ePubWU Ins i u ional Reposi o y
Saimi Bala and C is ina Cabanillas Macias and And eas Sol i and Jan
Mendling and Axel Polle es
Mining P ojec - O ien ed Business P ocesses
Book Sec ion (D a )
(Re e eed)
O iginal Ci a ion:
Bala, Saimi and Cabanillas Macias, C is ina and Sol i, And eas and Mendling, Jan and Polle es,
Axel ORCID: h ps://o cid.o g/0000-0001-5670-1146
(2015)
Mining P ojec - O ien ed Business P ocesses.
In: Business P ocess Managemen . BPM 2016. Lec u e No es in Compu e Science, ol 9253.
Sp inge , Cham.
pp. 425-440. ISBN 1611-3349
This e sion is a ailable a : h ps://epub.wu.ac.a /7203/
A ailable in ePubWU: Oc obe 2019
ePubWU, he ins i u ional eposi o y o he WU Vienna Uni e si y o Economics and Business, is
p o ided by he Uni e si y Lib a y and he IT-Se ices. The aim is o enable open access o he
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This documen is an ea ly e sion ci cula ed as wo k in p og ess. The e a e mino di e ences
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h p://epub.wu.ac.a /
Mining P ojec -O ien ed Business P ocesses?
Saimi Bala, C is ina Cabanillas, Jan Mendling,
And eas Rogge-Sol i, and Axel Polle es
Vienna Uni e si y o Economics and Business, Aus ia
{ i s name.las name}@wu.ac.a
Abs ac . La ge enginee ing p ocesses need o be moni o ed in de ail
ega ding when wha was done in o de o p o e compliance wi h ules
and egula ions. A ypical p oblem o hese p ocesses is he lack o con-
ol ha a cen al p ocess engine p o ides, such ha i is di icul o
ack he ac ual cou se o wo k e en i da a is s o ed in e sion con ol
sys ems (VCS). In his pape , we add ess his p oblem by de ining a
mining echnique ha helps o gene a e models ha isualize he wo k
his o y as GANTT cha s. To his end, we o mally de ine he no ion o a
p ojec -o ien ed business p ocess and a co esponding mining algo i hm.
Ou e alua ion based on a p o o ypical implemen a ion demons a es
he bene i s in compa ison o exis ing p ocess mining app oaches o his
speci ic class o p ocesses.
Keywo ds: p ocess mining, p ojec s, p ojec mining, e sion con ol
sys ems
1 In oduc ion
Business p ocess managemen plays an impo an ole o imp o ing he pe o -
mance and compliance o a ious ypes o p ocesses. In p ac ice, many p ocesses
a e execu ed wi h clea guidelines and egula o y ules, bu wi hou an explici
cen alized con ol imposed by a p ocess engine. In pa icula , i is o en im-
po an o exac ly know when which wo k was done. This is, o ins ance, he
case o complex enginee ing p ocesses in which di e en pa ies a e in ol ed.
We e e o his class o p ocesses as p ojec -o ien ed business p ocesses.
Such p ojec -o ien ed business p ocesses a e di icul o con ol due o he
lack o a cen alized p ocess engine. Howe e , he e a e a ious uns uc u ed
pieces o in o ma ion a ailable o analyze and moni o hei p og ess. One ype
o da a ha a e o en a ailable hese p ocesses is e en da a om e sion con ol
sys ems (VCS). While p ocess mining echniques p o ide a use ul pe spec i e on
how such e en da a can be analyzed, hey do no p oduce ou pu ha is eadily
o ganized acco ding o he p ojec o ien a ion o hese p ocesses.
?This wo k has been unded by he Aus ian Resea ch P omo ion Agency (FFG)
unde g an 845638 (SHAPE) and he Eu opean Union’s Se en h F amewo k P o-
g amme unde g an 612052 (SERAMIS).
