DECISION-MAKING SUPPORT FOR THE CORRECTNESS
OF INPUT DATA AT RUNTIME IN BUSINESS PROCESSES
MAR´IA TERESA G ´OMEZ-L ´OPEZ and RAFAEL M. GASCA ∗
Depa men o de Lenguajes y Sis emas In o m icos, Uni e si y o Se ille
Se ille, Spain
JOSE
´ MIGUEL P´EREZ- ´ALVAREZ †
In ellimen Secu i y
Toma es, Se ille, Spain
In a business p ocess, he in o ma ion ha lows be ween he ac i i ies can be in oduced by hose use s who
in e ac wi h he p ocess. This in oduced in o ma ion could be inco ec due o a lack o knowledge o a
mis ake. Fo his eason and o make he business p ocess execu ion consis en , we p opose a Decision
Suppo Sys em (DSS) o in o m he use abou he possible and co ec alues ha he inpu da a can ake.
The DSS akes in o accoun he business p ocess model and he policy o he company. The policy conce ning
he inpu da a and da a low ha he company manages can be ep esen ed by cons ain s (called Business
Da a Cons ain s). In o de o asce ain all he possible alues o he inpu da a ha pe mi he execu ion o
he p ocess ollowing he de ined goals, he DSS analyses he business p ocess model and he Business Da a
Cons ain s, using he cons ain p og amming pa adigm.
Keywo ds: Business p ocesses; Inpu Da a; Decision-making suppo ; Nume ical ech-niques; P ocess
Ins ance Con o mi y.
1. In oduc ion
A business p ocess consis s o a se o ac i i ies ha a e pe o med in coo dina-
ion wi hin an o ganiza ional and echnical en i onmen 1. In a business p ocess,
he in o ma ion ha lows be ween he ac i i ies can be in oduced by he use s.
Some imes, he use mus decide he alue o in oduce while aking in o accoun
he po en ial ac ions in o de o make he p ocess ins ance co ec . In he business
p ocess scena io, his implies he analysis o all he possible b anches ha can be
execu ed, and he decisions ha can be aken in he u u e. I he decision made is
inco ec , i will a ec o he decisions in he u u e, o i may e en make i impos-
∗{may egomez, gasca}@us.es. www.lsi.us.es/∼qui i
†jmpe ez@in ellimen sec.com, h p://www.in ellimen sec.com/
1
sible o inish he ins ance co ec ly ( ollowing he goals de ined o he company).
The equi emen o decision suppo equen ly a ises when decisions ha e o be
made in complex, unce ain, and/o dynamic en i onmen s 36. Fo his eason, we
p opose a solu ion whe e he decision-making suppo o inpu da a can be in e-
g a ed in o he business p ocess ins ances o in o m he use abou he possible
alues o he inpu a iables, he eby making he ins ance o he business p ocess
consis en . F om he poin o iew o inpu da a alues, he co ec ness o a busi-
ness p ocess is based on he co ec ness o he compliance ules ha desc ibe he
policy o he company; a co ec inpu alue is he e o e he alue o a a iable ha
sa is ies all he ules de ined by he company.
Nume ous s udies p opose a a ie y o axonomies o classi y he de ini ion o
business compliance ules 2, 3, 4, 5, such as a speci ica ion, a policy o a s an-
da dized p ocedu e, ha ep esen a na u al s ep owa ds he inclusion o seman ic
equi emen s be ween business unc ionali y and da a. Howe e i he ela ion be-
ween he alues o he da a low a iables has o be desc ibed, hen Business Da a
Cons ain s (hence o h e e ed o as BDCs) ha e he capaci y o desc ibe Business
Compliance Rules 6. These BDCs a e unde s ood as a ype o business compliance
ules, which ep esen he seman ic ela ion be ween he da a alues ha a e in o-
duced, ead and modi ied du ing he execu ion o he business p ocess ins ances 7.
The use o BDCs in decision-making suppo can be decisi e, since humans mus
o en make decisions abou he inpu da a in a business p ocess ins ance ha may
esul in being inco ec o he wo king o de o he p ocess. I all he po en ial
scena ios in he u u e a e aken in o accoun in he decision-making suppo o
inpu da a, hen la e iden i ica ion o non-con o mi ies o inpu da a wi h espec
o he BDCs can be p e en ed. The e o e, we p opose he use o BDCs o assis in
he decisions o he use s, despi e he complica ion ha he BDCs can be associa ed
wi h one ac i i y, a se o ac i i ies, o o he whole business p ocess, and hence
he assis ance mus in eg a e he model o he business p ocess. The BDCs espond
o he demand o he p ocessing o da a by p o iding mo e seman ic con en in
he business p ocess, which leads o a be e unde s anding o he p ope ies o he
da a used in he business p ocess.
