99 Process Mining: Overview and Opportunities

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1 Procss Mining: Ovrviw and Opportunitis WIL VAN DER AALST, Eindhovn Univrsity of Tchnology Ovr th last dcad, procss mining mrgd as a nw rsarch fild that focuss on th analysis of procsss using vnt data. Classical data mining tchniqus such as classification, clustring, rgrssion, association rul larning, and squnc/pisod mining do not focus on businss procss modls and ar oftn only usd to analyz a spcific stp in th ovrall procss. Procss mining focuss on nd-to-nd procsss and is possibl bcaus of th growing availability of vnt data and nw procss discovry and conformanc chcking tchniqus. Procss modls ar usd for analysis (.g., simulation and vrification) and nactmnt by BPM/WFM systms. Prviously, procss modls wr typically mad by hand without using vnt data. Howvr, activitis xcutd by popl, machins, and softwar lav trails in so-calld vnt logs. Procss mining tchniqus us such logs to discovr, analyz, and improv businss procsss. Rcntly, th Task Forc on Procss Mining rlasd th Procss Mining Manifsto. This manifsto is supportd by organizations and procss mining xprts contributd to it. Th activ involvmnt of nd-usrs, tool vndors, consultants, analysts, and rsarchrs illustrats th growing significanc of procss mining as a bridg btwn data mining and businss procss modling. Th practical rlvanc of procss mining and th intrsting scintific challngs mak procss mining on of th hot topics in Businss Procss Managmnt (BPM). This papr introducs procss mining as a nw rsarch fild and summarizs th guiding principls and challngs dscribd in th manifsto. Catgoris and Subjct Dscriptors: H.. [Databas Managmnt]: Databas Applications Data Mining Gnral Trms: Managmnt, Masurmnt, Prformanc Additional Ky Words and Phrass: Procss mining, businss intllignc, businss procss managmnt, data mining ACM Rfrnc Format: Van dr Aalst, W.M.P. 0. Procss Mining: Ovrviw and Opportunitis. ACM Trans. Manag. Inform. Syst.,, Articl (Fbruary 0), pags. DOI = 0./ INTRODUCTION Procss mining aims to discovr, monitor and improv ral procsss by xtracting knowldg from vnt logs radily availabl in today s information systms [Aalst 0]. Ovr th last dcad thr has bn a spctacular growth of vnt data and procss mining tchniqus hav maturd significantly. As a rsult, managmnt trnds rlatd to procss improvmnt and complianc can now bnfit from procss mining. Starting point for procss mining is an vnt log. Each vnt in such a log rfrs to an activity (i.., a wll-dfind stp in som procss) and is rlatd to a particular cas (i.., a procss instanc). Th vnts blonging to a cas ar ordrd and can b sn as on run of th procss. Evnt logs may stor additional information about vnts. Author s addrss: Dpartmnt of Mathmatics and Computr Scinc, Eindhovn Univrsity of Tchnology, PO Box, 00 MB, Eindhovn, Th Nthrlands. Prmission to mak digital or hard copis of part or all of this work for prsonal or classroom us is grantd without f providd that copis ar not mad or distributd for profit or commrcial advantag and that copis show this notic on th first pag or initial scrn of a display along with th full citation. Copyrights for componnts of this work ownd by othrs than ACM must b honord. Abstracting with crdit is prmittd. To copy othrwis, to rpublish, to post on srvrs, to rdistribut to lists, or to us any componnt of this work in othr works rquirs prior spcific prmission and/or a f. Prmissions may b rqustd from Publications Dpt., ACM, Inc., Pnn Plaza, Suit 0, Nw York, NY 0-00 USA, fax + () -0, or c 0 ACM /0/0-ART $0.00 DOI 0./ ACM Transactions on Managmnt Information Systms, Vol., No., Articl, Publication dat: Fbruary 0.

2 : W. van dr Aalst In fact, whnvr possibl, procss mining tchniqus us xtra information such as th rsourc (i.., prson or dvic) xcuting or initiating th activity, th timstamp of th vnt, or data lmnts rcordd with th vnt (.g., th siz of an ordr). world businss procsss popl machins componnts organizations modls analyzs supports/ controls spcifis configurs implmnts analyzs softwar systm rcords vnts,.g., mssags, transactions, tc. (procss) modl discovry conformanc nhancmnt vnt logs Fig.. Th thr basic typs of procss mining: (a) discovry, (b) conformanc, and (c) nhancmnt. Evnt logs can b usd to conduct thr typs of procss mining as shown in Fig. [Aalst 0]. Th first typ of procss mining is discovry. A discovry tchniqu taks an vnt log and producs a modl without using any a-priori information. Procss discovry is th most prominnt procss mining tchniqu. For many organizations it is surprising to s that xisting tchniqus ar indd abl to discovr ral procsss mrly basd on xampl bhaviors stord in vnt logs. Th scond typ of procss mining is conformanc. Hr, an xisting procss modl is compard with an vnt log of th sam procss. Conformanc chcking can b usd to chck if rality, as rcordd in th log, conforms to th modl and vic vrsa. Th third typ of procss mining is nhancmnt. Hr, th ida is to xtnd or improv an xisting procss modl thrby using information about th actual procss rcordd in som vnt log. Whras conformanc chcking masurs th alignmnt btwn modl and rality, this third typ of procss mining aims at changing or xtnding th a-priori modl. For instanc, by using timstamps in th vnt log on can xtnd th modl to show bottlncks, srvic lvls, and throughput tims. Unlik traditional Businss Procss Managmnt (BPM) tchniqus that us handmad modls [Wsk 00], procss mining is basd on facts. Basd on obsrvd bhavior rcordd in vnt logs, intllignt tchniqus ar usd to xtract knowldg. Thrfor, w claim that procss mining nabls vidnc-basd BPM. Unlik xisting analysis approachs, procss mining is procss-cntric (and not data-cntric), truly intllignt (larning from historic data), and fact-basd (basd on vnt data rathr than opinions). Procss mining is rlatd to data mining. Whras classical data mining tchniqus ar mostly data-cntric [Hand t al. 00], procss mining is procss-cntric. Mainstram businss procss modling tchniqus us notations such as th Businss Procss Modling Notation (BPMN), UML activity diagrams, Evnt-drivn Procss chains (EPC), and various typs of Ptri nts [Aalst and Stahl 0; Dsl and Risig ; Wsk 00]. Ths notations can b usd modl procss procsss with concurrncy, choic, itration, tc. ACM Transactions on Managmnt Information Systms, Vol., No., Articl, Publication dat: Fbruary 0.

3 Procss Mining: Ovrviw and Opportunitis : This papr introducs not only procss mining as a nw rsarch fild, but also familiarizs th radr with th Procss Mining Manifsto [TFPM 0] rlasd by th Task Forc on Procss Mining in Octobr 0. Th growing intrst in log-basd procss analysis motivatd th stablishmnt of a Task Forc on Procss Mining in 00. This manifsto aims to promot th topic of procss mining. Morovr, by dfining a st of guiding principls and listing important challngs, this manifsto hops to srv as a guid for softwar dvloprs, scintists, consultants, businss managrs, and ndusrs. Th goal is to incras th maturity of procss mining as a nw tool to improv th (r)dsign, control, and support of oprational businss procsss. Th rmaindr of this papr is organizd as follows. Sction introducs th notion of an vnt log, usd as input for procss mining. Sction shows how procss modls can b discovrd from scratch using only raw vnt data. Sction discusss th scond typ of procss mining: conformanc chcking. Sction laborats on th third typ of procss mining: nhancmnt. Th guiding principls and challngs listd in th manifsto ar summarizd in Sction. Sction discusss tool support and shows som ral-lif xampls. Sction concluds th papr.. EVENT LOGS AS A STARTING POINT FOR PROCESS MINING Digital vnt data is vrywhr in vry sctor, in vry conomy, in vry organization, and in vry hom and will continu to grow xponntially [Manyika t al. 0]. Th omniprsnc of such data allows for nw forms of procss analysis, i.., basd on obsrvd facts rathr than hand-mad modls. Starting point for procss mining is an vnt log. To introduc th basic procss mining concpts w us th vnt log shown in Fig. (log is takn from Chaptr of [Aalst 0]). This vnt log contains cass, i.., instancs of som rimbursmnt procss. Thr ar procss instancs following trac acdh. Activitis ar rprsntd by a singl charactr: a = rgistr rqust, b = xamin thoroughly, c = xamin casually, d = chck tickt, = dcid, f = rinitiat rqust, g = pay compnsation, and h = rjct rqust. Hnc, trac acdh modls a rimbursmnt rqust that was rjctd aftr a rgistration, xamination, chck, and dcision stp. cass followd this path consisting of fiv stps, i.., th first lin in th tabl corrsponds to = vnts. Th whol log consists of vnts. Not that vnts can hav all kinds of additional attributs (timstamps, transactional information, rsourc usag, tc.). Considr for xampl on of th a vnts. Such an vnt rfrs to th xcution of rgistr rqust for som rimbursmnt. Th vnt may hav a timstamp,.g., -0-0:., and an attribut dscribing th rsourcs involvd. Morovr, data attributs of th rimbursmnt (.g., nam of customr or numbr of loyalty card) and data attributs of th rgistration vnt (.g., th amount claimd or a booking rfrnc) may hav bn rcordd. All such attributs can b usd by procss mining tchniqus. Howvr, th backbon of procss mining is th control-flow prspctiv. Thrfor, for simplicity, vnts in Fig. ar dscribd by thir activity nams only. Howvr, it is important to raliz that vnts can hav various attributs,.g., timstamps can b usd for bottlnck analysis and rsourc attributs can b usd for organizational mining (.g., finding allocation ruls).. DISCOVERY This sction introducs th notion of procss discovry, i.., automatically construct modls basd on obsrvd vnts. ACM Transactions on Managmnt Information Systms, Vol., No., Articl, Publication dat: Fbruary 0.

