Hyp
ert
ext
for
“Ap
ply
” act
ivit
y (
clie
nt
site
vie
w)
Clie
nt
site
vie
w
Apply
Chec
kSal
ary
Chec
kSav
ings
Appro
ve
Clie
nt
logi
n pa
geLo
gin
Cre
ate
an A
ppl,
connec
t it t
o t
he
Apply
inst
ance
Clie
nt
hom
e pa
ge
Apply
fo
r lo
an
End
Activ
ity
Appl
y
Star
tAct
ivity
Appl
yStartCase
117
Web
ML
Deta
iled
Hyp
ert
ext
for
“Ap
ply
” act
ivit
y
Clie
nt
logi
n pa
geLo
gin
Clie
nt
hom
e pa
ge
Apply
fo
r lo
an
End
Activ
ity
Appl
y
Star
tAct
ivity
Appl
yStartCase
Appl
icat
ion
page Lo
anD
ata
Sum
Perio
dN
ew a
ppl.
Appl
icat
ion
W
Con
nec
ts t
he
new
ap
plic
atio
nto
the
curr
ent
inst
ance
of
Act
ivity
“Apply
”
Clie
nt
Use
r[O
ID=c
urrU
ser]
Assi
gn
Use
rToA
pp
118
Web
ML
Gen
eri
c h
yp
ert
ext
for
“Sala
ryC
heck
”
Em
plo
yee
site
vie
w
Apply
Chec
kSal
ary
Chec
kSav
ings
Appro
ve
Empl
oyee
logi
n pa
geLo
gin
Chec
k sa
lary
Finis
h c
hec
k if O
KAbort
cas
e if K
O
Empl
oyee
ho
me
page
End
Activ
ity
Che
ckSa
lary
Star
tAct
ivity
Che
ckSa
lary
Ope
nCas
es
Cas
e [r
eady
("C
heck
Sala
ry")
]
End
Activ
ity
Che
ckSa
lary
OK
KO
119
Web
ML
Th
e "
read
y"
pre
dic
ate
Built
-in,
met
a-le
vel pre
dic
ate
over
a C
ase
Imple
men
tation:
logic
al e
xpre
ssio
n o
ver
the
WF
met
a-m
odel
, der
ived
fro
m t
he
Proce
ss m
odel
Ther
e ex
ists
a c
om
ple
ted "
Apply
" ac
tivi
ty inst
ance
for
the
case
an
dth
ere
exis
ts n
o "
Chec
kSal
ary
" ac
tivi
ty inst
ance
for
the
case
Der
ivat
ion o
f "r
eady"
pre
dic
ate
s ca
n b
e done
at c
om
pile
tim
e fr
om
the
pro
cess
model
, w
hic
h m
ay incl
ude u
ser-
spec
ifie
d
pre
conditio
ns
on t
ransi
tions
Ope
nCas
es
Cas
e [r
eady
("C
heck
Sala
ry")
]
Show
only
the
Cas
es f
or
whic
h it
is c
orr
ect
to s
tart
now
th
e act
ivity
"Chec
kSala
ry"
120
Web
ML
Co
mp
lete
hyp
ert
ext
for
Sala
ryC
heck
Em
plo
yee
site
vie
w
Empl
oyee
logi
n pa
geLo
gin
Empl
oyee
ho
me
page
Cas
esO
K
Cas
e [r
eady
("C
heck
Sala
ry")
]
Sala
ry
chec
k pa
ge
All a
cc'ts
Acco
unt
Clie
nt
Use
r[a
ssig
nedT
o]
Acco
unt
Acco
unt
[acc
ID]
Sala
ries
Tran
sact
ion
[type
=“sa
lary
"]
Star
tAct
ivity
Che
ckSa
lary
End
Activ
ity
Che
ckSa
lary
Che
ckR
esAc
cpt
Rej
ect
Mar
kAbo
rt
Appl
icat
ion
Out
com
e:=
"abo
rted-
sala
ry"
End
Activ
ity
Che
ckSa
lary
If-th
en-e
lse“acc
ept”
“Rej
ect”
Cur
rAct
Ins
Activ
ityIn
stan
ce[C
ase=
Cas
eOID
][N
ame=
“App
ly”]
121
Web
ML
Gen
eri
c h
yp
ert
ext
for
“Ap
pro
ve”
act
ivit
y Apply
Chec
kSal
ary
Chec
kSav
ings
Appro
ve
Man
ager
site
view
Man
ager
logi
n pa
geLo
gin
Appro
ve o
rre
ject
loan
ap
plic
atio
n
Man
ager
hom
e pa
ge
End
Activ
ity
Appr
ove
Star
tAct
ivity
Appr
ove
Cas
esO
K
Cas
e [r
eady
("Ap
prov
e")]
122
Web
ML
Co
mp
lete
hyp
ert
ext
for
“Ap
pro
ve”
Appl
icat
ion
appr
oval
pag
e
Star
tAct
ivity
"App
rove
"Ap
plic
atio
n