In o de o explain ou p oposal, an example o web se ices selec ion is de-
pic ed in Figu e 1, whe e a e y simple business p ocess model is shown. We ha e
used he BPMN 2.0 no a ion 13 o inpu and ou pu da a. The BDCs a e ep-
esen ed by means o anno a ions in BPMN associa ed o he ac i i ies. In his
p ocess, he alues o da a i ems DE (Da a Enc yp ion) and NR (max Numbe o
Reques s) a e decided and in oduced by a human in o di e en ac i i ies, since
hey a e a iables ha can pa icipa e in a decision-making p ocess. The alue o
TS (Th oughpu Sa u a ion) is de i ed by Selec Se ice Le el, and hence canno be
de ined as an objec i e o he decision-making p ocess. Depending on he alue o
DE (DE≤2 o DE>2), one o he b anches o he p ocess is execu ed (XOR ga eway
o he di e en p o ide s o se ices). An example o inpu da a decision could in-
Selec Se ice
Le el
Selec P o ide
A
Selec P o ide
B
DE<=2
DE>2
Da a
Enc yp ion
(DE)
Th oughpu
Sa u a ion
(TS) Max Numbe o
Reques s (NR)
Op ion 1
Op ion 2
0<=DE<=3
300<=TS<=2000
TS<=400*DE
DE:1..3
(0<=DE<=3
300<=TS<=2000
TS<=400*DE) AND
(DE>=2  NR<=TS
500<=NR<=2000)
OR
(NR<=1.3*TS
600<=NR<=2000)
DE:2..3
NR<=TS
500<=NR<=2000
NR<=1.3*TS
600<=NR<=2000
0<=DE<=3
300<=TS<=2000
TS<=400*DE
Fig. 1. Simple example o Business P ocess Ins ance o decision-making suppo o inpu da a
suppo
ol e he de e mina ion o he possible co ec alues o DE in Selec Se ice Le el
ac i i y. As men ioned abo e, each BDC can be associa ed wi h one ac i i y, a se
o ac i i ies, o o he whole p ocess: he e o e each ac i i y has se e al BDCs asso-
cia ed, o example Selec Se ice Le el is associa ed o {0≤DE≤3, 300≤TS≤2000,
TS≤400*DE}. In o de o decide he co ec alues ha he a iable DE can ake
o sa is y all he BDCs in he u u e, ei he hose BDCs ela ed wi h only Selec
Se ice Le el (Op ion 1 o Figu e 1) can be aken in o accoun , o all he BDCs
ela ed di ec ly o indi ec ly wi h DE. This implies analysing all he possible pa hs
om Selec Se ice Le el, while conside ing he condi ions associa ed wi h he con-
ol lows and e alua ed a un ime (DE in he example), and he BDCs o Selec
P o ide A and Selec P o ide B (Op ion 2 o Figu e 1). Op ion 1 p esen s an
in e al wi h he possible alues o he a iable DE ([1..3]) when solely he BDCs
ela ed o he ac i i y Selec P o ide A a e analysed; and Op ion 2 shows how he
in e al wi h possible co ec alues o DE a e educed ([2..3]) i he BDCs ela ed
o he a iable NR a e also included in he analysis. Fo example, in Op ion 1 i is
possible o in oduce he alue 1 o he a iable DE, and o assign 350 o he a i-
able NR ({TS ≤400 ∗DE}). The p oblem a ises when he ac i i y Selec P o ide
Ais execu ed, and i is disco e ed ha he e a e no alid alues o he a iable
NR o sa is y he BDC {NR ≤TS}.
F om he p e ious example, i can be obse ed ha he BDCs could help o
in oduce consis en da a in business p ocess ins ances. These BDCs cons i u e
he basic knowledge o a Decision Suppo Sys em (DSS). This knowledge can be
en iched by including he analysis o he business p ocess model, and he condi ions
associa ed wi h he sequence lows. This decision-making suppo o inpu da a can
also be used o gua an ee he exis ence o a co ec ins ance o he p ocess, he eby
ob aining a mo e aul - ole an p ocess.
In o de o suppo his assis ance wi h he DSS o inpu da a, he ollowing
aspec s ha e been conside ed:
•The use o Business Da a Cons ain s o help in he decision-
making suppo o inpu da a. BDCs can assis in he decision-making
suppo by epo ing on he possible co ec alues o he da a in oduced
in each ins ance; howe e no all o he alues o he BDC a iables a e ye
known and ins an ia ed.
•The de elopmen o an algo i hm o a e se he p ocess model.
To b ing he ele an pa s o he business p ocess oge he and allow hem
o con ibu e owa ds decision-making suppo , an analysis o he p ocess
model and he BDCs o each ac i i y is necessa y. We p opose an algo i hm
ha a e ses he business p ocess model and combines he BDCs ela ed
o each ac i i y in o de o ob ain a ep esen a ion o he co ec alues
ha he a iables can assume.
•The gene a ion o he Nume ical ep esen a ion o he possible
co ec alues o he inpu da a. To gene a e a se o quali ied solu ion
al e na i es, ins ead o p o iding only one solu ion, we p opose ob aining
he possible anges o he decision a iables by means o Cons ain P o-
g amming.
•The implemen a ion o an applica ion o in eg a e ou p oposal
in o business p ocess modelle so wa e. In o de o acili a e he
decision-making p ocess desc ip ion, we ha e de eloped an applica ion o
he DSS (called MARTIN: MAking Reasoning o daTa INpu ) ha allows
he use o de ine he BDCs o each ac i i y, and connec he symbolic and
nume ical sol e s wi h he p ocess in o de o ob ain he possible co ec
alues o he inpu da a in each ins ance. This applica ion o e s he e-
qui ed agili y and lexibili y o he o ganiza ions so ha hey can comply
wi h changes in policy and legisla ion, and apply hem in he DSS.
Fo hese easons, his pape is o ganized as ollows: Sec ion 2 explains he g am-
ma o BDC used in his pape . Sec ion 3 p esen s a mo i a ing example whe e
decision-making suppo o inpu da a is used. Sec ion 4 s a es he necessa y de -
ini ions o o malize he p oposal. Sec ion 5 analyses how o a e se a business
p ocess model o s udy all he possible co ec alues o a a iable, and o p opose
an algo i hm o i s de elopmen . Once he BDCs in ol ed in he decision-making
suppo a e known, how Cons ain P og amming is applied o Decision-Making
suppo is analysed in Sec ion 6. In Sec ion 7, he de ails o he DSS ha we p o-
pose, he implemen ed applica ion o include BDCs in he decision-making suppo ,
and a case o s udy a e explained. Sec ion 8 discusses p e ious wo k ela ed o ou
p oposal. Finally, conclusions a e d awn and u u e wo k is p esen ed.
2. Rep esen a ion o he Decision-Making Objec i es
As men ioned abo e, BDCs a e a subse o he business compliance ules, o ien ed
o da a low alues. BDCs can be used o ep esen he ela ion be ween he alues o
he a iables ha low in a business p ocess ins ance, by helping in he desc ip ion
o he policy o he company and in he decision-making suppo o inpu da a.