4 : W. van dr Aalst # trac acdh abdg adch abdh acdg adcg adbh acdfdbh adbg acdfbdh acdfbdg acdfdbg adcfcdh adcfdbh adcfbdg acdfbdfdbg adcfdbg adcfbdfbdg adcfdbfbdh adbfbdfdbg adcfdbfcdfdbg M M a rgistr rqust a rgistr rqust p p p b xamin thoroughly c xamin casually d chck tickt b xamin thoroughly c xamin casually p d f p p dcid f chck tickt dcid rinitiat rqust p rinitiat rqust p g pay compnsation h rjct rqust g pay compnsation h rjct rqust f rinitiat rqust nd nd On vnt log and two potntial procss modls (M and M ) aiming to dscrib th obsrvd b- Fig.. havior... Applications of Procss Discovry Organizations us procdurs to handl cass. Somtims such procdurs ar nforcd by th information systm. Howvr, in most cass, procdurs ar informal and may not hav bn documntd at all. Morovr, vn whn procdurs hav bn documntd, rality may b vry diffrnt. Thrfor, it is important to discovr th actual procsss using vnt data. Th discovrd procss modls may b usd for discussing problms among stakholdrs (to rach consnsus; it is important to hav a shard viw of th ral procsss), for gnrating procss improvmnt idas (sing th actual procss and its problms stimulats r-nginring fforts), for modl nhancmnt (.g., bottlnck analysis, s Sction ), and for configuring a WFM/BPM systm (th discovrd procss modl can srv as a tmplat)... Larning Procss Modls From Evnt Logs Procss discovry tchniqus produc procss modls basd on vnt logs such as th on shown in Fig.. For xampl, th classical α-algorithm producs modl M for this log. This procss modl is rprsntd as a Ptri nt [Aalst and Stahl 0; Dsl and Risig ]. A Ptri nt consists of placs (, p, p, p, p, p, and nd) and transitions (a, b, c, d,, f, g, and h). Transitions may b connctd to placs and placs may b connctd to transitions. It is not allowd to connct a plac to a plac or a transition to a transition. ACM Transactions on Managmnt Information Systms, Vol., No., Articl, Publication dat: Fbruary 0.

5 Procss Mining: Ovrviw and Opportunitis : Th stat of a Ptri nt, also rfrrd to as marking, is dfind by th distribution of tokns ovr placs. A transition is nabld if ach of its input placs contains a tokn. For xampl, in M, transition a is nabld in th initial marking of M, bcaus th only input plac of a contains a tokn (black dot). An nabld transition may fir thrby consuming a tokn from ach of its input placs and producing a tokn for ach of its output placs. Firing a in th initial marking corrsponds to rmoving on tokn from and producing two tokns (on for p and on for p). Aftr firing a, thr transitions ar nabld: b, c, and d. Thr is a non-dtrministic choic btwn b and d. Firing b will disabl c bcaus th tokn is rmovd from th shard input plac (and vic vrsa). Transition d is concurrnt with b and c, i.., it can fir without disabling anothr transition. Transition bcoms nabld aftr d and b or c hav occurrd. Not that transition in M is only nabld if both input placs (p and p) contain a tokn. Aftr xcuting, thr transitions bcom nabld: f, g, and h. Ths transitions ar compting for th sam tokn thus modling a choic. Whn g or h is fird, th procss nds with a tokn in plac nd. If f is fird, th procss rturns to th stat just aftr xcuting a. It is asy to chck that all tracs in th vnt log can b rproducd by M. This dos not hold for th scond procss modl in Fig.. M is abl to rproduc tracs such as acdh ( instancs), abdg ( instancs), and acdfbdh ( instancs). Not that M has two transitions corrsponding to activity f. To rfr to thm thy ar namd f and f. M also allows for bhavior vry diffrnt from what can b obsrvd in th log,.g., abg and abdddddf bddddh ar possibl according to th modl but do not appar in th log. Thr ar also tracs in th log that cannot b rplayd by M,.g., adch ( instancs), adcg ( instancs), and adcfcdh ( instancs) ar not possibl according to M. Th two procss modls in Fig. ar visualizd in trms of Ptri nts. In fact, both modls ar so-calld WF-nts [Aalst t al. 0]. A WF-nt is a Ptri nt with on sourc plac and on sink plac such that all placs and transitions ar on a path from sourc to sink. Both modls in Fig. hav a sourc plac namd and a sink plac nd and all nods ar on a path from to nd. In gnral, th notation usd to visualiz th rsult may b vry diffrnt from th rprsntation usd during th actual discovry procss. All mainstram BPM notations (Ptri nts, EPCs, BPMN, YAWL, UML activity diagrams, tc.) can b usd to show discovrd procsss such as M [Aalst 0; Wsk 00]... Procss Discovry Algorithms Sinc th mid-nintis svral groups hav bn working on tchniqus for automatd procss discovry basd on vnt logs [Aalst t al. 00; Aalst t al. 00; Agrawal t al. ; Cook and Wolf ; Datta ; Dongn and Aalst 00; 00; Grco t al. 00; Wijtrs and Aalst 00]. In [Aalst t al. 00] an ovrviw is givn of th arly work in this domain. Th ida to apply procss mining in th contxt of workflow managmnt systms was introducd in [Agrawal t al. ]. In paralll, Datta [Datta ] lookd at th discovry of businss procss modls. Cook t al. invstigatd similar issus in th contxt of softwar nginring procsss [Cook and Wolf ]. Hrbst [Hrbst 000] was on of th first to tackl mor complicatd procsss,.g., procsss containing duplicat tasks. Most of th classical approachs hav problms daling with concurrncy. Th α- algorithm [Aalst t al. 00] is an xampl of a simpl tchniqu that taks concurrncy as a ing point. Th α-algorithm scans th vnt log for particular pattrns. For xampl, if activity a is followd by b but b is nvr followd by a, thn it is assumd that thr is a causal dpndncy btwn a and b. To rflct this dpndncy, th corrsponding Ptri nt should hav a plac conncting a to b. W us th notation, a > b ACM Transactions on Managmnt Information Systms, Vol., No., Articl, Publication dat: Fbruary 0.