Appl
icat
ion
[Clie
ntTo
Appl
]
End
Activ
ity
"App
rove
"
Man
ager
logi
n pa
geLo
gin
Man
ager
hom
e pa
geC
ases
OK
Cas
e [r
eady
("Ap
prov
e")]
Mar
kRej
ct
Appl
icat
ion
Out
com
e:=
"rej
ecte
d"M
arkA
pprv
Appl
icat
ion
Out
com
e:=
"app
rove
d"
Rej
ect
Appro
ve
New
Loa
n
Loan
[am
ount
:=ap
pl.a
mou
nt]
[dur
atio
n:=a
ppl.d
urat
ion]
Con
nect
Appl
Loan
Clie
nt
Use
r[a
ssig
nedT
o]
Activ
ity
Activ
ityIn
stan
ce[C
ase=
Cas
eOID
][N
ame=
“App
ly”]
123
Web
MLR
em
ark
s o
n e
xp
lici
tw
ork
flo
w
en
forc
em
en
t
The
poss
ibili
ty t
o e
xplic
itly
enfo
rce
a p
roce
ss
giv
es t
he
follo
win
g a
dva
nta
ges
:Sep
ara
tion
of co
nce
rns
(pro
cess
des
ign v
shyp
erte
xt
des
ign)
Evo
lvabili
ty(c
an m
odify
act
ivitie
s and p
roce
sses
se
par
atel
y)Tra
ckin
g a
nd a
uditin
g (
by
look
ing a
t th
e m
eta-
dat
a)M
ore
visi
bili
ty o
f th
e pro
cess
(th
rough e
xplic
it p
roce
ss
enac
tmen
t op
erat
ions)
Full
gen
eral
ity
(with p
oten
tial
for
auto
matic
gen
erat
ion
of par
ts o
f th
e w
ork
flow
s an
d/o
r co
rrec
tnes
s ch
ecki
ng)
CO
NS:
met
a-dat
a m
anag
emen
t is
an o
verh
ead
124
Web
ML
Case
sele
ctio
n t
hro
ug
h
ap
plica
tio
n d
ata
WfM
Sst
yle:
clie
nts
must
be
awar
e of Cas
es
Rea
dyC
ase:
Fetc
h t
he
case
s th
at a
re r
eady
for
activi
ty C
hec
kSal
ary
Fetc
h t
he
Applic
ation
inst
ance
s th
at
are
con
nec
ted t
o th
e “A
pply
” act
ivity
inst
ance
bel
ongin
g t
o th
e det
ecte
d r
eady
case
s
Empl
oyee
ho
me
page
Cas
esO
K
Cas
e [r
eady
("C
heck
Sala
ry")
]
Empl
oyee
ho
me
page
Pend
ingA
pps
Appl
icat
ion
[rea
dyC
ase
("C
heck
Sala
ry")
]
WM
ay b
e Rep
lace
d w
ith:
125
Web
MLA
dvan
tag
es
of
case
sele
ctio
n
thro
ug
h a
pp
lica
tio
n d
ata
Mos
t natu
ral usi
ng:
Cas
e-is
om
orphic
entities
Act
ivity-
isom
orphic
entities
Tak
es a
dva
nta
ge
of
use
r in
tuitio
n"T
he
form
I h
ave
to fill
""T
he
applic
ation
I h
ave
to
chec
k""T
he
file
on w
hic
h I
am
wor
king“
Hyp
erte
xt is
nor
mal
ly s
imple
r
Exa
mple
: Applic
atio
n is
isom
orphic
to
the
case
Empl
oyee
ho
me
page
Apps
CS
Appl
icat
ion
[rea
dyC
ase
("C
heck
Sala
ry")
]
W
126
Web
ML
Sala
ryC
heck
usi
ng
ap
pli
cati
on
data
Em
plo
yee
site
vie
w
Empl
oyee
logi
n pa
geLo
gin
Empl
oyee
ho
me
page
Apps
CS
Appl
icat
ion
[rea
dyC
ase
("C
heck
Sala
ry")
]
Sala
ry c
heck
pag
e All a
cc'ts
Acco
unt
Clie
nt Use
r[a
pplT
oUse
r]Ac
coun
t
Acco
unt
[acc
ID]
Sala
ries
Tran
sact
ion
[type
="cr
edit"
]
Star
tAct
ivity
Che
ckSa
lary
End
Activ
ity
Che
ckSa
lary
Che
ckR
esAc
cpt
Rej
ect
Mar
kAbo
rt
Appl
icat
ion
Out
com
e:=
"abo
rted-
sala
ry"
End
Activ
ity
Che
ckSa
lary
If-th
en-e
lseok
ko
W
127
Web
ML
Pu
ll v
sP
ush
Sty
les
Up t
o h
ere,
"pull
work
"st
yle:
Rea
dy
and R
eadyC
ase
are
in "
pull
work
"st
yle:
Use