Al hough he e a e se e al language cons uc s o he design o business compli-
ance ules, mos a e based on he use o IF-THEN ules o hei de i ed ex ensions
o ECA ules (e en -condi ion-ac ion ules) o ECAA ules (e en -condi ion-ac ion-
al e na i e ules). The languages o ep esen ing business ules a y be ween e-
sea ch p o o ypes (e.g. N3), endo speci ic o ma s (e.g. D ools, Fai Isaac Blaze
Ad iso , ILOG JRules and Jess), and p oposals o he XML-based exchange o
business ules (e.g. SRML, PRR, and SBVR). Ano he possibili y is he Business
P ocess Compliance Language (BPCL) 8, which de ines inclusion, p ecedence, and
exis ence condi ions o business ules by means o Objec Cons ain Language
(OCL) exp essions, which speci y co ec ness and compliance checks. Tha e sion
o BPCL was imp o ed in 6 o de elop a seman ic app oach o business ule man-
agemen ha allows in ui i e modelling and analysis o business p ocess compliance.
In 7, a g amma o BDCs is p esen ed. In his pape , we ex end his g amma o
include new ope a o s ha can acili a e he desc ip ion o he policy o he com-
panies. This ex ension is inspi ed by he idea o iewing a BDC as a Cons ain : a
Boolean combina ion o equa ions and inequa ions ha ollows he me amodel o
Figu e 2 based on ha p esen ed in 9.
A BDC can be an a omic cons ain , a nega ion o a cons ain , o a bina y
cons ain o med by wo cons ain s joined wi h a Boolean ope a o (AND, OR,
IMPLY). An a omic cons ain is o med by wo unc ions and a compa a o (<, ≤,
≥, . . .). Each unc ion can be a una y unc ion (a a iable), a cons an , o a bina y
unc ion joined wi h an ope a o (+, ∗, −, /).
The me amodel is based on he ecu si e de ini ion o Cons ain , since wo
cons ain s can be combined in o a new one, by using one o he Logic Ope a o s
o a Nega ion. Fo example, he cons ain s: {a+b ≤ c ∧ c > 8} and {a*b ≥ 5 ∨
¬(c ≤ 15)}, can be combined in o a new cons ain , o example by using he ∨
ope a o , he eby ob aining he new cons ain : {(a+b ≤ c ∧ c > 8) ∨ (a*b ≥ 5 ∨
¬(c ≤ 15))}. We he e o e de ine ou me hods o he combina ion o a BDC (c)
wi h ano he (c’):
•c.and(BDC c’) e u ns a new cons ain : {c∧c’}
•c.o (BDC c’) e u ns a new cons ain : {c∨c’}
•c.no () e u ns a new cons ain : {¬ c}
•c.imply(BDC c’) a new cons ain : {c→c’}
These me hods will be used in Algo i hm 5.2 o build he BDC ha will ep esen
all possible alues o a a iable in acco dance wi h he model o he business p ocess.

Cons ain
- domain : Va iableType
- name : S ing
Va iable
- FLOAT
- INTEGER
- NATURAL
<<enum>>
Va iableType
- isDe inedBy
*1
- NOT_EQUAL
- EQUAL
- GREATER_EQUAL
- GREATER
- LESS_EQUAL
- LESS
<<enum>>
Compa a o
- compa a o : Compa a o
A omicCons ain Nega ionCons ain
- booleanOp : LogicOpe a o
Bina yCons ain
- IMPLY
- OR
- AND
<<enum>>
LogicOpe a o
Func ion
- isFo medBy
2
1
Una yFunc ion
- has
1
- alue : S ing
Cons an
- uncOpe a o : Func ionOpe a o
Bina yFunc ion
- DIVIDE
- MULTIPLY
- MINUS
- PLUS
<<enum>>
Func ionOpe a o
- has
2
1
- ela es
2
- nega es
1
Fig. 2. Cons ain Me amodel
3. A mo i a ing example
The mo i a ing example used in his pape is he well-known example o he o ga-
niza ion o a con e ence, o which a educed model is p esen ed in Figu e 3. This
business p ocess shows an example whe e decisions abou inpu da a mus be made
a a ious poin s o he business p ocess, whe e he u u e alues o se e al a i-
ables emain unknown, since hey a e in oduced in o ac i i ies ha ha e ye o be
execu ed. Fi s o all, he o ganizing commi ee has o de e mine he ea ly and la e
egis a ion ees se e al mon hs be o e he numbe o pa icipan s is known, and
his decision canno be changed a e he call o pape s has been made. A simila
si ua ion exis s when deciding he numbe o p oceedings ha will be p in ed, bu
al hough he inal numbe o pa icipan s emains unknown un il he con e ence
ends, he e is a signi ican ela ionship be ween he numbe o accep ed pape s and
he numbe o pa icipan s. O he decisions, such as which es au an o book o
he gala dinne and lunches, ha e o be made al hough he numbe o pa icipan s
can in luence he de e mina ion o he p ice and he choice o es au an . Al hough
his in o ma ion is unknown, i is necessa y o make many decisions be o e he
con e ence s a s. In he example, nine di e en inpu da a a iables pa icipa e in
he da a decision-making p ocess (ea ly egis a ion ee, la e egis a ion ee, lunch
p ice, dinne p ice, numbe o p oceedings o p in , local speake cos , in e na ional
speake cos , publici y and enue cos ). The e a e o he inpu a iables, bu hei
Es ablish
Con e ence
Ra e
Con ac
Pa ne
P in
P oceedings
Final epo
and make
paymen s
In i e
Local
Speake
In i e
In e na ional
Speake
Au ho
No i ica ion
Regsi e
Ea ly
Income- o alCos <4000
/
Book
Dinne
Book
Lunch
Pape
Submission
Sen Pape Accep ed Pape
Num Ea ly
egis a ions
Num La e
Regis a ions
Hold
Con e ence
Sponso ship
AND
1
AND
2
XOR1
OR
1
Ea ly Regis a ion Fees
La e Regis a ion Fees
Venue Cos
publici y
P oceeding P ice
Numbe O P oceedings Con e ence lunch p ice
Con e ence dinne p ice
sponso ship>=5000
sponso ship>=8000
in e Speake Cos
localSpeake Cos
Fig. 3. Example o a con e ence o ganiza ion p ocess
alues a e de e mined in a manda o y way, o example he numbe o accep ed pa-
pe s, o he inal numbe o pa icipan s ha belong as pa o he da a low. Since
decisions ha e o be made by he o ganizing commi ee, wo ques ions a ise: Would
i be mo e conduci e owa ds he success o he con e ence o ake in o accoun all
he po en ial ac s in he u u e, and he possible b anches ha will be execu ed
depending on he speci ic alues o each p ocess ins an ia ion? How i can be done?