6 : W. van dr Aalst if and only if thr is a trac σ = t, t, t,... t n in th log and an i {,..., n } such that t i = a and t i+ = b. a b if and only if a > b and b a; a#b if and only if a b and b a; and a b if and only if a > b and b > a. Ths four ordring rlations ar usd to crat placs conncting th diffrnt transitions in th Ptri nt. Th α-algorithm is simpl and fficint, but has problms daling with complicatd routing constructs and nois (lik most of th othr approachs dscribd in litratur). Rgion-basd approachs ar abl to xprss mor complx control-flow structurs without undrfitting. Stat-basd rgions wr introducd in [Ehrnfucht and Roznbrg ] and gnralizd in various ways [Cortadlla t al. ]. In [Aalst t al. 00; Dongn t al. 00; Sol and Carmona 00] it is shown how ths statbasd rgions can b applid to procss mining. In paralll, svral authors applid languag-basd rgions to procss mining [Brgnthum t al. 00; Wrf t al. 00]. Th basic ida of ths approachs is to discovr placs. Not that th addition of placs limits th bhavior of th Ptri nt. Th ida is to add placs that do not xclud any of th bhavior sn in th vnt log. For practical applications of procss discovry it is ssntial that nois and incompltnss ar handld wll. Surprisingly, only fw discovry algorithms focus on addrssing ths issus. Notabl xcptions ar huristic mining [Wijtrs and Aalst 00], fuzzy mining [Günthr and Aalst 00], and gntic procss mining [Mdiros t al. 00]. ProM s huristic minr uss th algorithm dscribd in [Wijtrs and Aalst 00] (s also Sction. in [Aalst 0]). Th algorithm first builds a dpndncy graph basd on th frquncis of activitis and th numbr of tims on activity is followd by anothr activity. Basd on prdfind thrsholds, dpndncis ar addd to th dpndncy graph graph (or not). Th dpndncy graph rvals th backbon of th procss modl. This backbon is usd to discovr th dtaild split and join bhavior of nods. If an activity has multipl input arcs, thn th huristic minr analyzs th log to s whthr th join is an AND-join, an XOR-join or an OR-join. In cas of an OR-join, th dtaild synchronization bhavior is larnd. If an activity has multipl output arcs, thn th split bhavior is larnd in a similar fashion. S Chaptr of [Aalst 0] for a mor laborat introduction to th various procss discovry approachs dscribd in litratur.. CONFORMANCE In rcnt yars, powrful procss mining tchniqus hav bn dvlopd that can automatically construct a suitabl procss modl givn an vnt log. Whras procss discovry constructs a modl without any a priori information (othr than th vnt log), conformanc chcking uss a modl and an vnt log as input. Th modl may hav bn mad by hand or discovrd through procss discovry. For conformanc chcking, th modld bhavior and th obsrvd bhavior (i.., vnt log) ar compard... Applications of Conformanc Chcking Conformanc chcking tchniqus rlat vnts in th log to activitis in th modl,.g., vnts ar mappd to transition firings in th Ptri nt. This way it is possibl to compar th obsrvd bhavior in th vnt log and th modld bhavior. For xampl, on can quantify diffrncs (.g., 0% of th obsrvd cass ar possibl according to th modl ) and diagnos dviations (.g., in rality activity x is oftn skippd although th modl dos not allow for this ). Conformanc chcking can b usd to chck th quality of documntd procsss (asss whthr thy dscrib rality accuratly), to idntify dviating cass and undrstand what thy hav in common, ACM Transactions on Managmnt Information Systms, Vol., No., Articl, Publication dat: Fbruary 0.

7 Procss Mining: Ovrviw and Opportunitis : to idntify procss fragmnts whr most dviations occur, for auditing purposs, to judg th quality of a discovrd procss modl, to guid volutionary procss discovry algorithms (.g., gntic algorithms nd to continuously valuat th quality of nwly cratd modls using conformanc chcking), and as a ing point for modl nhancmnt. Th abov list shows that conformanc chcking can b usd for a varity of rasons ranging from valuating a procss discovry algorithm to auditing and complianc monitoring. Not that auditors nd to validat information about organizations by dtrmining whthr thy xcut businss procsss within crtain boundaris st by managrs, govrnmnts, and othr stakholdrs. Clarly, vnt logs provid valuabl input for this... Diagnosing Diffrncs Btwn Obsrvd Bhavior and Modld Bhavior Typically, four quality dimnsions for comparing modl and log ar considrd: (a) fitnss, (b) simplicity, (c) prcision, and (d) gnralization (Chaptr of [Aalst 0]). A modl with good fitnss allows for most of th bhavior sn in th vnt log. A modl has prfct fitnss if all tracs in th log can b rplayd by th modl from bginning to nd. Oftn fitnss is dscribd by a numbr btwn 0 (vry poor fitnss) and (prfct fitnss). Obviously, th simplst modl that can xplain th bhavior sn in th log is th bst modl. This principl is known as Occam s Razor. Fitnss and simplicity alon ar not sufficint to judg th quality of a discovrd procss modl. For xampl, it is vry asy to construct an xtrmly simpl Ptri nt ( flowr modl ) that is abl to rplay all tracs in an vnt log (but also any othr vnt log rfrring to th sam st of activitis). Similarly, it is oftn undsirabl to hav a modl that only allows for th xact bhavior sn in th vnt log. Rmmbr that th log contains only xampl bhavior and that many tracs that ar possibl may not hav bn sn yt. (Not that in our simpl xampl most tracs ar frqunt, but oftn thr ar many on-of-a-kind tracs.) A modl is prcis if it dos not allow for too much bhavior. Clarly, th flowr modl lacks prcision. A modl that is not prcis is undrfitting. Undrfitting is th problm that th modl ovr-gnralizs th xampl bhavior in th log (i.., th modl allows for bhaviors vry diffrnt from what was sn in th log). A modl should also gnraliz and not rstrict bhavior to just th xampls sn in th log. A modl that dos not gnraliz sufficintly is ovrfitting. Ovrfitting is th problm that a vry spcific modl is gnratd whras it is obvious that th log only holds xampl bhavior (i.., th modl xplains th particular sampl log, but a nxt sampl log of th sam procss may produc a compltly diffrnt procss modl). Th four four quality dimnsions for comparing modl and log can b quantifid in various ways. S [Aalst 0; Aalst t al. 0; Adriansyah t al. 0; Munoz-Gama and Carmona 0; Rozinat and Aalst 00] for mor dtails... Conformanc Chcking Algorithms Basically, thr ar thr approachs to conformanc chcking. Th first approach is to crat an abstraction of th bhavior in th log and an abstraction of th bhavior allowd by th modl. An xampl is th notion of a footprint dscribd in Sction. of [Aalst 0]. A footprint is a matrix showing causal dpndncis btwn activitis. For xampl, th footprint of an vnt log may show that x is somtims followd by y but nvr th othr way around. If th footprint of th corrsponding modl shows that x is nvr followd by y or that y is somtims followd ACM Transactions on Managmnt Information Systms, Vol., No., Articl, Publication dat: Fbruary 0.

8 : W. van dr Aalst by x, thn th footprints of vnt log and modl disagr on th ordring rlation of x and y. Th scond approach rplays th vnt log on th modl. A naiv approach towards conformanc chcking would b to simply count th fraction of cass that can b parsd compltly (i.., th proportion of cass corrsponding to firing squncs lading from th initial stat to th final stat). This approach cannot distinguish btwn an almost fitting cas and a cas that is compltly unrlatd to th modld bhavior. A bttr approach is to continu rplaying th vnt log on th modl vn whn transitions ar not nabld. Simply borrow tokns, forc th transition to fir anyway, and rcord th problm. In th nd, th numbr of borrowd tokns and th numbr of tokns lft bhind (not consumd) indicat th fitnss lvl. S [Rozinat and Aalst 00] and Sction. in [Aalst 0]. Th third, and most advancd, approach is to comput an optimal alignmnt btwn ach trac in th log and th most similar bhavior in th modl. Considr for xampl th following thr alignmnts btwn th xampl log and modl M : γ = a c d h a c d h and γ = a d c h a c h and γ = a d c f d b h a c f b h γ shows a prfct alignmnt: all movs of th trac in th vnt log (top part of alignmnt) can b followd by movs of th modl (bottom part of alignmnt). γ shows an optimal alignmnt for trac adch in th vnt log and modl M. Th first mov of th trac in th vnt log can b followd by th modl (vnt a). Howvr, in th scond position of th alignmnt, w s a mov of th trac in th vnt log which cannot b mimickd by th modl. This mov in just th log is dnotd as (d, ). γ shows an optimal alignmnt for trac adcfdbh in th vnt log and modl M. Hr, w ncountr two situations whr log and modl cannot mov togthr. Also not th mov (f, f ), i.., vnt f in th log corrsponds to th xcution of transition f. Alignmnts γ and γ clarly show th rasons for non-conformanc btwn modl and log. Such problms can asily b quantifid as shown in [Aalst t al. 0; Adriansyah t al. 0]. Conformanc can b viwd from two angls: (a) th modl dos not captur th ral bhavior ( th modl is wrong ) and (b) rality dviats from th dsird modl th vnt log is wrong ). Th first viwpoint is takn whn th modl is supposd to b dscriptiv, i.., captur or prdict rality. Th scond viwpoint is takn whn th modl is normativ, i.., usd to influnc or control rality.. ENHANCEMENT It is also possibl to xtnd or improv an xisting procss modl using th vnt log. A non-fitting procss modl can b corrctd using th diagnostics providd by th alignmnt of modl and log. Morovr, vnt logs may contain information about rsourcs, timstamps, and cas data. For xampl, an vnt rfrring to activity rgistr rqust and cas may also hav attributs dscribing th prson that rgistrd th rqust (.g., John ), th tim of th vnt (.g., 0-0-0:. ), th ag of th customr (.g., ), and th claimd amount (.g., 0 uro ). Aftr aligning modl and log it is possibl to rplay th vnt log on th modl. Whil rplaying on can analyz ths additional attributs. For xampl, it is possibl to analyz waiting tims in-btwn activitis. Simply masur th tim diffrnc btwn causally rlatd vnts and comput basic statistics such as avrags, variancs, and confidnc intrvals. This way it is possibl to idntify th main bottlncks [Aalst 0]. Information about rsourcs can b usd to discovr rols, i.., groups of popl frquntly xcuting rlatd activitis. Hr, standard clustring tchniqus can b usd. ACM Transactions on Managmnt Information Systms, Vol., No., Articl, Publication dat: Fbruary 0.