2 S. Bala e al.
In his pape , we de ine o mal concep s o cap u ing p ojec -o ien ed p o-
cesses. These concep s p o ide he ounda ion o us o de elop an au oma ic
disco e y echnique which we e e o as p ojec mining. The ou pu o ou
p ojec mining algo i hm is o ganized acco ding o he speci ic s uc u e yp-
ically encoun e ed in p ojec -o ien ed business p ocesses. Wi h his wo k, we
ex end he ield o p ocess mining owa ds he co e age o his speci ic ype o
business p ocess.
The pape is s uc u ed as ollows. Sec ion 2 desc ibes he esea ch p ob-
lem and summa izes insigh s om p io esea ch upon which ou p ojec mining
app oach is buil . Sec ion 3 de ines he p elimina ies o ou wo k and p esen s
an algo i hm o mine p ojec -o ien ed business p ocesses. Sec ion 4 desc ibes
he implemen a ion o his algo i hm and discusses he esul s om i s applica-
ion o VCS logs om a eal-wo ld enginee ing p ojec . Sec ion 5 highligh s he
implica ions o his wo k be o e Sec ion 6 concludes.
2 Backg ound
He e, we desc ibe he add essed p oblem and ela ed wo k.
2.1 P oblem Desc ip ion
The class o p ocesses ha we discuss in his pape a e long- e m enginee ing
p ojec s. These p ocesses ha e speci ic equi emen s o moni o ing. Fi s , hey
a e execu ed only once acco ding o he speci ic needs o a pa icula p ojec , and
only pa ially acco ding o ecu ing p ocess desc ip ions. Second, hey in ol e
a ious ac o s ha ypically documen hei wo k in a semi-s uc u ed way using
ex and ables. Thi d, wo k in he p ojec is usually subjec o cons ain s
ega ding he s a and end and he empo al o de . Fou h, he e is ypically
no p ocess engine con olling he execu ion. Fi h, e en hough hese limi a ions
in e ms o aceabili y exis , he e a e usually s ong equi emen s in e ms o
acking when which wo k was conduc ed.
In line wi h hese obse a ions, a p ojec -o ien ed business p ocess can be
de ined as an ad-hoc plan ha speci ies he asks o be pe o med wi hin a limi ed
pe iod o ime and wi h a limi ed se o esou ces o achie ing a speci ic goal.
Unlike epe i i e business p ocesses o which no a ions such as BPMN [12] o
EPC [1] a e commonly used, p ojec -o ien ed business p ocesses may be p ope ly
ep esen ed wi h PERT o GANTT models. The concep is illus a ed in Fig. 1.
Documen a ion is equi ed no only explici ly as pa o some ac i i ies bu
also o comply wi h no ms and egula ions ha may equi e some e idence o
he ac ions being pe o med in he o ganiza ion. Documen s a e usually ee
o o ma o con ain ables, a bes . The uns uc u edness o da a makes i
di icul o moni o p ocesses and check ules on hem. A s a ing poin o
analysis o p ojec -o ien ed p ocesses can be da a logs ha a e s o ed in So wa e
Con igu a ion Managemen (SCM) sys ems ha help acking he e olu ion o
da a and es o e in o ma ion i needed [19]. Howe e , hund eds o e sions o
Mining P ojec -O ien ed Business P ocesses 3
P1
RESOURCES
P1P2
P2
1 2
DATA
PROJECT
ACTIVITY 1
ACTIVITY 2
{P1,P2}
{P2}
... n
... ACTIVITY n {Pn}
PROJECT MINING
Fig. 1. P oblem illus a ion
housands o iles a e common in a single p ojec [20], which makes i imp ac ical
o b owse his da a manually.
Le us see an example inspi ed by a eal scena io o a p ocess o w i e a
p ojec p oposal ha uses a Ve sion Con ol Sys em (VCS) o s o e he da a.