Ob iously, i he e is no in o ma ion abou he ela ionship be ween he a i-
ables ea lyRegis a ionFee,la eRegis a ionFee,lunchP ice, e c, no ype o in e ence
abou he possible co ec alues can be aken. Fo his eason, BDCs a e necessa y.
Examples include:
• {sen Pape *0.3 ≤accep edPape s ≤sen Pape *0.8}associa ed wi h he
ac i i y Pape Submission.
• {numEa lyPa icipan *0.8 ≤accep edPape s ≤numEa lyPa icipan *
1.2}associa ed wi h he ac i i y Au ho No i ica ion.
• {numO P oceedings ≥1.1 * numbe O Pa icipan s}associa ed wi h he
ac i i y P in P oceedings.
• {Income*0.80 ≤To alCos ≤Income*0.9}associa ed wi h he ac i i y
Hold Con e ence.
These BDCs, combined wi h he p ocess model and he condi ional sequence
lows p esen ed in Figu e 3, can be applied in o de o asce ain he decision a i-
ables, o example lunchP ice in he ac i i y Book Lunch. All he a iables and
BDCs a e explained in de ail in he Appendix o he pape .
4. Fo maliza ion o he Decision-making suppo o inpu da a in
Business P ocesses
In his sec ion, basic no a ion and concep s a e in oduced and he o malism used
o exp ess ou p oblem is b ie ly desc ibed. A DSS o inpu da a in business p o-
cesses unde akes asks such as an e alua ion o al e na i es, and communica es
i s conclusions by aking in o accoun he BDCs ha mus be consis en in u u e
decisions and in he p ocess model. The e o e, he same model can be in ol ed a
di e en poin s o decision-making acco ding o he decision a iables ha he use
needs o asce ain, and o he speci ic alues ins an ia ed o he a iables o he
da a low. The e o e, wo aspec s a e combined in he decision-making suppo : he
model M(<P,BDC,DF>), o med by ac i i ies and con ol lows, he BDCs associ-
a ed o each ac i i y, and he da a low a iables; and he decision poin DP, o med
by he decision a iables (whose possible alues need o be asce ained) and all he
ins ances o he da a low a iables un il he momen <DV,DFI>. Acco ding o
hese wo desc ip o s, he decision-making suppo can be pe o med by ob aining
all he possible DVI uples o alues o DV ha sa is y DBC and he DFI. A DSS
o he inpu da a o a business p ocess can he e o e be speci ied by means o he
Decision P ocess Model:
De ini ion o Decision P ocess Model: This is o med by means o he
business p ocess model (M), and he decision poin s (DP). Each o hese pa s a e
de ined a he same ime as:
M=<P,BDC,DF>
DP =<DV,DFI>
By using hese wo pa s o he desc ip ion, he decision-making suppo o
inpu da a ob ains he DVI, which is a se o co ec uples o ins an ia ion o DV:
<M,DP>→DVI | ∀ di∈DVI,{BDC ∪DFI ∪ di}`>
The ollowing subsec ions o malize he model, he decision poin s, and speci y
how o ob ain he decision-making suppo in de ail.
4.1. Business P ocess Model (M)
A pa o he Decision P ocess Model is he p ocess model P, and is composed o :
•SE, one s a e en o ini ialize he p ocess.
•EE, a se o end e en s, wi h a leas one elemen .
•A, a se o ac i i ies ha de ines he model o he p ocess.
•CF, a se o con ol low pa e ns (AND, OR, XOR) ha desc ibes he
possible b anches o execu e.
•Cond, a se o condi ions associa ed wi h he con ol lows OR and XOR,
ha desc ibes he pa hs ha he p ocess can ake depending on he alues
o he a iables in he da a low. These condi ions a e e alua ed a un ime,
A1
A2
A3
A4
BDC1
BDC2
BDC3
BDC4
BDC5
BDC6
BDC7
Ac i i ies Business Da a
Cons ain s
Fig. 4. Func ion ela ion be ween Ac i i ies and BDCs
when he alues o he a iables a e known.
The BDCs associa ed o each ac i i y o he p ocess (BDC) a e de ined wi h he
da a low a iables DF; he DF can hen be de ined o a ange o possible alues o
assis in he decision suppo (DFR). The ela ionship be ween ac i i ies and BDCs
is p esen ed in Figu e 4, whe e he e is a su jec i e co espondence be ween he
Ac i i y and he Business Da a Cons ain se s. The impo ance o he in eg a ion o
business ules and business p ocesses was analysed in 33. E e y BDCiin a Business
Da a Cons ain se has a co esponding elemen Ajin he Ac i i y se , such ha
(Aj) = BDCi, whe eby mul iple ac i i ies migh be u ned in o he same BDC by
applying , al hough no all he Ac i i y elemen s mus ha e a ela ion wi h he
elemen s o he Business Da a Cons ain se . The e o e, he BDC is desc ibed by
means o he uple: BDC =<DFR, :A→BDCs>.
The BDCs ha desc ibe he ange o he possible alues o each da a low a i-
able a e assigned o he whole p ocess.
Fo he p ocess example p esen ed in Figu e 3, he componen s o he p ocess
acco ding he de ini ions abo e a e:
•A={Es ablish Con e ence Ra e, Con ac Con ibu ion, Pape submission,
Au ho No i ica ion, ...}.
•CF ={AND1, AND2, XOR1, OR1}.
•Cond o he con ol low pa e n XOR1is {Income- o alCos <4000}, and
o he con ol low pa e n OR1a e {sponso ship ≥5000, sponso ship ≥
8000}.
•BDC is composed o :
–DFR ={5000 ≤ o alCos ≤50000},{5000 ≤income ≤60000},{50
≤numEa lyPa icipan s ≤200}, ...