9 Procss Mining: Ovrviw and Opportunitis : It is also possibl to construct social ntworks basd on th flow of work and analyz rsourc prformanc (.g., th rlation btwn workload and srvic tims). S [Song and Aalst 00] for an ovrviw of various procss mining tchniqus analyzing th organizational prspctiv basd on vnt logs. Standard classification tchniqus can b usd to analyz th dcision points in th procss modl [Rozinat and Aalst 00]. For xampl, activity ( dcid ) has thr possibl outcoms ( pay, rjct, and rdo ). Using th data known about th cas prior to th dcision, w can construct a dcision tr xplaining th obsrvd bhavior. Procss mining is not rstrictd to offlin analysis and can also b usd for prdictions and rcommndations at runtim. For xampl, th compltion tim of a partially handld customr ordr can b prdictd using a discovrd procss modl with timing information [Aalst t al. 0].. PROCESS MINING MANIFESTO Th IEEE Task Forc on Procss Mining rcntly rlasd a manifsto dscribing guiding principls and challngs [TFPM 0]. Th manifsto aims to incras th visibility of procss mining as a nw tool to improv th (r)dsign, control, and support of oprational businss procsss. It is intndd to guid softwar dvloprs, scintists, consultants, and nd-usrs. Bfor summarizing th manifsto, w brifly introduc th task forc... Task Forc on Procss Mining Th growing intrst in log-basd procss analysis motivatd th stablishmnt of th IEEE Task Forc on Procss Mining. Th goal of this task forc is to promot th rsarch, dvlopmnt, ducation, and undrstanding of procss mining. Th task forc was stablishd in 00 in th contxt of th Data Mining Tchnical Committ of th Computational Intllignc Socity of th IEEE. Mmbrs of th task forc includ rprsntativs of mor than a dozn commrcial softwar vndors (.g., Pallas Athna, Softwar AG, Futura Procss Intllignc, HP, IBM, Fujitsu, Infosys, and Fluxicon), tn consultancy firms (.g., Gartnr and Dloitt) and ovr twnty univrsitis. Concrt objctivs of th task forc ar: to mak nd-usrs, dvloprs, consultants, managrs, and rsarchrs awar of th stat-of-th-art in procss mining, to promot th us of procss mining tchniqus and tools, to stimulat nw procss mining applications, to play a rol in standardization fforts for logging vnt data, to organiz tutorials, spcial sssions, workshops, panls, and to publish articls, books, vidos, and spcial issus of journals. For xampl, in 00 th task forc standardizd XES (www.xs-standard.org), a standard logging format that is xtnsibl and supportd by th OpnXES library (www.opnxs.org) and by tools such as ProM, XESam, Nitro, tc. S for rcnt activitis of th task forc... Guiding Principls As with any nw tchnology, thr ar obvious mistaks that can b mad whn applying procss mining in ral-lif sttings. Thrfor, th six guiding principls listd in Tabl I aim to prvnt usrs/analysts from making such mistaks. As an xampl, considr Guiding Principl GP: Evnts Should B Rlatd to Modl Elmnts. It is a misconcption that procss mining is limitd to control-flow discovry, othr prspctivs such as th organizational prspctiv, th tim prspctiv, and th data prspctiv ar qually important. Howvr, th control-flow prspctiv (i.., th ordring of activitis) srvs as th layr conncting th diffrnt prspctivs. Thrfor, it is important to rlat vnts in th log to activitis in th modl. Conformanc chcking and modl nhancmnt havily rly on this rlationship. Using Fig., w showd for xampl alignmnt γ which rlats obsrvd trac adcfdbh to firing s- ACM Transactions on Managmnt Information Systms, Vol., No., Articl, Publication dat: Fbruary 0.

10 :0 W. van dr Aalst GP GP GP GP GP GP Tabl I. Six Guiding Principls Listd in th Manifsto Evnt Data Should B Tratd as First-Class Citizns Evnts should b trustworthy, i.., it should b saf to assum that th rcordd vnts actually happnd and that th attributs of vnts ar corrct. Evnt logs should b complt, i.., givn a particular scop, no vnts may b missing. Any rcordd vnt should hav wll-dfind smantics. Morovr, th vnt data should b saf in th sns that privacy and scurity concrns ar addrssd whn rcording th vnt log. Log Extraction Should B Drivn by Qustions Without concrt qustions it is vry difficult to xtract maningful vnt data. Considr, for xampl, th thousands of tabls in th databas of an ERP systm lik SAP. Without qustions on dos not know whr to. Concurrncy, Choic and Othr Basic Control-Flow Constructs Should b Supportd Basic workflow pattrns supportd by all mainstram languags (.g., BPMN, EPCs, Ptri nts, BPEL, and UML activity diagrams) ar squnc, paralll routing (AND-splits/joins), choic (XOR-splits/joins), and loops. Obviously, ths pattrns should b supportd by procss mining tchniqus. Evnts Should B Rlatd to Modl Elmnts Conformanc chcking and nhancmnt havily rly on th rlationship btwn lmnts in th modl and vnts in th log. This rlationship may b usd to rplay th vnt log on th modl. Rplay can b usd to rval discrpancis btwn vnt log and modl (.g., som vnts in th log ar not possibl according to th modl) and can b usd to nrich th modl with additional information xtractd from th vnt log (.g., bottlncks ar idntifid by using th timstamps in th vnt log). Modls Should B Tratd as Purposful Abstractions of Rality A modl drivd from vnt data provids a viw on rality. Such a viw should srv as a purposful abstraction of th bhavior capturd in th vnt log. Givn an vnt log, thr may b multipl viws that ar usful. Procss Mining Should B a Continuous Procss Givn th dynamical natur of procsss, it is not advisabl to s procss mining as a ontim activity. Th goal should not b to crat a fixd modl, but to brath lif into procss modls such that usrs and analysts ar ncouragd to look at thm on a daily basis. qunc acf bh in M. Aftr rlating vnts to modl lmnts, it is possibl to rplay th vnt log on th modl [Aalst 0]. Rplay may b usd to rval discrpancis btwn an vnt log and a modl,.g., som vnts in th log ar not possibl according to th modl. Tchniqus for conformanc chcking can b usd to quantify and diagnos such discrpancis. Timstamps in th vnt log can b usd to analyz th tmporal bhavior during rplay. Tim diffrncs btwn causally rlatd activitis can b usd to add avrag/xpctd waiting tims to th modl. Ths xampls illustrat th importanc of guiding principl GP; th rlation btwn vnts in th log and lmnts in th modl srvs as a ing point for diffrnt typs of analysis... Challngs Procss mining is an important tool for modrn organizations that nd to manag non-trivial oprational procsss. On th on hand, thr is an incrdibl growth of vnt data. On th othr hand, procsss and information nd to b alignd prfctly in ordr to mt rquirmnts rlatd to complianc, fficincy, and customr srvic. Dspit th applicability of procss mining thr ar still important challngs that nd to b addrssd; ths illustrat that procss mining is an mrging disciplin. Tabl II lists th lvn challngs dscribd in th manifsto [TFPM 0]. As an xampl considr Challng C: Daling with Concpt Drift. Th trm concpt drift rfrs to th situation in which th procss is changing whil bing analyzd [Bos t al. 0]. For instanc, in th bginning of th vnt log two activitis may b concurrnt whras latr in th log ths activitis bcom squntial. Procsss may chang du to priodic/sasonal changs (.g., in Dcmbr thr is mor dmand or on Friday aftrnoon thr ar fwr mploys availabl ) or du to changing condi- ACM Transactions on Managmnt Information Systms, Vol., No., Articl, Publication dat: Fbruary 0.