The p ojec his o y, and hence, he da a p oduced, s a s when people begin o
wo k on he p oposal, which in ol es a desc ip ion o he p ojec goals and mile-
s ones, a di ision o asks in o wo k packages, an es ima ion o cos and esou ces
equi ed, e ce e a. This in o ma ion is sp ead in he eposi o y o e se e al old-
e s con aining di e en documen s, which a e la e me ged in o a single ile. I
he p oposal is accep ed, he i s s ep is o o ganize a kicko mee ing and assign
speci ic esou ces o he wo k packages. A hie a chical se o olde s is hen c e-
a ed in he eposi o y in o de o s o e he in o ma ion gene a ed o each wo k
package. As he p ojec e ol es o e ime, esou ces con ibu e by adding, emo -
ing o modi ying in o ma ion o he VCS eposi o y. P ojec e olu ion is guided
by speci ic no ms ha impose he execu ion o p ede ined s eps. Fo ins ance,
he Eu opean no m EN5016 equi es a p elimina y Reliabili y, A ailabili y and
Main ainabili y (RAM) analysis o suppo a ge s.
Table 1 depic s an exce p o he log da a gene a ed, whe e he i s column
(on he le hand side) indica es he commi iden i ie , he second column indi-
ca es he pe son who commi ed changes, he hi d column indica es he commi
da e, and he ou h column indica es he iles a ec ed and he ype o ac ion
pe o med among added (A), modi ied (M) and dele ed (D). Fo he sake o
simplici y, he able shows he log da a o a speci ic ime pe iod and he ac ions
ela ed o a speci ic ask, namely, De ine example. Tha ask was assigned o
esou ce X and was supe ised by esou ce Y and, la e on, also by esou ce Z.
Exis ing amewo ks, such as Sub e sion o Gi , allow o access hei logs
in di e en ways. Howe e , he co e ed in o ma ion is limi ed o ( oughly) ha
4 S. Bala e al.
Table 1. Exce p om VCS log da a o he e e enced ime pe iod
CID Resou ce Da e Lis o changes
1 Y 2014-11-12 11:57:46 A /example
A /example/SHAPE/ToyS a ionExample.docx
. . . . . . . . . . . .
3 X 2014-11-14 16:34:07 M /example/ToyS a ion.bpmn
M /example/ToyS a ion.png
4 W 2014-12-15 13:49:11 D /example/Download
5 W 2015-01-08 16:06:41 A /example/Download2
6 X 2015-01-13 11:47:09 M /example/ToyS a ion 0Loop.bpmn
M /example/ToyS a ion nLoop.bpmn
7 Z 2015-01-16 16:50:29 A /example/ToyS a ion 0Loop.pd
A /example/ToyS a ion- eedbackZ.pd
depic ed in Table 1. Especially o big p ojec s ha a e equen ly upda ed
o e a la ge pe iod o ime, hese logs a e complex o analyze. The e o e, he
p oblem o add ess is how o analyze and isualize he in o ma ion p oduced
in p ojec -o ien ed business p ocesses such ha i can be ep esen ed in an
unde s andable and manageable way by p ojec expe s and enable, a.o., he
au oma ion o mechanisms o compliance checking. The ollowing p ope ies o
p ojec -o ien ed p ocess logs mus be aken in o accoun o achie e his goal: (i)
VCS eposi o ies consis o a hie a chy o olde s and iles which a e logically
o ganized such ha wo k is g ouped in a speci ic way; (ii) p ocess ac i i ies a e
no egis e ed in VCS log en ies. The e o e, such in o ma ion mus be in e ed
by easoning on he eposi o y s uc u e and/o he con en o he log en ies;
(iii) he g anula i y o he e en s is unknown a p io i and i needs o be de ined
be o e analyzing he da a.