– :A→BDCs =Es ablish Con e ence Ra e→ {1.2 * ea lyReg-
is a ionFee ≤la eRegis a ionFees ≤1.5 * ea lyRegis a ionFee, ix-
Algo i hm 5.2 Recu si e Algo i hm o a p ocess g aph
1: unc ion G aphT a e sal(G aph g, Node n, Cons ain c)
2: while n is no an END node do
3: i n is an Ac i i y hen
4: i c == new Cons ain () hen
5: c = n.ob ainCons ain ();
6: else
7: c.and(n.ob ainCons ain ());
8: end i
9: n = g.ob ainNeighbou (n);
10: .A single node is e u ned ( o line 3 o P ocess G aph de ini ion).
11: else i n. ype is a Spli Con ol Flow hen
12: Se nodes = g.ob ainNeighbou (n);
13: .Se e al neighbou s a e ob ained, one o each b anch.
14: Node n1 = nodes.ge ();
15: Cons ain c1 = new Cons ain ();
16: A ay A ayNodes[] = new Nodes[nodes.size()];
17: A ayNodes[0] = G aphT a e sal(g, n1, c1);
18: c1.add(g.label(n,n1));
19: in ege i = 1;
20: while nodes.nex () do
21: Node n2 = nodes.ge ();
22: Cons ain c2 = new Cons ain ();
23: A ayNodes[i++] = G aphT a e sal(g, n2, c2);
24: c2.and(g.label(n, n2));
25: i n is an OR o an XOR con ol low hen
26: c1.OR(c2);
27: else
28: c1.AND(c2)
29: end i
30: end while
31: c.and(c1);
32: n = heNodeDis inc O End(A ayNodes);
33: else .Any join con ol low
34: n = g.ob ainNeighbou s(n);
35: .A single node is e u ned ( o line 2 o P ocess G aph de ini ion).
36: e u n n;
37: end i
38: end while
39: e u n n
40: end unc ion
Final epo and make paymen s
Any BDCs de ined o he whole p ocess, and no jus o a speci ic ac i i y,
will be included wi h an AND Boolean ela ion wi h he BDCs ob ained om he
execu ion o he algo i hm. I he e is an ac i i y wi hou any associa ed BDC, i
will be equal o no including a cons ain o including a ue cons ain in he BDC

ob ained.
6. E alua ing he Decision P ocess Model a he Decision Poin s
As men ioned in Sec ion 4, an impo an key o he decision-making suppo o
inpu da a is how o p esen he in o ma ion so ha i is use ul o he use . In
o de o ob ain a nume ical ep esen a ion by means o in e als, we p opose he
use o he Cons ain P og amming pa adigm o assu e ha he decision model
can be e alua ed in an e icien way. This assu ance is hanks o he CSP o mal
ep esen a ion being e y simila o he o mal ep esen a ion o he decision model
p esen ed in his pape . The e o e, we p opose modelling and e alua ing a Con-
s ain Sa is ac ion P oblem wi h he BDC ob ained om Algo i hm 5.2, and o
he ins an ia ed a iables o da a low.
A Cons ain Sa is ac ion P oblem (CSP) ep esen s a easoning amewo k con-
sis ing o a iables, domains and cons ain s. Fo mally, i is de ined as a uple <X,
D,C>, whe e X={x1,x2,. . .,xn}is a ini e se o a iables, D={d(x1), d(x2),
. . .,d(xn)}is a se o domains o he alues o he a iables, and C={C1,C2,
. . .,Cm}is a se o cons ain s. Each cons ain Ciis de ined as a ela ion Ron a
subse o a iables V={xi,xj,. . .,xl}, called he cons ain scope. The ela ion
Rmay be ep esen ed as a subse o he Ca esian p oduc d(xi)×d(xj)×. . . ×
d(xl). A cons ain Ci= (Vi,Ri) simul aneously speci ies he possible alues o he
a iables in Vin o de o sa is y R. Le Vk={xk1,xk2,. . .,xkl}be a subse o X,
and an l- uple (xk1,xk2,. . .,xkl) om d(xk1), d(xk2), . . .,d(xkl) can he e o e be
called an ins an ia ion o he a iables in Vk. An ins an ia ion is a solu ion i and
only i i sa is ies he cons ain s C.
In o de o sol e a CSP, a combina ion o sea ch and consis ency echniques is
commonly used 11. The consis ency echniques emo e inconsis en alues om he
domains o he a iables du ing o be o e he sea ch. Du ing he sea ch, a p opaga-
ion p ocess is execu ed which analyses he combina ion o alues o a iables whe e
he cons ain s a e sa is iable. Se e al local consis ency and op imiza ion echniques
ha e been p oposed as ways o imp o ing he e iciency o sea ch algo i hms.
In a CSP, he inclusion o a cons ain in he se Chas he same e ec as
including his cons ain wi h an ∧ ela ion wi h he se C. Fo his eason, in his
case he CSP will be composed o he a iables o he da a low, bo h ins an ia ed
and non-ins an ia ed, o he BDC ob ained om Algo i hm 5.2, and o he BDCs
de ined o he whole p ocess. The pa s o he CSP acco ding o he de ini ion o
Decision P ocess Model a e he e o e:
•X:DF
•D:DFI
•C:{BDCs de ined o he whole p ocess} ∪ {BDC ob ained om he execu ion
o he algo i hm G aphT a e sal}
Since he CSP e u ns all he possible alues o he a iables (DF in his case),
i is necessa y o educe i o p esen only he alues o he decision a iables (DV).
To his end, he decision a iables a e de ined as objec i es du ing he p opaga ion
p ocess whe e he a iables a e ins an ia ed. This enables he sea ch o s op he
ins an ia ion in he b anches whe e no new alues o decision a iables can be
ound, he eby bounding he unnecessa y combina ions o alues. Fo each solu ion
ound, each alue o he decision a iables is s o ed in a so ed lis . Each o hese
so ed lis s is ea ed in o de o e u n he lis o in e als o each a iable o
decision. Fo example i he alues {1, 2, 3, 5, 8, 9, 10} a e ound o he a iable
x, he lis o in e als buil is {[1, 3], [5, 5], [8, 10]}.