11 Procss Mining: Ovrviw and Opportunitis : C C C C C C C C C C0 C Tabl II. Som of th Most Important Procss mining Challngs Idntifid in th Manifsto Finding, Mrging, and Claning Evnt Data Whn xtracting vnt data suitabl for procss mining svral challngs nd to b addrssd: data may b distributd ovr a varity of sourcs, vnt data may b incomplt, an vnt log may contain outlirs, logs may contain vnts at diffrnt lvl of granularity, tc. Daling with Complx Evnt Logs Having Divrs Charactristics Evnt logs may hav vry diffrnt charactristics. Som vnt logs may b xtrmly larg making thm difficult to handl whras othr vnt logs ar so small that not nough data is availabl to mak rliabl conclusions. Crating Rprsntativ Bnchmarks Good bnchmarks consisting of xampl data sts and rprsntativ quality critria ar ndd to compar and improv th various tools and algorithms. Daling with Concpt Drift Th procss may b changing whil bing analyzd. Undrstanding such concpt drifts is of prim importanc for th managmnt of procsss. Improving th Rprsntational Bias Usd for Procss Discovry A mor carful and rfind slction of th rprsntational bias is ndd to nsur highquality procss mining rsults. Balancing Btwn Quality Critria such as Fitnss, Simplicity, Prcision, and Gnralization Thr ar four compting quality dimnsions: (a) fitnss, (b) simplicity, (c) prcision, and (d) gnralization. Th challng is to find modls that scor good in all four dimnsions. Cross-Organizational Mining Thr ar various us cass whr vnt logs of multipl organizations ar availabl for analysis. Som organizations work togthr to handl procss instancs (.g., supply chain partnrs) or organizations ar xcuting ssntially th sam procss whil sharing xprincs, knowldg, or a common infrastructur. Howvr, traditional procss mining tchniqus typically considr on vnt log in on organization. Providing Oprational Support Procss mining is not rstrictd to off-lin analysis and can also b usd for onlin oprational support. Thr oprational support activitis can b idntifid: dtct, prdict, and rcommnd. Combining Procss Mining With Othr Typs of Analysis Th challng is to combin automatd procss mining tchniqus with othr analysis approachs (optimization tchniqus, data mining, simulation, visual analytics, tc.) to xtract mor insights from vnt data. Improving Usability for Non-Exprts Th challng is to hid th sophisticatd procss mining algorithms bhind usr-frindly intrfacs that automatically st paramtrs and suggst suitabl typs of analysis. Improving Undrstandability for Non-Exprts Th usr may hav problms undrstanding th output or is tmptd to infr incorrct conclusions. To avoid such problms, th rsults should b prsntd using a suitabl rprsntation and th trustworthinss of th rsults should always b clarly indicatd. tions (.g., th markt is gtting mor comptitiv ). Such changs impact procsss and it is vital to dtct and analyz thm [Bos t al. 0].. PROCESS MINING IN PRACTICE Although th manifsto lists many opn challngs, xisting procss mining tchniqus can asily b applid in practic. At TU/ (Eindhovn Univrsity of Tchnology) w hav applid procss mining in ovr 00 organizations. To hlp th radr to gt d with procss mining, w brifly discuss tool support and show two cas studis takn from [Aalst 0]... Tool Support Th opn-sourc tool ProM has bn th d-facto standard for procss mining during th last dcad. Procss discovry, conformanc chcking, social ntwork analysis, organizational mining, dcision mining, history-basd prdiction and rcommndation, ACM Transactions on Managmnt Information Systms, Vol., No., Articl, Publication dat: Fbruary 0.

12 : W. van dr Aalst tc. ar all supportd by ProM [Aalst 0; Vrbk t al. 00]. For xampl, dozns of diffrnt procss discovry algorithms ar supportd by ProM. Th functionality of ProM is unprcdntd, i.., thr is no product offring a comparabl st of procss mining algorithms. Howvr, th tool rquirs procss mining xprtis and is not supportd by a commrcial organization. Hnc, it has th advantags and disadvantags common for opn-sourc softwar. Fortunatly, thr is also a growing numbr of commrcially availabl softwar products offring procss mining capabilitis. Exampls ar: ARIS Procss Prformanc Managr (Softwar AG), Comprhnd (Opn Connct), Discovry Analyst (Stro- LOGIC), Flow (Fourspark), Futura Rflct (Futura Procss Intllignc), Intrstag Automatd Procss Discovry (Fujitsu), Procss Discovry Focus (Iontas/Vrint), ProcssAnalyzr (QPR), and Rflct on (Pallas Athna). All of th products mntiond support procss discovry, i.., constructing a procss modl basd on an vnt log. For xampl, Futura Rflct supports gntic procss mining as dscribd in [Mdiros t al. 00]. Som of th systms mntiond hav difficultis discovring concurrncy,.g., ARIS Procss Prformanc Managr, Flow, and Intrstag Automatd Procss Discovry. All systms tak th timstamps in th vnt log into account to b abl to provid prformanc-rlatd information, i.., flow tims and bottlncks can b discovrd. Non of th commrcial softwar products provids comprhnsiv support for conformanc chcking, i.., th focus is on procss discovry and prformanc masurmnt. Howvr, ProM supports th diffrnt typs of conformanc chcking dscribd in Sction.. Som of ths products mbd procss mining functionality in a largr systm,.g., Pallas Athna mbds procss mining in thir BPM suit BPM on. Othr products aim at simplifying procss mining using an intuitiv usr intrfac... Discovring Spaghtti Procsss Thr is a continuum of procsss ranging from highly structurd procsss (Lasagna procsss) to unstructurd procsss (Spaghtti procsss). Figur shows why unstructurd procsss ar oftn calld Spaghtti procsss. Th modl was obtaind using ProM s huristic minr [Wijtrs and Aalst 00]. Hnc, low frqunt bhavior has bn filtrd out. Nvrthlss, th modl is too difficult to comprhnd. Not that this is not ncssarily a problm of th discovry algorithm. Activitis ar only connctd if thy frquntly followd on anothr in th vnt log. Hnc, th complxity shown in Fig. rflcts rality and is not causd by th discovry algorithm. Figur is an xtrm xampl usd to illustrat th charactristics of a typical Spaghtti procss. Givn th data st it is not surprising that th procss is unstructurd; th patints did not form a homognous group and includd individuals with vry diffrnt mdical problms. Th procss modl in Fig. can b simplifid dramatically by slcting a group of patints with similar problms or by slcting only th most frqunt activitis. Nvrthlss, its complxity xmplifis som of th challngs mntiond in th manifsto (in particular C, C, C, C0, and C)... Analyzing Lasagna Procsss Procsss in municipalitis ar typically Lasagna procsss. Figur shows a so-calld WOZ procss discovrd for a Dutch municipality. W applid th huristic minr [Wijtrs and Aalst 00] on an vnt log containing information about objctions against th so-calld WOZ ( Waardring Onrornd Zakn, i.., Valuation of Ral Estat) valuation. Dutch municipalitis nd to stimat th valu of houss and apartmnts. Th WOZ valu is usd as a basis for dtrmining th ral-stat proprty tax. Th highr th WOZ valu, th mor tax th ownr nds to pay. Thrfor, Dutch mu- ACM Transactions on Managmnt Information Systms, Vol., No., Articl, Publication dat: Fbruary 0.