rs c
hoose
thei
r ca
ses,
insp
ecting t
he
pas
t ac
tivi
ties
"Push
"st
yle
also
poss
ible
(push
dat
a and/o
r w
ork
): Push
data
: M
anager
ass
igns
doc
to
Em
plo
yee(
s)Pu
sh w
ork
: M
anag
er a
ssig
ns
Tra
nsl
ate
activi
ty t
o Em
plo
yee(
s)push
data
AN
D/O
R p
ush
work
Insp
ecting t
he
pas
t vs
. pre
paring t
he
futu
re
128
Web
ML
Web
ML U
nit
fo
r P
ush
sty
le
pro
cess
co
ntr
ol
New
Web
ML
unit:
Ass
ign
Cre
ates
a n
ew A
ctiv
ityI
nst
ance
sConnec
ts it
to t
he
curr
ent
case
[Con
nec
ts t
o a
giv
en u
ser]
[Con
nec
ts a
n a
ctiv
ity
inst
ance
pla
ying t
he
role
of a
wor
k item
to t
he
Act
ivityI
nst
ance
,and t
o th
e use
r]
Exa
mple
: docu
men
t tr
ansl
atio
n w
ork
flow
Doc
umen
t1:1
Tra
nsl
.Doc
Em
plo
yee
Wri
te D
oc
Man
ager
Use
r0:N
Assi
gn
ActivityName
User
EntityA
1:1
0:N
Cre
ates
Tra
nsl
ates
129
Web
ML
Man
ag
er
site
vie
w
in P
ush
sty
le
Man
ager
hom
e pa
ge
Cre
ate
doc
End
Activ
ity
Writ
eDoc
Star
tAct
ivity
Writ
eDoc
StartCase
Doc
writ
ing
pa
ge Doc
umen
tTi
tleBo
dy
New
doc
.
Doc
umen
t
W
Man
ager
logi
n pa
geLo
gin
Tran
slat
or
Use
r[ty
pe="
empl
oyee
"]D
ocum
ent
[Use
r=U
serID
][A
ctiv
ity="
Tran
slat
e“]
Assi
gnD
oc A
Assi
gnpa
ge
130
Web
ML
Em
plo
yee s
ite v
iew
in
Pu
sh s
tyle
Empl
oyee
hom
e pa
ge
End
Activ
ity
Tran
slD
oc
Doc
umen
t tra
nsla
tion
page
Tran
slat
eTi
tleBo
dy
Stor
eTra
ns
Doc
umen
t
W
Empl
oyee
logi
n pa
geLo
gin
Doc
um.
Doc
umen
t
Star
tAct
iv.
Tran
slD
oc
No longer
cre
ate
s th
e Act
ivityI
nst
ance
Set
s st
atus=
"Act
ive"
MyD
ocs W
Do
cum
en
t[T
ran
slate
s][r
ead
yC
ase
("T
ran
slate
")]
131
Web
ML
Su
mm
ary
of
Pu
sh s
tyle
Use
ful an
d n
atura
l in
circu
mst
ance
s w
hen
"t
he
futu
re c
an b
e fo
rese
en"
When
it
is p
ossi
ble
to
dec
ide:
Whic
h a
ctiv
ity
will
tak
e pla
ce n
ext
Who w
ill p
erfo
rm it
When
obje
cts
exis
t w
hose
lifet
ime
coin
cides
with t
he
lifet
ime
of th
e ac
tivi
ty inst
ance
or
of th
e ca
se
Not
alw
ays
poss
ible
When
wor
kflo
w e
volu
tion
dep
ends
on o
ther
use
rs' fu
ture
ch
oic
es (
nex
t ta
sk is
for
the
man
ager
…)
In t
he
abse
nce
of ap
pro
pri
ate
entities
in t
he
dat
a m
odel
Web
ML
II.6
Inte
gra
tin
g W
eb
se
rvic
es
in a
Web
ap
plica
tio
n
133
Web
ML
Web
serv
ices:
para
dig
m
for
inte
ract
ion
Bas
ed o
n X
ML
mes
sages
Sta
ndar
ds:
SO
AP:
XM
L m
essa
gin
gW
SD
L: o
ne-
mes
sage
or
two-m
essa
ges
oper
atio
ns
UD
DI:
"ye
llow
pag
es"
of w
eb s
ervi
ces
WSCL:
sim
ple
model
for
corr
elat
ing m
essa
ges
into
co
nve
rsat
ions
(dia
lect
of SO
AP
and W
SD
L)W
SFL
, W
SCI,
BPE
4W
SL:
Spec
ifyi
ng w
orkf
low
s of
web
ser
vice
sCom
ple
x, s
truct
ure
d c
onve
rsat
ions
Com
ple
x m
essa
ge
corr
elat
ion m
odel
Role
s, t
ransa
ctio
n p
roper
ties
, re
cove
ry,
…
134
Web
ML
Sim
ple