Fo he example o Figu e 3, he CSP buil o analyse he possible alid alues o
he a iable o decision Numbe O P oceedings in he ac i i y P in P oceeding (which
uses he a iables and BDCs p esen ed in he Appendix) is:
//All he a iables o he da a low
o alCos , numEa lyPa icipan , numLa ePa icipan , ... In ege
//The a iables ins an ia ed un il he decision poin
Ea lyRegis a ionFee = 500
La eRegis a ionFee = 750
.. .
//Range o he da a low a iables and BDCs o he whole p ocess
o alCos [5000..50000]In ege
numEa lyPa icipan s[50..200]In ege
numLa ePa icipan s[10..100]In ege
numPa icipan s[60..300]In ege
Cos Pe Pa icipan = 3*lunchP ice+dinne P ice+p oceedingsP ice
.. .
//BDCs ob ained om he algo i hm G aphT a e sal
BDCs o he Ac i i y Es ablish Con e ence Ra e
BDCs o he Ac i i y Con ac Pa ne
...
(((Income- o alCos <4000) ∧( ue)) ∨(¬(Income- o alCos <4000) ∧
(sponso ship≥5000)∧(Local in i a ion) ∨
((sponso ship≥8000) ∧(In e na ional in i a ion))))
//BDCs o he Ac i i y Hold Con e ence
//BDCs o he Ac i i y Final epo and make paymen s
Goal o b anching(numO P oceedings)
The CSP sol e used in ou p oposal is ChocoT M 12. Once he esolu ion o he
CSP has inished, he lis o in e als ob ained is used o in o m he use abou he
possible co ec alues. Fo he example, once ea lyRegis a ionFees,la eRegis a-
ionFees, enueCos ,sponso ship,sen Pape s and accep edPape s ha e been ins an-
ia ed in he p ocess wi h he alues {500, 750, 2500, 8500, 150} espec i ely, in he
ac i i y P in P oceedings he in e al {[67, 123]}is ob ained in o de o asce ain
P ocess Laye
Business
Da a
Cons ain s
Reposi o y
P esen a ion
Laye
Applica ion Laye
Pe sis ence Laye
Da a Inpu
Decision Making
Suppo Laye
Nume ical Sol e Symbolic Sol e
Fig. 7. DSS o Decision-making suppo o Inpu Da a
he possible alues o he decision a iable numbe O P oceedings.
7. Implemen a ion de ails in a Case o S udy
In o de o show he bene i s o he decision-making suppo o inpu da a in
business p ocess ins ances, we ha e implemen ed a solu ion based on he P ocess
Awa e In o ma ion Sys em (PAIS) amewo k and illus a ed how o acili a e he
inpu da a suppo in o a comme cial solu ion.
7.1. Decision Suppo Sys em o Inpu Da a in Business
P ocesses
In o de o pe mi he decision-making suppo o inpu da a a a ious poin s o
he p ocess based on he BDCs, his pape is based on an ex ension o he classic
PAIS amewo k 16, as p esen ed in 17, and shown in Figu e 7. In gene al, a PAIS
a chi ec u e 18 can be iewed as a 4- ie sys em as p esen ed in 16, whe e, om
op o bo om, he laye s a e: P esen a ion Laye , P ocess Laye , Applica ion Laye
and Pe sis ency Laye . As a undamen al cha ac e is ic, PAIS p o ides he means
o sepa a e p ocess logic om applica ion code.
Da a decision-making suppo and business p ocess laye s a e wo pa allel and
”independen ” sys ems. They a e conside ed independen since hey can be simul-
aneously execu ed in sepa a e machines, o di e en applica ions. Howe e , his
independence ails om he poin o iew o da a low in o ma ion, since, o he
a ious decision poin s, he Da a Inpu Decision laye uses he ins an ia ed a i-
ables in he da a low and uses he decision a iables ha he p ocess needs. Wi h
he p esen ed DSS, i is possible o design bo h he business p ocess model and
he BDCs, he eby achie ing highe le els o lexibili y and agili y in he business
p ocess managemen . One o he i ems ha also needs o be s udied is how o s o e
he BDCs, and how o de ine he ela ion be ween each o hem and he ac i i ies o
he p ocess. When a g ea deal o BDCs ha e o be handled, he use o a da abase o
s o e and manage hese cons ain s is manda o y, especially when no all he BDCs
a e es ablished o he whole business p ocess, and he ela ion be ween ac i i ies
and BDCs has o be de ined.
The necessi y o s o e he business compliance ules was analysed in 19, bu
ailed o ake da a seman ics in o accoun . Howe e , BDCs canno be s o ed in
a classic ela ional da abase, since s o ing a BDC also implies s o ing all he de-
ails ela ed o i s a iables, he domain o a iables, and da a pe sis ence ela ion-
ships. The di icul y in s o ing BDCs a ises due o he p oblem o how o s o e he
cons ain s hemsel es as da a, since hey do no belong o a ype suppo ed by
comme cial da abases. In o de o manage cons ain s, we p opose he use o Con-
s ain Da abase Managemen Sys ems (CDBMS) as explained in 20. Tha p oposal
is based on an en elope o a da abase managemen sys em o manage Cons ain s
as a classic ype, which has been p oposed o he desc ip ion o he BDCs. A sim-
ila way o s o e BDCs was used in p e ious wo k 7. This solu ion shields he use
om unnecessa y de ails on how he BDCs a e s o ed and que ied.
Once how o s o e he BDCs is asce ained, he nex ques ion is how each BDC
is ela ed o each ac i i y and wi h he es o he p ocess. Figu e 8 ep esen s
he ela ions necessa y o desc ibe ha a P ocess has a se o Da a low a iables,
a ailable o he Ac i i ies. Each BusinessDa aCons ain can be associa ed wi h
a se o ac i i ies o o he whole p ocess, while each Ac i i y can ha e se e al
associa ed BDCs. These associa ions a e es ablished in he able Ac i i y/BDC.