13 Procss Mining: Ovrviw and Opportunitis : B_Cathtr a Dmur 0 O_ECG daglijks 0, B_Halsinf./subclavia op OK 0, O_ECG op aanvraa g 0, B_Drain(s) wond 0, B_Doorbwgn 0, B_Wondzorg opn bui k B_Nfrostomi cathtr L O_Bnzodiazpins O_CT-schdl 0 B_Primo luchtmatras O_X arm B_Supra Pubisch blaascat h B_Oogglazn B_Dcubitus zorg stadium B_Dcubitus zorg stadium a B_Urtr cathtr L B_Dcubitus zorg stadium b B_Halsinf./subclavia op Ok B_Maagsond 0 0, B_Prifr infuus 0, 0 B_Wisslligging 0 0, C_-Asystoli 0, B_Bi-PAP B_Vrwijdrn Agravs B_IPPB B_Vrband spal k B_Uro stoma B_Badming 0, 00 B_Cathtr a dmur 0, B_Wann 0, 00 B_Trachostomi - prcutaan B_Rintubati 0 B_Dfibrilati B_Orthopadisch tracti C_Rsp Insuff B_Pacmakr inbrngn B_Thoraxdrain 0, O_X-thorax daglijks 0 0, B_Prifr infuus 0,0 0, B_Swan Ganz op OK 0, B_Drain(s) rdon 0 0, O_EMV scor 0 O_Echo nir blaas prostaat B_PCA pomp C_s Shock, Sptisch O_Toxicologi O_Transthoracaal ECHO C_Dcubitus stuit st. a C_Flbitis 0, B_Drain gol f O_Plura vocht kwk B_Plura Puncti C_Subcutaan mfysm 0, B_Basiszorg 00 0, O_Wgn x pr wk 0, B_Badming 0, B_Prifr infuus 0,0 B_Artri lijn op OK 0 0 M_MasurmntChmistry 0, O_ECG cito B_Mdium car B_Pacmakr standby 0,0 M_MasurmntDcubitus 0, 0 B_Cathtr piduraal 0 0, O_Wond inspcti B_IABP in op OK O_CT thorax B_CAPD B_Artri lijn op OK 00 0, 0, B_Pacmakr AAN 0, C_Shock, Anaphylactisch B_Isolati strikt B_Actif koln C_Stridor C_Platzbauch M_MasurmntBloodGas B_Actif warmt tovogn B_O maskr/nusslang 0, C_Bactrimi O_Blodkwk 0, B_Bi of Trilumn Cathtr 0 C_-VT B_Artri lijn op ICU 0, B_Prifr infuus 0 C_-Asystoli 0, 0 B_Bronchiaal toilt C_Trombopni C _ C V A 0, C_Pnumoni (klinisch) O _ B EE 0, 0 C _ A R D S C_Psychos/vrward O_Wgn x pr wk 0, B_Cardiovrsi 0 B_Bzok: afw. tijdn 0 O_Vancomycin dal / top 0 B_Minitrachotomi B_Minitrachotomi B_Mdium car 0 0, 0 B_Artri lijn op ICU 0 C_Sufhid C_Anuri (<ml/kg/u) C_Ischmi, Myocard 0 O_SDD klkwk Ma/Do 0, C_MI zkr B_Extubati 0 0, B_Cathtr a Dmur 0 B_Liscathtr(s) 0 O_Wond kwk B_Halsinf./subclavia op IC O_ECHO Buik 0, O_ E EG C_Bloding waarvoor rok O_Gntamycin dal / top C_Oliguri (< ml/kg/u) 0 C_Badmingsafhanklijkhi d B_Drain(s) sump O_CT-buik B_Blodtodining mt druk B_Oogzalvn / druppln B_Drain(s) wond B_Fixatur Extrn C_Hmi-bld C_-VKF, atrium-fluttr C_DIS C_Rsp Insuff B_Basiszor g O_Pulmonalis angi o C_Fbris.c.i. O_Coronair angiogram B_PTCA B_Liscathtr(s) B_Vrnvlaar O _ T EE C_Non oligurisch nirinsu f 0 B_Air fluid bd 0, B_Halsinf./subclavia op I C 0 C_Autoxtubati 0 O_X bn C_Pnumothorax B_Vrplgvorm boomstam C_Para-valvulair lk na OK C_Bronchitis (klinisch) 0 C_Acut Tubulus Ncros B_CVVH 0, B_Intrmit. cathtrisrn 0,0 C_Pancratitis C_Bronchitis -purulnt B_Trachostoma/Tub LOS 0, O_Kwk art. lijn B_Duo luchtmatras 0, C_Lijn spsis O_Kwk liscathtr vnu s 0 0, C_Dprssi B_Uritip O_ECG x p.w. 0 B_Clysmrn B_IABP in op OK C_MI moglijk C_MI moglijk C_-SVT, paroxysmaa l 0, B_Low flow bd B_Low flow bd 0 0 B_Trachostomi O_Kwk pritonum O_Kl kwk C_Ictrus (bili > 0 ) O_Tobramycin dal / top C_s Shock, Hypovolamisch O_Sigmoidoscopi C_Empym C_Urinwginfcti O_Echo prifr vatn B_Buikligging B_Primo luchtmatras C_Lkkag na plastik C_Dcubitus hak st. a C_-VF C_Hypoglycami 0 B_Jjunumsond C_Hyprglycami >0mmol/l C_Subcutaan mfysm C_Fistl bovnst tr di g C_Darmprforati B_Vacuum thrapi 0 O_Fundus scopi O_Fundus scopi B_Wondzorg opn bui k 0 C_Hpatitis, drug inducd C_Hypoglycami B_Badming Nit Invasi f B_Badming Nit Invasif C_Rhabdomyolysis B_CAVH(D) B_CAVH(D) C_Aspirati B_Buikligging O_ uurs urin Na Crat U r O_Kwk prifr infuus C_Abcs B_Isolati strikt C_Critical illnss polynu r B_Actif koln O_Huiduitstrijk Oksl Li /R C_Hypoxmi C_Ischmisch hpatiti s C_Candidosis invasif C_GI-bloding 0, C_Dcubitus ovrig st. C_Autoxtubati B_Pacmakr inbrngn C_Dcubitus stuit st. C_Ischmisch darm C_Pnumoni (moglijk) B_PEP maskr C_Naadlkkag C_Lijnkwk positif C_Nosocomial Pnumoni C_Log Syndroom B_Fasciotomi B_Fasciotomi C_Trombopni C_GI-bloding C_Pnumoni B_NO badming C_Tamponad C_Maagrtnti(>00 ml/) C_Badmingsafhanklijkhid B_Isolati arogn B_Isolati arogn C_Plistrlasi B_Ncrotomi C_Platzbauch C_Pritonitis C_Gn plaats af d B_Empym spoling O_Mthyl blauw/ fistulogram C_Plura-Effusi C_Colitis, psudommbranus C_Parotitis B_IPPB B_Wondzorg opn thorax C_Coma B_Uritip B_Isolati Univrsl C _ A R D S C_Hyprglycami >0mmol/ l B_Plasmafors C_TIA C_Cholcystitis, acalc B_Dcubitus zorg stadium b C_Hamolys B_Dcubitus zorg stadium b C_Intra-pritonaal Abcs B_Supra Pubisch blaascat h B_Vrplgvorm prikklarm 0 B_Actif warmt tovogn 0 0, B_Sclrosrn GI blodin g B_PEG catht r B_Donor Multi Orgaan 0, 0, 0 O_ECG daglijks 0, C_Ischmi O_Wgn daglijks C_Hypotnsi B_Trachostomi C_Blodvrlis > 0 ml/uur B_Empym spoling C_s Shock, Hypovolamisch B_Ontlastnd LP bij druk B_Cathtr spinaal B_Thoraxdrain 0, B_Cathtr a dmur C_Darmprforati O_Lab. x pr wk B_Ranimati 0 0, B_IABP in op ICU C_Blodvrlis > 0 ml/uur 0 B_Swan Ganz op IC U B_Wondzorg ovrig B_Rthoratocomi op OK B_Amputati Extrmitit B_Isolati contact B_PEP maskr C_Psychos/vrwar d 0, B_Halsinf./subclavia op OK 0 B_Pacmakr AAN 0, O_Dopplr prifr vatn B_Bi of Trilumn Catht r B_PCA pomp C_s Shock, Cardiaal C_Sufhid C_Lkkag na plastik M_MasurmntClinic 0, 0, O_X-thorax cito 0 0, B_Trachostomi - prcutaan 0 C_s Shock, Cardiaal 0, O_SDD / SOD studi 0 O_Dopplr prifr vatn C_Bloding waarvoor > PC B_Wisslligging C_Dcompnsati na OK C_Strnumwondinfcti C_-Prmatur Slagn NNO B_Laparotomi 0 B_Jjunostomi C_Tamponad B_Plura Puncti B _ C P A P B_Isolati druppl C_Hmorrhoidn blodnd C_Ischmi waarvoor R OK C_Endocarditis C_Cholcystitis, stnn C_Thrombo-mboli art C_Postanox ncfalopa t O_Fnytoin B_Dcubitus zorg stadium B_Dcubitus bhandling B_Isolati Univrsl C_-VKF, atrium-flutt r 0, 0, C_Ilus B_Isolati druppl O_Lithium C_Atlctas B_Vacuum thrapi B_Vrband spal k B_Dcubitus bhandling O_BAL / Lavag C_Lucopni O_Ascits kwk O_Coloscopi C_Pustuluz af w O_Liquor kwk C_N Phrnicus Paralys 0, 0, 0, 0, B_Fysiothrapi 0, 0, B_Mobilisrn 0, 0 O_Gastro / Duodnscopi B_Bi-PAP B_Vrplgvorm boomstam B_Vrplgvorm prikklarm O_Virus srologi B_Vrband gips C_Dcubitus stuit st. a O_Paractamol B_Isolati Bschrmnd 0, 0 0, 0, B_Wondzorg opn thorax 0 O_ECG cito O_X-thorax cito 0, O_Blodkwk 0, 0 O_Blodkwk 0 0, O_Cito GRAM + sputumkwk 0, O_Kwk bi/tri lumn cath. O_Blodkwk O_Blodkwk 0, 0 O_Sputum kwk 0, O_Facs kwk 0, O_Cito GRAM + bronchuskw k 0, O_Kwk urincathtr 0 O_Wgn daglijks 0, O_Synacthn C_s Shock, Sptisc h C_Myoclonin C_Dwarslasi O_Cito GRAM + sputumkw k 0, O_Blodkwk 0 0, 0 O_Kl kwk B_Ranimati B_IABP in op ICU B_Ballonnrn B_Sond-Voding 0, B_Anus Pratr Naturalis B_Blodtodining mt druk O_Kwk shat h B_Swan Ganz op ICU 0, B_PTCA O_Ramsay-scor B_Dcubitus zorg stadium a C_Polyuri (>0ml/kg/u) 0 C_-VT 0, B_IABP uit op ICU B_PEG cathtr C_-Brady / Aritmi B_O maskr/nusslan g B_Badming g op ICU 0 B_NO badming B_Ontlastnd LP bij druk O_Urin kwk 0, 0 O_Urin kwk 0, 0 O_Bnzodiazpins O_Kwk swan ganz O_Kwk ovrig O_Kwk ovrig 0, 0, 0 0, O_Wond kwk O_Sigmoidoscopi O_Kwk prifr infuus C_Exanthm / Rash B_Duo luchtmatras B_Wondzorg ovrig 0 C_Dcubitus hak st. 0, B_Isolati contact B_Sclrosrn GI bloding B_Pacmakr standb y 0 C_-Brady / Aritmi B_Swan Ganz op OK 00 B_Vrnvlaar 0, B_Bzok: afw. tijdn 0 0,0 B_Bzok: wakn B_R OK O_X-thorax x p.w. B_Bzok: kind. togstaan 0, 0,0 B_Wann O_X TWK C_Wondinfcti B_Drain gol f B_Trachostoma/Tub LOS O_IAP studi 0, 0 O_Lab. x pr wk 0 O_Cystoscopi 0, B_Mobilisrn C_Oliguri (< ml/kg/u) C_Fibro-prolifrativ ARDS C_-SVT, paroxysmaa l O _ B EE 0 0, O_EMV scor 0, 0, O_Pulmonalis angi o C_Diabts Insipids C_Convulsi(s) O_Sputum kwk 0 0, O_Kwk pritonum O_Virus srologi O_I.V Cathtr kwk ovrig 0, 0 C_s Shock, Onbkn d C_Bronchitis (moglijk) O_Huiduitstrijk Oksl Li / R O_Ascits kwk B_Isolati bschrmnd 0 B_Anus Pratr Naturalis 0, 0 O_X-thorax op aanvraa g O_X-thorax daglijks 0,0 O_ECG x p.w. 0,0 B_Cardiovrsi 0 O_Mthyl blauw/ fistulogram O_ECG op aanvraag B_IABP uit op ICU O_Facs kwk 0 0, 0 C_Candida kolonisati 0, O_Lumbaal Puncti O_Vancomycin dal / top B_Ballonnrn B_Bronchiaal toilt 0,0 B_Sond-Voding 0, B_Cathtr piduraal B_Intrmit. Hamo Dialys O_X-thorax op aanvraag O_Coloscopi B_Drain(s) sump B _ C P A P 0 B_CVVH 0,0 0,0 O_kwk pacmakrdraad O_Ramsay-scor O_X-thorax x p.w. 0, C_Dcompnsati gn OK C_Hpatitis, drug inducd C_Ischmi, Myocard O_SDD rctumkwk Ma/Do 00 0, O_SDD sputumkwk Ma/Do 0, O_SDD rctumkwk Ma/Do O_SDD sputumkwk Ma/Do 0, O_SDD klkwk Ma/Do 0 0, 0 O_SDD / SOD studi 0, 0 C_Longbloding 0, 0 B_Orthopadisch tracti B_Dcubitus zorg stadium a B_Extubati 0, 0, 0, O_Lithium O_IAP studi 0,0 C_Intra-pritonaal Abcs B_Maagsond B_Doorbwgn 0 C_Hyprtnsi B_Dcubitus zorg stadium a 0, 0, B_Drain(s) rdon 0 B_Ncrotomi 0, 0, 0 0, 0, 0, 0, B_Fysiothrapi B_Plasmafors B_Intrmit. cathtrisrn 0, B_Blaasspoln B_Intrmit. Hamo Dialys B_Blaasspoln B_ E R C P 0, 0 0, 0 0, C_Lvrfaln O_ECHO Bui k 0, O_Echo prifr vatn B_Oogzalvn / druppln 0 B_Bzok: wakn O_Bronchoscopi O_Gastro / Duodnscopi 0, 0 O_Plurapuncti O_Bronchoscopi 0,0 O_Trachaspoling O_ E E G 0 C_Addisson / Bijnir Insuff C_Acut Lung Injury 0 O_Trachaspoling B_Jjunumsond C_Bloding waarvoor > PC 0, C_Bloding waarvoor rok B_R OK 0 0 0, B_Brochusscopi B_Intubati O_Gntamycin dal / top 0, 0, 0 C_Rthoratocomi 0, B_Dcubitus zorg stadium b O_CT-buik 0, O_CT-schdl C_Pancratitis 0, 0, 0 O_Kwk trachostoma 0, 0, B_Vrwijdrn tampon O_Kwk trachostoma B_Vrwijdrn tampon B _ E R C P 0, B_Jjunostomi B_Nfrostomi cathtr R C_Aspirati C_s Shock, Onbkn d B_Rintubati 0, B_Intubati 0 C_Stridor B_Rintubati na Autoxt 0 B_Vrwijdrn Agravs C_Shock, Anaphylactisch O_Cito GRAM + bronchuskw k 0, 0 B_Brochusscopi C_Atlctas O_Sinus kwk O_Sinus kwk O_Coronair angiogram B_Amputati Extrmiti t O_CT bkkn O_X bn B_Badming g op ICU B_Oogglazn C_Dhiscnti O _ T E E 0, O_Synacthn 0, B_Air fluid bd 0, 0 0, 0,0 0 C_rOK ivm pluravoch t B_Rintubati na Autoxt 0, O_X TWK O_X arm C_Pnumothorax 0, O_Kwk shat h 0 0, O_kwk pacmakrdraad O_Kwk bi/tri lumn cath. 0, O_Kwk liscathtr ar t 0, C_Dcubitus ovrig st. b O_X b.o.z. 0 O_X b.o.z O_Kwk art. lij n 0, O_Kwk liscathtr ar t B_Dcubitus zorg stadium a O_Digoxin O_Kwk liscathtr vnu s C_Nosocomial Pnumoni B_Clysmrn 0 B_Rthoratocomi op OK 0 0 B_Nfrostomi cathtr L B_Halsinf./subclavia op Ok O_Echo nir blaas prostaat 0, 0 B_Laparotomi 0, C_Naadlkkag O_Tobramycin dal / top 0, 0, O_Kwk swan ganz O_Plura vocht kwk O_Kwk urincathtr O_I.V Cathtr kwk ovrig 0, C_Candidami 0, 0 C_Rthoratocomi C_Dcubitus stuit st. b O_Cystoscopi O_CT thorax O_Plurapuncti B_Dfibrilati C_-VF 0 0,0 O_Wond inspcti C_Addisson / Bijnir Insuff 0,0 C_Bactrimi C_Empym C_Bronchitis (klinisch ) C_Dcompnsati na OK C_Anuri (<ml/kg/u) C_Ischmi waarvoor R OK O_Lumbaal Puncti O_Toxicologi O_ uurs urin Na Crat U r B_T drain O_BAL / Lavag O_Biopsi O_Biopsi C_Thrombo-mboli art O_Transthoracaal ECHO 0 0 O_Paractamol C_Convulsi(s) B_Donor Wfsl B_Urtr cathtr R B_Dcubitus zorg stadium a C_Dcubitus hak st. a C_Dcubitus ovrig st. a C_Hypotnsi 0, O_Fnytoin O_Liquor kwk C_Colitis, psudommbranu s C_Lijn spsis B_Horizontaal B_Horizontaal C_Druk ncros ldr s B_Isolati Bschrmnd O_Digoxin Fig.. Spaghtti procss dscribing th diagnosis and tratmnt of patints in a Dutch hospital. Th procss modl was constructd basd on an vnt log containing, vnts. Thr ar diffrnt activitis (taking vnt typs into account) xcutd by diffrnt individuals (doctors, nurss, tc.). nicipalitis nd to handl many objctions (i.., appals) of citizns that assrt that th WOZ valu is too high. Figur shows th procss of handling ths objctions within a particular municipality. Th diagram is not intndd to b radabl; it is only includd to show th contrast with Fig.. Domain: hus complt OZ0 Voorbridn OZ0 Voorbridn complt OZ0 Stop vordring OZ0 Stop vordring complt OZ Zlf uitspraak OZ Zlf uitspraak complt OZ0 Administati OZ0 Administati complt OZ Start vordring OZ Start vordring complt OZ0 Boordln OZ0 Boordln complt OZ Hrtaxrn OZ Hrtaxrn complt OZ Uitspraak OZ Uitspraak complt OZ0 Incomplt OZ0 Incomplt complt OZ0 Horn OZ0 Horn complt OZ0 Wacht Boord OZ0 Wacht Boord complt OZ Uitspr. wacht OZ Uitspr. wacht complt Fig.. WF-nt discovrd basd on an vnt log of a Dutch municipality. Th log contains vnts rlatd to objctions against th so-calld WOZ valuation. Ths objctions gnratd vnts. Thr ar activitis. For of ths activitis both and complt vnts ar rcordd. Hnc, th WF-nt has transitions. Th discovrd WF-nt has a good fitnss: of th cass can b rplayd without ncountring any problms. Th fitnss of th modl and log at th vnt lvl is 0.. This valu is basd on th approach dscribd in [Aalst 0; Rozinat and Aalst 00]. Th high valu shows that almost all rcordd vnts ar xplaind by th modl. Hnc, th WOZ procss is clarly a Lasagna procss. Nvrthlss, it was intrsting for th municipality to s th dviations highlightd in th modl. Figur shows a fragmnt of th diagnostics providd by th ProM s conformanc chckr. Th municipality s log contains timstamps. Thrfor, it is possibl to rplay th vnt log whil taking th timstamps into account. ProM can visualiz th phass of th procss that tak most tim. For xampl, th plac in-btwn OZ Uitspraak ( of announcmnt of final judgmnt) and OZ Uitspraak complt (nd of announcmnt of final judgmnt) was visitd tims. Th avrag tim spnt in this plac is. days. This indicats that activity OZ Uitspraak (final judgmnt) taks about a wk. It is also possibl to simply slct two activitis and masur th tim that passs in-btwn ths activitis. On avrag 0. days pass in-btwn th compltion of activity OZ0 Voorbridn (prparation) and th compltion of OZ Uitspraak (final judgmnt). Such xampls illustrat that procss mining ACM Transactions on Managmnt Information Systms, Vol., No., Articl, Publication dat: Fbruary 0.