WS
DL in
tera
ctio
ns
Exa
mple
: em
plo
yees
use
a r
emot
e w
eb s
ervi
ce in for
th
e sa
vings
chec
kReq
ues
t m
essa
ge:
clie
nt's
savi
ng
his
tory
and loa
nre
imburs
emen
t per
iod
Res
pon
se m
essa
ge:
max
imum
loa
n a
mou
nt
with a
ccep
table
risk
rat
e
Fina
ncia
l se
rvic
e pr
ovid
er
Web
se
rvic
em
essg
Apply
Chec
kSal
ary
Chec
kSav
ings
Appro
ve
135
Web
ML
WS
DL:
a s
tan
dard
fo
r si
mp
le
inte
ract
ion
s
Cen
tral
com
pon
ent
of a
web
ser
vice
:op
erat
ion =
1 o
r 2 m
essa
ges
Bank
Web
app
licat
ion
Fina
ncia
lSer
v
Max
loan
am
ount
requ
est
Max
loan
am
ount
res
pons
e
One
-way
subs
crip
tion
to fi
nanc
ial n
ews
Stoc
k of
fer s
olic
it
Fina
ncia
l new
s no
tific
atio
n
Stoc
k of
ferr
espo
nse
•N
otifi
catio
n•
One
-way
136
Web
ML
Syn
chro
no
us
vs.
asy
nch
ron
ou
s tw
o-m
ess
ag
e o
pera
tio
ns
Tw
o-m
essa
ge
oper
atio
ns
may
be
use
dSyn
chro
nou
sly:
no
act
ion is
take
n b
etw
een 1
stand 2
nd
msg
Asy
nch
ronou
sly:
act
ion is
per
form
ed b
etw
een t
he
msg
s
Bank
Web
app
licat
ion
Fina
ncia
lSer
v
Max
loan
am
ount
resp
onse
Mak
e ap
poin
tmen
t res
pons
e
Stoc
k of
fer s
olic
itSt
ock
offe
rres
pons
e
Solic
it n
umbe
r of c
lient
s co
nnec
ted
Num
ber o
f clie
nts
conn
ecte
dre
spon
se
Mak
e ap
poin
tmen
t req
uest
Max
loan
am
ount
requ
est
137
Web
ML
Exte
nd
ing
Web
ML t
o s
up
po
rt
inte
ract
ion
s w
ith
web
serv
ices
Hig
h-l
evel
spec
ific
ation
Data
mod
el:
spec
ific
entities
mod
elin
g t
he
resu
lts
of t
he
inte
ract
ion w
ith s
ervi
ces
(e.g
., r
eturn
ed d
ata
)H
yper
text
model
: pri
mitiv
es o
f th
e gra
phic
al lan
guag
e fo
r ex
tensi
ons
for
handlin
g s
ervi
ces
Run-t
ime
support
for
Web
ser
vice
sAbili
ty o
f ex
chan
gin
g m
essa
ges
Suppor
t fo
r co
nve
rsat
ions
Tra
nsf
erri
ng d
ata
bet
wee
n t
he
under
lyin
g d
ata
mod
el
and m
essa
ges
(XM
L)
138
Web
ML
Nam
ePa
ssw
d
Defa
ult
Web
ML d
ata
mo
del fo
r su
pp
ort
ing
Web
serv
ices
OutIn
Con
vers
atio
nIn
stan
ceTi
meS
tam
pSt
atus
Use
r
1:n
PartO
f1:
1
Ope
ratio
nIn
stan
ceTi
meS
tam
p
1:1
1:1
0:1
1:1
1:N
Mes
sage
Type
Nam
eO
utIn
Ope
ratio
nTyp
eO
pNam
ePo
rtNam
eBi
ndin
gSe
rvic
eNam
eN
etw
orkA
ddre
ssTi
meo
ut
1:1
1:1
0:1
0:1
1:1
1:1
0:N
0:N
Con
vTyp
e
1:n
PartO
f
1:1
0:N
0:1
Nam
eTi
meo
utFo
rmat Mes
sage
Inst
ance
Con
tent
Gro
up 0:n
1:1
Parti
cipa
tes
1:1
139
Web
ML
New
Web
ML c
on
stru
cts
for
sup
po
rtin
g W
eb
serv
ices
One
Web
ML
oper
atio
n for
eac
h u
sage
of W
eb s
ervi
ce
oper
atio
ns
Mar
ks for
oper
atio
ns
that
sta
rt,
resp
. en
d c
onve
rsat
ions
Req
Rsp
Con
vNam
e
SollR
sp
Con
vNam
e
One
W
Con
vNam
e
Not
if
Con
vNam
e
AsSo
lRsp
Con
vNam
e
AsR
eqR
sp
Con
vNam
e
Req
Rsp
Con
vNam
e
Req
Rsp
Con
vNam
e
140
Web
ML
Web
ML s
erv
ice o
pera
tio
ns
are
m
acr
os
1.