Ac i i ies
(pk) IdAc i i y: in
Name: S ing
Ac i i y/BDC
(pk) IdBR: in
(pk) IdAc i i y: in
1..1
0..n
Business Da a Cons ain s
(pk) IdBDC: in
ule: Cons ain
0..n
1..1
Business P ocesses
(pk) IdP ocess: in
Name: S ing
bpmn2.0: XML
Da aFlowVa s
(pk) IdVa iable: in
Name: S ing
Type: S ing
1..1 0..n
1..1
1..n
0..n
0..1
Fig. 8. Rela ions be ween BDCs and Ac i i ies
P esen a ion
Laye }
Connec o
MARTIN
(MAking Reasoning o
daTa INpu )
P ocess Laye
Da a Inpu Decision-Making
Suppo Laye
1
4
2
3
Fig. 9. DSS o he decision-making suppo o inpu da a wi h ools
7.2. Compu a ional applica ion o he Decision-Making suppo
o Inpu Da a
In o de o acili a e he c ea ion o BDCs and he decision-making o he p ocess,
we ha e implemen ed an applica ion and a connec o ha ollow he DSS p esen ed
in Sec ion 7.1. This solu ion uses a speci ic se o echnologies ha could be eplaced
by ano he se . The speci ic con igu a ion ha we ha e implemen ed is p esen ed in
Figu e 9, which desc ibes a possible combina ion o ools o execu e he decision-
making suppo o inpu da a. We ha e p epa ed a ideo 21 whe e he s eps o
he design and execu ion o he decision suppo a e shown. The s eps o con igu e
and use he applica ion a e:
(1) Modelling he business p ocess and de ining he da a low a iables (P,
DF): The business p ocess and he da a low a iables can be modelled in any
Business P ocess Managemen Sys em, o example: In alioT M , Ac i i iT M , and
Boni a Open Solu ionT M . In he case o s udy p esen ed, we ha e used Boni a
Open Solu ionT M since i is an open-code applica ion wi h ee dis ibu ion, and
is commonly used in he p i a e company sec o . Once he p ocess is modelled,
he designe o he p ocess mus decide on he decision poin s associa ed wi h
any ac i i y.
(2) Loca ing he decision poin s and decision a iables (DV): I a designe
conside s alloca ing a decision poin in o a de e mined ac i i y, hen a connec-
o mus be added in he ac i i y o ela e i wi h he so wa e ha execu es
he decision. We ha e implemen ed a connec o (as shown in he ideo 21) o
acili a e he ela ion be ween he decision poin and he easoning so wa e
(MARTIN) ha ob ains he possible and co ec alues o he decision a i-
ables. When a connec o is included in an ac i i y, i is necessa y o de ine he
decision a iables. In he connec o , called ”Da a Decision-Making Connec o ”,
all he da a low alues and he decision da a a iables a e sen a un ime o

he so wa e ha execu es he e alua ion o he decision-making suppo o
he model ob ained a design ime, as explained in Sec ion 6.
(3) C ea ing he BDCs (BDC): Once he p ocess model is de ined, i is necessa y
o c ea e he BDCs and associa e hem o each ac i i y, using he solu ion o
Figu e 8. The decision-making p ocess uses all he a iables ins an ia ed in he
da a low a un ime, and he BDCs ha ep esen he possible co ec alues in
he u u e. To his end, we ha e implemen ed an applica ion called MARTIN:
MAking Reasoning o daTa INpu , o acili a e he c ea ion o BDCs, and he
associa ion o he BDCs o he ac i i ies. Mos comme cial ools p o ide an
XML ep esen a ion o he c ea ed p ocess ha ollows he BPMN 2.0 13. Fo
his eason, we ha e implemen ed a ans o ma ion om a .bpmn ile ha ep e-
sen s he ype models desc ibed in his pape ( o med by ac i i ies and con ol
lows), in o ou P ocess G aph. When he XML o he p ocess is analysed, he
ables Business P ocess,Ac i i ies, and Da a lowVa shown in Figu e 8 a e illed
in au oma ically, since he XML o he p ocess holds all his in o ma ion. I is
hen possible o c ea e and/o assign BDCs o he a ious ac i i ies. In o de o
p o ide a simple way o he business expe o add BDCs, he in e ace (shown
in Figu e 10.a) enables he business p ocess model o be iewed, and BDCs o
be assigned o he ac i i ies ha belong o he business p ocess (Figu e 10.b).
a) C ea ing BDCs b) Assigning BDCs
Fig. 10. Connec o o c ea e and assign BDCs o ac i i ies
(4) Ins an ia ing he business p ocess model (DFI) and ob aining he de-
cision a iables ins an ia ion (DVI). Once he business p ocess model has
been designed in a Business P ocess Managemen Sys em, he execu ion p o-
cess can be pe o med. When a business p ocess ins ance execu es an ac i i y
wi h a Da a Decision-Making Connec o , he da a low alues o ha ins ance
and da a decision a e sen o he Da a Inpu Decision-Making Suppo laye
which implemen s he e alua ion o models, as explained in Sec ion 6. Figu e
11 p esen s an example o he o m ha shows he possible in e als o each
decision a iable.
Fig. 11. Fo m o Ac i i y Es ablish Con e ence Ra e a un ime
8. Rela ed Wo k
Decision-making suppo in business p ocesses ca ies signi ican con ibu ions e-
la ed o how o model he p ocess, which in u n help he designe o decide he
bes combina ion o ac i i ies o achie e an objec i e. A simula ion-based app oach
o decision-making suppo is p oposed in 22 wi h espec o complex dynamic sys-
ems, and includes unce ain da a. A me hodology o op imize a p ocess whe e he
desc ip ion is no clea ( uzzy) is pu o wa d in 23. Howe e , in bo h pape s, he
help in he business p ocess has been o ien ed owa ds he design o he model, o
he edesign o he business p ocess 24, by looking a he quali y o he p ocess
a design ime, bu no how his p ocess wo ks a un ime, he eby missing he
impo ance o he a iables o he da a low. Da a has also been in ol ed in o he
s udies ela ed o decision suppo ; o example 25 p oposes an ope a ional decision
suppo o he cons uc ion o p ocess models based on his o ical da a o simula e
p ocesses. Tha p oposal includes a gene ic app oach o a business p ocess o op-
e a ional decision suppo , and includes business p ocess modelling and wo k low
simula ion wi h he models gene a ed, by using p ocess mining. O he wo k ela ed
o how o model he p ocesses, such as ha in 26, p oposes a amewo k o assis-
ance o c ea e models which ake he necessa y esou ces in ol ed in he p ocess
in o accoun . In ha pape , he da a ha desc ibe he esou ces o he execu ion
o he p ocess a e used, bu no he da a ha lows a un ime, no does i conside
how his assis ance can help a un ime.