14 : W. van dr Aalst Fig.. Fragmnt of th WF-nt annotatd with diagnostics gnratd by ProM s conformanc chckr. Th WF-nt and vnt log fit wll (fitnss is 0.). Nvrthlss, svral low-frqunt dviations ar discovrd. For xampl. activity OZ Hrtaxrn (r-valuation of WOZ valu) is d tims without bing nabld according to th modl. unlik classical Businss Intllignc (BI) tools hlps organizations to look insid thir procsss. This is in stark contrast with contmporary BI tools that typically focus on rporting and fancy looking dashboards.. CONCLUSION This papr introducd procss mining as a nw tchnology nabling vidnc-basd procss analysis. W introducd th thr basic typs of procss mining (discovry, conformanc, and nhancmnt) using a small xampl and usd som largr xampls to illustrat th applicability in ral-lif sttings. Nvrthlss, thr ar still many opn scintific challngs and most nd-usr organizations ar not yt awar of th potntial of procss mining. This triggrd th dvlopmnt of th Procss Mining Manifsto by an intrnational task forc involving procss mining xprts rprsnting organizations. This manifsto can b obtaind from Th radr intrstd in procss mining is also rfrrd to th rcnt book on procss mining [Aalst 0]. Also visit for sampl logs, vidos, slids, articls, and softwar. ACKNOWLEDGMENTS Th author would lik to thank all that contributd to th Procss Mining Manifsto: Arya Adriansyah, Ana Karla Alvs d Mdiros, Franco Arciri, Thomas Bair, Tobias Blickl, Jagadsh Chandra Bos, Ptr van dn Brand, Ronald Brandtjn, Joos Buijs, Andra Burattin, Josp Carmona, Malu Castllanos, Jan Clas, Jonathan Cook, Nicola Costantini, Francisco Curbra, Ernsto Damiani, Massimiliano d Loni, Pavlos Dlias, Boudwijn van Dongn, Marlon Dumas, Schahram Dustdar, Dirk Fahland, Diogo R. Frrira, Walid Gaaloul, Frank van Gffn, Sukriti Gol, Christian Günthr, Antonlla Guzzo, Paul Harmon, Arthur tr Hofstd, John Hoogland, Jon Espn Ingvaldsn, Koki Kato, Rudolf Kuhn, Akhil Kumar, Marcllo La Rosa, Fabrizio Maggi, Donato Malrba, Ronny Mans, Albrto Manul, Martin McCrsh, Paola Mllo, Jan Mndling, Marco Montali, Hamid Motahari Nzhad, Michal zur Muhln, Jorg Munoz-Gama, Luigi Pontiri, Jol Ribiro, Ann Rozinat, Hugo Sgul Pérz, Ricardo Sgul Pérz, Marcos Spúlvda, Jim Sinur, Pnina Soffr, Minsok Song, Alssandro Sprduti, Giovanni Stilo, Caspr Stol, Kith Swnson, Maurizio Talamo, Wi Tan, Chris Turnr, Jan Vanthinn, Gorg Varvarssos, Eric Vrbk, Marc Vrdonk, Robrto ACM Transactions on Managmnt Information Systms, Vol., No., Articl, Publication dat: Fbruary 0.