2.2 Rela ed Wo k
The p oblem desc ibed has been add essed in he li e a u e om di e en pe -
spec i es. The i s ca ego y o ela ed wo k ackles he p oblem by ans o ming
i in o a p ocess mining p oblem. Consequen ly, app oaches ha e been de eloped
o p ep ocess VCS da a such ha p ocess mining echniques can be applied, and
hence, a business p ocess can be de i ed om he log da a. In his g oup, Kindle
e al. [9,10] de eloped an algo i hm o ex ac ing so wa e p ocesses ha a e
mapped o Pe i Ne s. Ac i i ies, which a e no explici in he logs, a e disco e ed
om hei inpu and ou pu a i ac s. Howe e , s ong assump ions a e made on
he ilenames as well as on he so wa e p ocess li ecycle. Rubin e al. in [15] ad-
d essed he p oblem o enginee ing p ocesses ha a e no well documen ed and
a e usually uns uc u ed. They p o ided a b idge om Kindle e al.’s app oach
o P oM [5] in o de o mine di e en p ocess pe spec i es, such as pe o mance
social ne wo k analyses. Rubin e al. [16] applied p ocess mining o he ou is ic

Mining P ojec -O ien ed Business P ocesses 5
indus y and ob ained use p ocesses om web clien logs pu suing he goal o
imp o ing he so wa e sys em by analyzing he unde lying p ocess. Poncin e
al. [14] de eloped he FRASR amewo k o p ep ocessing so wa e eposi o ies
o ans o m he VCS da a o logs ha con o m o he p ocess mining e en log
me a model [4] as u ilized in P oM [5]. Howe e , hese app oaches dis ega d he
single-ins ance na u e o p ojec -o ien ed business p ocesses and ea hem as
p ocedu es ha can be epea ed o e ime.
The second ca ego y o ela ed wo k ocuses on he isualiza ion o VCS da a
o di e en pu poses. Se e al app oaches s udy he in e ac ion among de elop-
e s o e ime om a isualiza ion poin o iew. Fo ins ance, Ogawa and Ma
[11] d ew s o yline pa hways o show he s o y o each de elope ’s con ibu ion.
O he app oaches analyze and isualize VCS da a a ile le el in o de o disco e
ile e sion e olu ion. Voinea and Telea [20] in oduced an in e ac i e na iga ion
me hod o su ile e sion e olu ion as well as wo me hods o clus e e sions
o he same ile in an abs ac ion laye . Wu e al. [22] also isualized he e olu-
ions o en i e p ojec s a ile le el, emphasizing he e olu ion momen s. Finally,
se e al app oaches s udy change p edic ion wi h he aim o disco e ing p edic-
ion pa e ns ha can help in he p ocess o so wa e de elopmen [24,23]. The
app oaches men ioned in his ca ego y as well as o he s ha apply simila ech-
niques [6,8,3] ocus on s udying so wa e e olu ion om di e en s andpoin s.
Howe e , he goal pu sued di e s in all cases om ou goal in ha hey a e no
in e es ed in disco e ing p ojec s asks ou o he log da a, and hence, hey lack
an explici no ion o wo k s uc u e ha we need o conside o ou pu pose.
Ou app oach combines ideas om bo h a eas, as we aim a iden i ying asks
like in he app oaches ha ely on p ocess mining, bu we mus clus e he da a
in an app op ia e way, o which echniques de eloped in he app oaches ha
pu sue isualiza ion may be adap ed o ex ended.
3 Mining VCS E en Da a
He e, we i s o malize he no ions encoun e ed in he p ojec mining se ing.
Then we de elop an app oach o acqui e a hie a chical o e iew on he p ojec
om a eposi o y pe spec i e.
3.1 P elimina ies
Ve sion con ol sys ems (VCSs) a e used in p ojec s o ensu e eliable collabo-
a ion. We build ou app oach on VCS. Typically, he wo k low in VCS is ha
people wo k on iles (e.g., ex , sou ce code, sp ead shee s) and commi hem o
he cen al eposi o y. P ojec pa icipan s commen on hei commi s so ha
o he pa icipan s can be e unde s and he na u e o he changes o he iles.