In gene al, pape s ound in he li e a u e ela ed o decision-making suppo
a e no ocused on he assis ance o he use o he inpu da a. Al hough wo k
such as 27 is o ien ed owa ds audi ing he p ocess in o de o de ec gaps be ween
he in o ma ion sys em p ocess low and he in e nal con ol low in he business
p ocess, he quali y o he da a alues a un ime is no a cause o conce n o
he au ho s. This de ec ion can be de i ed om he exis ence o an o e sigh in he
desc ip ion o he seman ics o da a in he business p ocesses. The use o compliance
ules has adi ionally been used o he alida ion o he business p ocess, no o
use assis ance. The alida ion o business p ocess aces has been a ield o in ense
esea ch o e ecen yea s using business compliance ules: see 28 as an en y poin
in o his li e a u e. Howe e , hese ypes o p oposal canno be used in he decision-
making suppo o inpu da a, since hey a e ocused on he compliance o he
p ocess model s uc u e 29, 30.
Rega ding how o model da a-awa e compliance ules, s udies such as 10, 31,
32, and 34, ha e de ined g aphical no a ions o ep esen he ela ionship be ween
da a and compliance ules by means o da a condi ions. These ypes o compliance
ules canno he e o e be used o in e he possible alues o he a iables ha a e
in ol ed in he decisions. In 35, ”seman ic cons ain s” and he SeaFlows amewo k
o enabling in eg a ed compliance suppo a e p oposed. Fu he mo e, in 37, a
p ep ocessing s ep o enable da a-awa e compliance checking in an e icien manne
is p esen ed: he da a desc ibe unde wha condi ions he ac i i ies can be execu ed.
In gene al, many examples can be ound whe e da a objec s a e used o compliance
e i ica ion, o ins ance, he seman ically anno a ing ac i i ies wi h p econdi ions
and e ec s ha may e e o da a objec s a e in oduced in 38, bu none o hese
ac o s assis he use wi h his in o ma ion a un ime.
Summa izing, o he bes o ou knowledge, only one p elimina y s udy 17 exis s
ha uses he knowledge o he business p ocess model and he BDCs o decision-
making suppo o inpu da a, while he es o he p oposals a e ocused on he
design o e-design o he business p ocess model. The cu en pape cons i u es an
imp o emen on he p e ious pape by: including he p ocess g aph de ini ion o
ep esen he model; de ining he algo i hm o a e se he g aph; and by imple-
men ing an applica ion ha can be in eg a ed wi h comme cial ools.
9. Conclusions and Fu u e wo k
In his pape , he use o BDCs is p oposed o in e he possible inpu da a alues in
a business p ocess ins ance, by aking in o accoun he model o he p ocess and he
decision poin s whe e he assis ance mus be execu ed. In o de o mee his chal-
lenge, wo di e en pa s ha e been dis inguished: an analysis o he p ocess model
a design- ime, by means o an algo i hm o a e se he business p ocess model;
and an e alua ion o he decision model a un ime, o ob ain he possible alues
o he decision a iables using cons ain p og amming. In o de o implemen his
p oposal, a case o s udy has been de eloped using Boni a So and an applica ion
called MARTIN has been included in o de o c ea e, include, and e alua e he
BDCs in he business p ocess model.
One aspec ha can be imp o ed in his wo k is he p esen a ion o he possible
and co ec alues o he decision a iables o he use . In he case o nume ical
decision suppo , his imp o emen could be a ained by showing he use he pos-
sible combina ions o a ious decision a iables simul aneously, ins ead o solely
p esen ing he in e als o each a iable. As u u e wo k, we also plan o include a
symbolic ep esen a ion o he possible co ec a iables ins ead o in e al alues.
Fu he esea ch could be analysed in g ea e dep h, such as: a) In he cu en
p oposal, all he BDCs a e de ined o all ins ances, bu i would be possible, de-
pending on he alues o he da a low o he momen whe e he ins ance is execu ed,
ha a di e en se o BCDs could be in ol ed in he decision suppo . b) Rela ed o
he abo e p oposal, he e is also he possibili y o including new BDCs o he use
a un ime, which would ende he decision-making suppo o inpu da a mo e
cus omized. This would help he use o include, o example, educ ions o he
domains o de e mined decision a iables associa ed wi h u u e decision poin s,
e en be o e he inal alue is in oduced. I , a a decision poin , he e a e no possi-
ble alues o a decision a iable, i implies ha no BDC is consis en . Fo u u e
wo k, we p opose ein o cing he de ec ion o he minimum se o BDCs ha a e
no sa is iable, and asce aining how his si ua ion can be ixed by means o inpu
da a decisions.
Acknowledgemen
This wo k has been pa ially unded by he Jun a de Andaluc´ıa by means o la Conseje ´ıa
de Inno aci´on, Ciencia y Emp esa (P08-TIC-04095) and by he Minis y o Science and
Technology o Spain (TIN2009-13714) and he Eu opean Regional De elopmen Fund
(ERDF/FEDER).
Appendix
Da a low Va iables:
• o alCos ,income is he o al cos and income o he con e ence espec i ely
•numEa lyPa icipan s is he numbe o people a ending, egis e ed in he
ea ly pe iod
•numLa ePa icipan s is he numbe o people a ending, egis e ed in he la e
pe iod
•numPa icipan s is he inal numbe o pa icipan s
•cos Pe Pa icipan is he cos pe pa icipan , and includes lunch and dinne ,
cos o p oceedings, e c.
•ea lyRegis a ionFee is he cos o ea ly egis a ion
•la eRegis a ionFee is he cos o la e egis a ion
•sponso ship is he income om he companies ha sponso he e en
•dinne P ice is he p ice o he gala dinne