Cre
ate
a new
Conve
rsat
ion inst
ance
2.
Cre
ate
a new
Oper
atio
n inst
ance
Nam
e="g
etM
ax"
, …
3.
Con
nec
t th
e O
per
ation
to
the
Con
v. I
nst
ance
4.
Com
pos
e para
met
ers
on t
he
inco
min
g lin
ks into
XM
L m
essg
5.
Sen
d X
ML
mes
sg;
blo
ck w
aitin
g for
answ
er6.
Dec
om
pose
XM
L an
swer
may
invo
lve
man
ipula
ting t
he
under
lyin
g d
ata
7.
Exp
ort
sele
cted
ite
ms
from
the
answ
er a
s par
am
eter
s of
the
outg
oing lin
ks
Che
ckM
axC
onv
getM
ax
141
Web
ML
Th
e s
avin
gs
check
usi
ng
a W
eb
se
rvic
e:
data
mo
del
Sce
nar
io:
the
Get
Max
Am
ount
WS is
calle
d
Req
ues
t: s
avin
gs
his
tory
of
the
applic
ant
From
the
Res
pon
se,
an E
stim
ate
inst
ance
is
extr
acte
d
Gro
up
Tra
nsa
c
Loan
Appl.
Use
r
SiteV
iew
0:n
1:n
0:n
Acc
ount
1:1
0:1
1:1
1:1
0:n 1:1
0:n
0:n
0:1
0:n
1:1
Est
imat
e
Max
Amou
ntC
ondi
tions
142
Web
ML
Hyp
ert
ext
for
the s
avin
gs
check
u
sin
g a
Web
serv
ice
Savi
ngs
chec
k pa
ge
Rat
e ap
pl.
Rat
ing
Con
fid.Clie
nt
Use
r[a
pplT
oUse
r]
Max
Amou
nt
Estimate
Get
Max
Amou
nt
getM
ax
Em
plo
yee
site
vie
w
Rat
ing
Appl
icat
ion
Sav
ings
Acc
ount
[cliT
oAcc
ount]
[typ
e=
"savin
gs"
]
Appl
Appl
icat
ion
End
Activ
ity
Che
ckSa
ving
s
Empl
oyee
ho
me
page
Apps
CS
Appl
icat
ion
[rea
dyC
ase
("C
heck
Savi
ngs"
)]
Star
tAct
ivity
Che
ckSa
ving
sW
Req
uir
esco
nve
rsio
nto
XM
L
Req
uir
esco
nve
rsio
nfr
om
XM
L
143
Web
ML
Vari
an
t: a
uto
mati
c ch
eck
resu
lt
usi
ng
th
e W
eb
serv
ice r
esp
on
se
Savi
ngs
chec
k pa
ge
Clie
nt
Use
r[a
pplT
oUse
r]
Get
Max
Amou
nt
getM
ax
Rat
ing
Appl
icat
ion
<sav
ings
:=tru
e>R
atin
g
Appl
icat
ion
<sav
ings
:=fa
lse>
Appl
Appl
icat
ion
[aID
]
max
Amou
nt>
reqA
mou
nt
ClS
avin
gs
Acc
ount
[cliT
oAcc
ount]
[typ
e=
"savin
gs"
]
okko
End
Activ
ity
Che
ckSa
ving
s
End
Activ
ity
Che
ckSa
ving
s
reqA
mou
nt
144
Web
ML
New
s su
bsc
rip
tio
n u
sin
g W
eb
S
erv
ices:
data
mo
del
Sce
nar
io:
Use
rs s
ubsc
ribe
to n
ews
by
calli
ng t
he
one-
way
WS
oper
ation
"Subsc
ribe"
New
s update
s arr
ive
thro
ugh a
not
ific
atio
n W
S
oper
ation
"U
pdate
"D
ata
mod
elin
g:
new
Subsc
ription
and U
pdate
Msg
entities
Gro
up
Tra
nsa
c
Loan
Appl.