Le Fbe he uni e se o iles. Files a e o ganized in a ile ee. The e o e,
each ile ∈Fhas one pa en ile. The only ile wi hou a pa en ile is he
oo ile. We cap u e his in o ma ion in he pa en ela ion Pa en :F×F. Fo
example, le p∈Fbe he pa en o ile c∈F, hen ( p, c)∈Pa en . The
6 S. Bala e al.
ansi i e closu e on he pa en iles is gi en by he unc ion ances o :F→2F
ha e u ns he se o iles along he pa h o he oo .
When p ojec membe s did a ce ain amoun o wo k and wan o sa e hei
cu en p og ess, hey commi he changes o he VCS. We de ine changes on
iles as he e en s o in e es on he lowes g anula i y.
De ini ion 1 (E en ). Le E be he se o e en s. An e en e ∈E is a ou -
uple ( , o, s,k), whe e
– ∈Fis he a ec ed ile o he e en .
–o∈O={added, modi ied, dele ed}is he change ope a ion on he ile wi h
ob ious meaning.
– s ∈TS =N0 ep esen s a unix ime s amp ma king he ime o he e en
occu ence.
–k∈Σ∗is a commen in na u al language ex .
Fo e en s e= ( , o, s,k) we o e load , o, s, and k o be used as accesso
unc ions. Fo example, is he unc ion :E→Fmapping an e en o i s
a ec ed ile.
P ojec pa icipan s can commi a numbe o changes o di e en iles a one
s ep. The e o e, we de ine he no ion o commi s as ollows.
De ini ion 2 (Commi ). A commi Cis a se o e en s sha ing he same ime
s amp and commen , i.e., ∀e,e0∈ C : s(e) = s(e0)∧k(e) = k(e0). Addi ionally,
each e en in a commi a ec s di e en iles, i.e., ∀e,e0∈ C :e6=e0→ (e)6=
(e0).
Usually, i is in he hands o p ojec pa icipan s, when hey decide o commi
changes o he VCS. In he ex eme case, he e could be only a single commi
made in a p ojec ha adds all iles o he eposi o y. No e ha his ex eme
p ac ice would ende he use o a VCS obsole e. On he con a y, i is common
p ac ice o egula ly pe o m commi s in o de o secu ely s o e wo k p og ess
and o educe he chance o con lic s [13,7]. Con lic s occu , when ano he pa -
icipan commi ed changes o a ile ha is being commi ed and can cause
ex a wo k. Based on hese insigh s, we make he assump ion ha commi s a e
egula ly made du ing wo k.
P ojec s a e decomposed in o wo k packages. We assume a hie a chical wo k
package s uc u e o a p ojec , such ha a wo k package can ha e sub wo k
packages. Fu he , he amoun o wo k in a single wo k package need no be
done in one single ime span, bu i can be spli in o se e al ac i i ies. Ac i i ies
ha e a s a and end ime, and subsequen ac i i ies can ha e idle pe iods in
be ween. Thus, we de ine p ojec s as ollows.
De ini ion 3 (P ojec ). A p ojec Pis a uple (W, S, A, α, ω, β), whe e
–Wis he se o wo k packages in he p ojec .
–S⊆W×Wis he ela ion ha hie a chically decomposes wo k packages in o
a ee s uc u e.
–A is he se o ac i i ies ha a e conduc ed in he wo k packages.
Mining P ojec -O ien ed Business P ocesses 7
–α:A→TS is he unc ion ha assigns a s a ime o ac i i ies. Ac i i ies
a e o de ed by hei s a imes.
–ω:A→TS is he unc ion ha assigns an end ime o ac i i ies.
–β:A→Wis he mapping unc ion ha maps ac i i ies o hei co espond-
ing wo k packages.