Use
r
SiteV
iew
0:n
1:n
0:n
Acc
ount
1:1
0:1
1:1
1:1
0:n 1:1
0:n
0:n
0:1
0:n
1:1
Est
imat
e
Subs
crip
tion
Topi
cSu
bscD
ate
1:n
Subs
Use
r1:
1
Upd
ateM
sg
Title
Body1:
1U
pdSu
bscr
Rea
d1:n
145
Web
ML
Hyp
ert
ext
for
fin
an
cial n
ew
s su
bsc
rip
tio
n u
sin
g W
eb
serv
ices
Clie
nt
site
vie
w Subs
crip
.To
pic
Freq
.
Fina
ncia
l new
ssu
bscr
iptio
n pa
geSu
bscr
ibe
New
sSub
scr
Upd
ates
New
sSub
scr
Clie
nt s
umm
ary
page
Mak
e fin
anci
alne
ws
subs
crip
tion
Rea
d ne
ws
New
sub
s.
Subs
crip
tion
All s
ubsc
.
Subs
crip
tion
Upd
ates
Upd
ateM
sgs[
read
=fal
se]
Rea
d fin
anci
al u
pdat
es
New
upd
.
Upd
ateM
sg
Con
nect
Subs
crip
tion
Mar
k re
ad
Upd
ateM
sg
New
upd
ate
Upd
ateM
sg
New
upd
ate
146
Web
ML
Makin
g a
n a
pp
oin
tmen
t th
rou
gh
a
Web
serv
ice:
data
mo
del
Sce
nari
o: b
ank
clie
nts
req
ues
t appoi
ntm
ents
with a
n
inve
stm
ent
advi
sor
by
calli
ng t
he
asy
nch
ronou
s re
ques
t-re
sponse
WS o
per
atio
n M
akeA
ppoi
ntm
ent
Input:
rea
son for
of th
e re
ques
ting a
n a
ppoin
tmen
t
From
the
outp
ut,
inst
ance
s of th
e Appoin
tmen
ten
tity
are
cr
eate
d
Gro
up
Tra
nsa
c
Loan
Appl.
Use
r
SiteV
iew
0:n
1:n
0:n
Acc
ount
1:1
0:1
1:1
1:1
0:n 1:1
0:n
0:n
0:1
0:n
1:1
Est
imat
e
1:n
1:1
1:1
Upd
ateM
sg
1:n
Subs
crip
tion
Appo
intm
ent
1:n
Advi
sorN
ame
Dat
e
Appt
Use
r
1:1
147
Web
ML
Hyp
ert
ext
for
an
ap
po
intm
en
t re
qu
est
Ban
k cl
ients
req
ues
t ap
poin
tmen
ts w
ith a
n
inve
stm
ent
advi
sor
thro
ugh t
he
bank
applic
ation
App.
Req
.D
ate
Topi
c
Appo
intm
ent
requ
est p
age
Clie
nt s
umm
ary
page
Req
uest
ap
poin
tmen
t
Sche
dule
dap
poin
tmen
ts
AsR
eqR
sp
AppS
ched
ulin
g
Sche
dule
d ap
poin
ts. p
age
New
app
.
Appo
intm
ent
Con
nect
Appt
Use
r
My
appt
s.
Appo
intm
ent[u
ID]
Appo
intm
t.
Appo
intm
ent
148
Web
ML
Web
ap
plica
tio
ns
vs.
W
eb
serv
ices
Web
applic
ations
may
incl
ude
calls
to W
eb
serv
ices
(se
en)
Web
applic
ations
may
als
o im
ple
men
tW
eb
serv
ices
Sce
nar
io:
set
up a
WS for
sala
ry c
hec
ks
Apply
Clie
nt
Appro
ve
Man
ager
Sal
ary
chec
k
Em
plo
yee
Sav
ings
chec
k
Em
plo
yee
Sal
ary
chec
k
Em
plo
yee
Sal
ary
chec
k W
eb s
ervi
ce
149
Web
ML
Web
serv
ices
imp
lem
en
ted
b
y W
eb
ap
plica
tio
ns
Ban
k ap
plic
ation v
iew
poin
t: S
alar
yChec
kis
a W
S
(bla
ck b
ox)
Sal
aryC
hec
kpro
vider
vie
wpoin
t: S
alar
yChec
kis
a
Web
applic
ation d
rive
n b
y Fi
nanci
al e
xper
tsExt
ernal m
sgs.
Apply
Clie
nt
Appro
ve
Man
ager
Sal
ary
chec
k
Em
plo
yee
Sav
ings
chec
k
Em
plo
yee
Sal
ary
chec
k
Em
plo
yee
Sal
ary
chec
k W
eb s
ervi
ce
150
Web
ML
Sala
ryC
heck
imp
lem
en
tati
on
(s
ketc
h)
Sal
aryC
hec
kem
plo
ys e
xper
ts t
o p
erfo
rm s
alar
y ch
ecks
Expe
rt ho
me
page
Perf
orm
che
ck
SalC
heck
Sala
ryC
heck
Sala
ry c
heck
s re
ques
ted
New
req.
Req
uest
Con
nect
Expe
rtToR
eq
Che
cks
Che
ckR
eque
st[!
answ
ered
]
Che
ck R
eq.
Che
ckR
eque
st
Chk
Res
.R
esul
tPe
rsO
p
151
Web
ML
Web
ap
plica
tio
ns
vs.
W
eb
serv
ices
In g
ener
al,
any
unit in W
ebM
L ca
n b
e ex
port
ed
as a
WS r
eq-r
esp
oper
atio
n:
Input:
all
unit p
ara
met
ers
Outp
ut:
def
ault X
ML-
izat
ion
of u
nit c
onte
nts
More
inte
rest
ingly
, co
mple
x fr
agm
ents
of
hyp
erte
xt (
pro
cess
es invo
lvin
g u
sers
, dat
a, W
Ss)
ca
n b
e w
rapped
as
Web
Ser
vice
s an
d e
xport
ed
furt
her
152
Web
ML
Web
Serv
ice C
on
vers
ati
on
s
Sev
eral
Web
Ser
vice
cal
ls r
espondin
g t
o t
he
sam
e ap
plic
ation n
eeds
Exa
mple
s:Tri
p s
ched
ule
: se
quen
ce o
f ca
lls t
o se
vera
l se
rvic
es,
one
for
each
part
of th
e tr
ipSubsc
ription t
o new
s se
rvic
es:
one
subsc
ription,
seve
ral
not
ific
atio
ns
The
work
flow
under
lyin
g a
conve
rsat
ion c
an b
e ar
bitra
rily
com
ple
x Equiv
ale
nt
nam
es:
WS “
core
ogra
phy”
, W
S
“com
posi
tion”
153
Web
ML
BP
EL4
WS
: E
merg
ing
Web
S
erv
ice c
on
vers
ati
on
sta
nd
ard
Prom
oted
by
Mic
roso
ft,
IBM
, an
d B
EA
Bor
n a
s co
nve
rgen
ce o
f pre
viou
s st
andar
ds,
in p
articu
lar:
XLA
NG
(M
icro
soft
), a
blo
ck-s
truc
ture
d la
ngua
ge w
ith
basi
c co
ntro
l flo
w s
truc
ture
s su
ch a
s se
quen
ce,
swit
ch (
for
cond
itio
nal r
outi
ng),
whi
le (
for
loop
ing)
, al
l (f
or p
aral
lel
rout
ing)
, an
d pi
ck (
for
race
con
diti
ons
base
d on
tim
ing
or
exte
rnal
tri
gger
s.)
WSFL
(IB
M)
-al
mos
t id
enti
cal t
o th
e w
orkf
low
lang
uage
use
d by
IBM
’s M
Q S
erie
s W
orkf
low
, al
low
ing n
este
d g
raphs
(but
acyc
lic,
the
only
ite
ration s
upport
ed is
on o
ne
activi
ty w
hic
h is
per
form
ed u
ntil ex
it c
onditio
ns
are
met
.)
Spec
ific
ally
con
cern
ed w
ith W
eb s
ervi
ce c
ompos
itio
n
154
Web
ML
Tw
o s
tyle
s o
f sp
eci
fica
tio
nin
BP
EL4
WS
Blo
ck S
tyle
(XLA
NG
)<s
eque
nce>
activ
ityA
activ
ityB
</se
quen
ce>
Gra
phSt
yle
(WSF
L)<f
low
> <lin
ks> <lin
k na
me=
"L"/>
</lin
ks>
activ
ityA
<sou
rce
linkN
ame=
"L"/>
activ
ityB
<tar
get l
inkN
ame=
"L"/>
</flo
w>
Act
ivitie
s in
BPEL4
WS a
re:
•Bas
ic:
nor
mal
ly t
he
invo
cation
of W
SD
L op
erat
ions
•Str
uct
ure
d:
arb
itra
ry c
omposi
tion
s of
basi
c and
stru
cture
d a
ctiv
itie
s.