No e ha his de ini ion e lec s an ac i i y cen ic iew on p ojec s. The
de ini ion delibe a ely omi s u he dimensions, e.g., cos s, esou ces, isks. The
idea is no o cap u e p ojec s in e e y de ail, bu o ocus on he wo k packages
o a p ojec o ob ain an o e iew o he wo k ha is being done. We a e in e -
es ed in when wo k has been s a ed in a wo k package, and when wo k packages
ha e been done. This in o ma ion can be de i ed om he ac i i ies associa ed
o he wo kpackages. An ob ious assump ion is ha he wo k package s a s
wi h i s i s ac i i y, and ends when i s las ac i i y is comple ed.
Based on hese no ions, we can de ine he ask o p ojec disco e y as econ-
s uc ing he p ojec P om a se o low le el e en da a E. In he ollowing,
we p esen an app oach o his p oblem.
3.2 P ojec Disco e y Technique
Fo p ojec disco e y om he VCS commi his o y, we need o iden i y ac i i ies
ha a e pe o med, associa e he ac i i ies o wo k packages and ec ea e he
wo k package s uc u e o he p ojec . Ou aim is o c ea e a hie a chical model
ha p o ides an o e iew o he p ojec wo k. The e o e, we ha e o iden i y he
s a and end imes o ac i i ies and o wo k packages be o e we can isualize
he p ojec wo k. The inpu o he echnique is he log ha is s o ed in he VCS.
The challenge is ha he aw log only eco ds commi s on he ile sys em le el
and in o ma ion on ac i i y le el is missing. Howe e , we can deduce ac i i y
in o ma ion om e en s based on he ollowing assump ions.
A1: Meaning ul ile ee s uc u e. The ile ee s uc u e in a p ojec ep-
esen s i s wo k package s uc u e. Tha is, he knowledge wo ke s o ganize
hei wo k in a ile hie a chy ha e lec s he p ojec s uc u e.
A2: Local changes. Ac i i ies in a wo k package a ec only iles o he wo k
package olde , o in he co esponding sub- ee in he ile ee s uc u e.
A3: F equen commi s. Commi s o he VCS a e egula ly pe o med, when
conduc ing wo k in an ac i i y.
disco e y s a ed
P ep ocess log
in o a se o
e en s
VCS log e en s E
Agg ega e
e en s o
ac i i ies
ac i i ies A
Iden i y wo k
packages o
ac i i ies
wo k
packages W
Agg ega e
wo k packages
wo k
package
s uc u e S
Compu e wo k
package
cha ac e is ics
p ojec P
Fig. 2. P ojec disco e y echnique o e iew as BPMN p ocess model.
8 S. Bala e al.
No e ha assump ion A1 can be seen as a s ong assump ion on he ile
ee s uc u e. Ne e heless, we a gue ha e en i A1 is no en i ely me , he
agg ega ion o wo k in o ma ion on he ile ee hie a chy p o ides a aluable
iew on he p ojec . Figu e 2 shows he di e en s eps o he echnique. We
desc ibe each o hem in de ail.
S ep 1: P ep ocessing. The i s s ep is o ans o m aw logs o e sion
con ol sys ems (which migh be g ouped by commi s) in o a lis o e en s as
speci ied in De ini ion 1. This s ep is easily done by eplica ing he in o ma ion
on commi le el o be con ained in he e en s. The ou pu is a se o e en s E.
S ep 2: Agg ega ing e en s o ac i i ies. Gi en he se o e en s E ha we
ga he ed om a e sion con ol sys em, he nex s ep is o iden i y he ac i i ies
o which he e en s belong. No e ha we do no know he ac i i ies o he
p ojec in ad ance, bu need o in e hem based on he e en s. Each e en
a ec s a single ile in he ile hie a chy.
ime
c1c2c4
c2 c3
?
a3
obse ed
ac i e
ime
a3'
adjus ed
ac i e
ime
c3
c4
c
c1
Fig. 3. Adjus men o ac i i y s a ime α.
Based on assump ion A2, we a e in e es ed in ac i i ies conduc ed in a wo k
package, ha is, we il e o he e en s ha a e con ained in he gi en ile o i s
child en. Fo e e y ile o in e es , we selec he se o e en s a ec ing he ile o
i s child en as E ={e∈E| = (e)∨ ∈ances o ( (e))}. The ask is hen o
ind he ac i i ies which emi ed he se o e en s E . We ely on assump ion A3,
which s a es ha du ing an ac i i y, we expec mul iple commi s. Assump ion
A3 allows us o conclude ha i we do no obse e commi s o a longe pe iod
o ime, he e is no ac i i y being pe o med in he wo k package.
To his end, we adop he abs ac ion echnique by Baie e al. [2] and allow
he domain expe o o mula e ules o agg ega ing e en s o ac i i ies based
on bounda y condi ions. Assuming ha people equen ly commi hei p og ess
(A3), we can speci y a bounda y condi ion based on he empo al dis ance o
p e ious e en s. Fo example, we can speci y ha a ime pe iod o se en days
wi hou a commi is a bounda y condi ion. As he esul , we ob ain he mapping
om e en s o hese ac i i ies, which we call γ :E →A in he emainde o
he pape . The se o disco e ed ac i i ies iden i ied o he wo k package based
on gi en bounda y condi ions is hen A ={a|e∈E , γ (e) = a}. We also
Mining P ojec -O ien ed Business P ocesses 15
Fig. 6. Do ed cha om P oM
Fig. 7. Cha om Disco plo ing he e en s o e ime.
6 Conclusion
In his pape we add essed he p oblem o mining and isualizing p ojec -
o ien ed business p ocesses in a way ha is in o ma i e o manage s. We de-
ine an app oach ha akes VCS logs as inpu o gene a e Gan cha s. Ou
algo i hm wo ks unde he assump ions ha eposi o ies e lec he hie a chical
s uc u e o he p ojec , each wo k package is con ained in a co esponding di-
ec o y and p ojec membe s commi hei wo k egula ly du ing ac i e wo king
imes. The app oach was implemen ed as a p o o ype and e alua ed based on
eal-wo ld da a om open sou ce p ojec s.
In u u e wo k, we aim o ex ac u he de ails o he VCS logs in o de o
calcula e me ics ha app oxima e he wo k e o . We plan o in es iga e on how
he p ojec mining app oach is a ec ed by p ojec cha ac e is ics. Fu he mo e,
we wan o u ilize s a is ical me hods o be e es ima e he bounda ies o he
ac i i ies and wo k packages. Finally, we ha e al eady inco po a ed eedback
om manage s and plan o ex end hese o ull use s udies.
Re e ences
1. an de Aals , W.: Fo maliza ion and e i ica ion o e en -d i en p ocess chains.
In o ma ion and So wa e Technology 41(10), 639–650 (1999)
2. Baie , T., Mendling, J., Weske, M.: B idging abs ac ion laye s in p ocess mining.
In o ma ion Sys ems 46, 123–139 (2014)

16 S. Bala e al.
3. D’Amb os, M., Lanza, M.: A Flexible F amewo k o Suppo Collabo a i e So -
wa e E olu ion Analysis. In: So wa e Main enance and Reenginee ing. pp. 3–12
(2008)
4. an Dongen, B.F., Van de Aals , W.M.: A Me a Model o P ocess Mining Da a.