155
Web
ML
BP
EL4
WS
an
d W
eb
ML
All s
peci
fica
tion
s in
BPE
L4W
S ca
n be
exp
ress
ed b
y m
eans
of
Web
ML
hype
rtex
ts,
wit
h fe
w li
mit
atio
ns:
all (
for
para
llel r
outi
ng)
laun
chin
g op
erat
ions
in p
aral
lel i
s no
t su
ppor
ted
pick
(fo
r ra
ce c
ondi
tion
s) is
only
sup
port
ed a
s a
user
cho
ice
or
a W
eb s
ervi
ce c
all (
but
not
as a
n in
tern
al m
echa
nism
suc
h as
a
tim
er o
r a
data
base
trig
ger)
Ther
efor
e, W
eb S
ervi
ce c
ompo
siti
on c
an b
e ex
pres
sed
by m
eans
of
wor
kflo
w p
rim
itiv
es
As il
lust
rate
d in
the
tut
oria
l, p
roce
ss c
ontr
ol c
an b
e im
plic
it o
r ex
plic
it
WF
“cas
e” =
WS
“con
vers
atio
n”
156
Web
ML
Su
mm
ary
Exi
stin
g W
eb a
pplic
ation
des
ign m
odel
s in
clude:
Dat
aH
yper
text
sPer
sonal
izat
ion
(Pre
senta
tion
)Ext
ended
the
Web
ML
mod
el t
o co
ver
Work
flow
(pro
cess
des
ign)
Web
ser
vice
usa
ge
and c
ompos
itio
nPara
dig
m for
ext
ensi
on -
min
imal
, ca
refu
lly d
esig
ned
co
nce
pts
:Appro
pri
ate
data
mod
elin
g (
WF
and W
S m
eta-m
odel
s)N
ew p
rim
itiv
es (
WF
and W
S-s
pec
ific
)
157
Web
ML
Refe
ren
ces
[A03]
W.M
.P v
an d
erAal
st.
"Don't g
o w
ith t
he
flow
: W
eb
serv
ice
com
posi
tion s
tandar
ds
expose
d",
IEEE I
nte
lligen
t Sys
tem
s, 1
8(1
):72-7
6,
2003.
[AD
H+
02]
W.M
.P v
an d
erAal
st,
M.D
um
as,
A.H
.M t
erH
ofs
tede
and P
.Wohed
. "P
atte
rn-B
ased
Anal
ysis
of
BPM
L (a
nd W
SCI)
",
tech
nic
al re
port
, Q
uee
nsl
and U
niv
ersi
ty o
f Tec
hnolo
gy,
2002.
[AH
K+
02]
W.M
.P.
van d
erAal
st,
A.H
.M t
erH
ofs
tede,
B.
Kie
pusz
ewsk
ian
d A
.P.
Bar
ros.
"W
ork
flow
Pat
tern
s",
tech
nic
al
report
, Q
uee
nsl
and U
niv
ers
ity
of
Tec
hnolo
gy,
2002
[AM
M+
98]
P.A
tzen
i, G
.Mec
ca,
P.M
eria
ldo.
"Desi
gn a
nd
Mai
nte
nan
ce o
f D
ata-
Inte
nsi
ve W
eb S
ites
", E
DBT 1
998
158
Web
ML
Refe
ren
ces
[BPE]
Busi
nes
s Pro
cess
Ente
rpri
se L
anguage
for
Web
Ser
vic
es.
Ava
ilable
at
ww
w-1
06.ibm
.com
/dev
elo
per
work
s/lib
rary
/ws-
bpel
[Con00]
Jim
Connalle
n.
"Web
Applic
atio
ns
with U
ML"
, Addis
on-W
esle
y, 2
000.
[CFB
+02]
S.C
eri, P
.Fra
tern
ali, A
.Bongio
, M
.Bra
mbill
a,
S.C
omai
, M
.Mat
era.
Morg
an-K
auff
man
n (
Jim
Gra
y's
seri
es),
2002.
Ava
ilable
in I
talia
n (
McG
raw
-Hill
)[F
FKLS
98]
M.F
ernan
dez
, D
.Flo
resc
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Man
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Dat
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Sites
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K.Y
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Mo
re in
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ati
on
…
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w.
ww
w. w
eb
ml
web
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org
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rg
Web
ML
reso
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es (
pap
ers,
manuals
, co
urs
ewar
e,
slid
es,
solv
ed e
xerc
ises
)W
eb m
odel
ing c
om
pen
diu
m &
bib
liogra
phy
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rati
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w.w
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Rat
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w.w
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o/
pag
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.do
dem
o o
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vera
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of W
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ervi
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gra
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