EMOI-INTEROP 160, 30 (2005)
5. an Dongen, B.F., de Medei os, A.K.A., Ve beek, H., Weij e s, A., Van De Aals ,
W.M.: The P oM amewo k: A new e a in p ocess mining ool suppo . In: Ap-
plica ions and Theo y o Pe i Ne s 2005, pp. 444–454. Sp inge (2005)
6. Feld , R., S a on, M., Hul , E., Liljeg en, T.: Suppo ing so wa e decision mee -
ings: Hea maps o isualising es and code measu emen s. In: 39 h Con . on
So wa e Enginee ing and Ad anced Applica ions, pp. 62–69. IEEE (2013)
7. Hou, Q., Ma, Y., Chen, J., Xu, Y.: An Empi ical S udy on In e -Commi Times
in SVN. In: In . Con . on So wa e Eng. and Knowledge Eng., pp. 132–137. (2014)
8. Kagdi, H., Yusu , S., Male ic, J.I.: Mining Sequences o Changed- iles om Ve sion
His o ies. In: Wo kshop on Mining So wa e Reposi o ies. pp. 47–53. ACM (2006)
9. Kindle , E., Rubin, V., Sch¨a e , W.: Ac i i y Mining o Disco e ing So wa e P o-
cess Models. So wa e Enginee ing 79, 175–180 (2006)
10. Kindle , E., Rubin, V., Sch¨a e , W.: Inc emen al Wo k low Mining Based on Doc-
umen Ve sioning In o ma ion. In: Li, M., Boehm, B., Os e weil, L. (eds.) Uni ying
he So wa e P ocess Spec um, LNCS 3840, pp. 287–301. Sp inge (2006)
11. Ogawa, M., Ma, K.L.: So wa e e olu ion s o ylines. In: P oceedings o he 5 h
in e na ional symposium on So wa e isualiza ion. pp. 35–42. ACM (2010)
12. OMG: BPMN 2.0. Recommenda ion, OMG (2011)
13. Pila o, C.M., Collins-Sussman, B., Fi zpa ick, B.W.: Ve sion con ol wi h sub e -
sion. O’Reilly Media, Inc. (2008)
14. Poncin, W., Se eb enik, A., an den B and, M.: P ocess mining so wa e eposi o-
ies. In: So wa e Main enance and Reenginee ing (CSMR), 2011 15 h Eu opean
Con e ence on. pp. 5–14. IEEE (2011)
15. Rubin, V., G¨un he , C.W., Van De Aals , W.M., Kindle , E., Van Dongen, B.F.,
Sch¨a e , W.: P ocess mining amewo k o so wa e p ocesses. In: So wa e P ocess
Dynamics and Agili y, pp. 169–181. Sp inge (2007)
16. Rubin, V., Lomazo a, I., an de Aals , W.M.: Agile de elopmen wi h so wa e
p ocess mining. In: In . Con . on So w. and Sys em P ocess. pp. 70–74. (2014)
17. To alds, L., Hamano, J.: Gi : Fas e sion con ol sys em. h p://gi -scm.com
(2010)
18. Ve beek, H., Buijs, J.C., Van Dongen, B.F., Van De Aals , W.M.: Xes, xesame,
and p om 6. In: In o ma ion Sys ems E olu ion, pp. 60–75. Sp inge (2011)
19. Voinea, L., Telea, A.: An Open F amewo k o CVS Reposi o y Que ying, Analysis
and Visualiza ion. In: In e na ional Wo kshop on Mining So wa e Reposi o ies
(MSR ’06). pp. 33–39. ACM (2006)
20. Voinea, L., Telea, A.: Mul iscale and Mul i a ia e Visualiza ions o So wa e E o-
lu ion. In: Symposium on So wa e Visualiza ion. pp. 115–124. ACM (2006)
21. Wilson, J.M.: Gan cha s: A cen ena y app ecia ion. Eu opean Jou nal o Ope -
a ional Resea ch 149(2), 430–437 (2003)
22. Wu, J., Spi ze , C., Hassan, A., Hol , R.: E olu ion Spec og aphs: isualizing
punc ua ed change in so wa e e olu ion. In: Wo kshop on P inciples o So wa e
E olu ion. pp. 57–66 (Sep 2004)
23. Ying, A., Mu phy, G., Ng, R., Chu-Ca oll, M.: P edic ing Sou ce Code Changes
by Mining Change His o y. IEEE T ans. So w. Eng. 30(9), 574–586 (2004)
24. Zimme mann, T., Weisge be , P., Diehl, S., Zelle , A.: Mining Ve sion His o ies o
Guide So wa e Changes. In: In . Con . So wa e Enginee ing. pp. 563–572. (2004)