15 Procss Mining: Ovrviw and Opportunitis : Vigo, Jianmin Wang, Barbara Wbr, Matthias Widlich, Ton Wijtrs, Liji Wn, Michal Wstrgaard, and Mo Wynn. REFERENCES AALST, W. VAN DER 0. Procss Mining: Discovry, Conformanc and Enhancmnt of Businss Procsss. Springr-Vrlag, Brlin. AALST, W. VAN DER, ADRIANSYAH, A., AND DONGEN, B. VAN 0. Rplaying History on Procss Modls for Conformanc Chcking and Prformanc Analysis. WIREs Data Mining and Knowldg Discovry. AALST, W. VAN DER, DONGEN, B., HERBST, J., MARUSTER, L., SCHIMM, G., AND WEIJTERS, A. 00. Workflow Mining: A Survy of Issus and Approachs. Data and Knowldg Enginring,,. AALST, W. VAN DER, HEE, K. VAN, HOFSTEDE, A., SIDOROVA, N., VERBEEK, H., VOORHOEVE, M., AND WYNN, M. 0. Soundnss of Workflow Nts: Classification, Dcidability, and Analysis. Formal Aspcts of Computing,,. AALST, W. VAN DER, REIJERS, H., WEIJTERS, A., DONGEN, B. VAN, MEDEIROS, A., SONG, M., AND VER- BEEK, H. 00. Businss Procss Mining: An Industrial Application. Information Systms,,. AALST, W. VAN DER, RUBIN, V., VERBEEK, H., DONGEN, B. VAN, KINDLER, E., AND GÜNTHER, C. 00. Procss Mining: A Two-Stp Approach to Balanc Btwn Undrfitting and Ovrfitting. Softwar and Systms Modling,,. AALST, W. VAN DER, SCHONENBERG, M., AND SONG, M. 0. Tim Prdiction Basd on Procss Mining. Information Systms,, 0. AALST, W. VAN DER AND STAHL, C. 0. Modling Businss Procsss: A Ptri Nt Orintd Approach. MIT prss, Cambridg, MA. AALST, W. VAN DER, WEIJTERS, A., AND MARUSTER, L. 00. Workflow Mining: Discovring Procss Modls from Evnt Logs. IEEE Transactions on Knowldg and Data Enginring,,. ADRIANSYAH, A., DONGEN, B. VAN, AND AALST, W. VAN DER 0. Conformanc Chcking using Cost- Basd Fitnss Analysis. In IEEE Intrnational Entrpris Computing Confrnc (EDOC 0), C. Chi and P. Johnson, Eds. IEEE Computr Socity,. AGRAWAL, R., GUNOPULOS, D., AND LEYMANN, F.. Mining Procss Modls from Workflow Logs. In Sixth Intrnational Confrnc on Extnding Databas Tchnology. Lctur Nots in Computr Scinc Sris, vol.. Springr-Vrlag, Brlin,. BERGENTHUM, R., DESEL, J., LORENZ, R., AND MAUSER, S. 00. Procss Mining Basd on Rgions of Languags. In Intrnational Confrnc on Businss Procss Managmnt (BPM 00), G. Alonso, P. Dadam, and M. Rosmann, Eds. Lctur Nots in Computr Scinc Sris, vol.. Springr- Vrlag, Brlin,. BOSE, R., AALST, W. VAN DER, ZLIOBAITE, I., AND PECHENIZKIY, M. 0. Handling Concpt Drift in Procss Mining. In Intrnational Confrnc on Advancd Information Systms Enginring (Cais 0), H. Mouratidis and C. Rolland, Eds. Lctur Nots in Computr Scinc Sris, vol.. Springr- Vrlag, Brlin, 0. COOK, J. AND WOLF, A.. Discovring Modls of Softwar Procsss from Evnt-Basd Data. ACM Transactions on Softwar Enginring and Mthodology,,. CORTADELLA, J., KISHINEVSKY, M., LAVAGNO, L., AND YAKOVLEV, A.. Driving Ptri Nts from Finit Transition Systms. IEEE Transactions on Computrs,,. DATTA, A.. Automating th Discovry of As-Is Businss Procss Modls: Probabilistic and Algorithmic Approachs. Information Systms Rsarch,, 0. DESEL, J. AND REISIG, W.. Plac/Transition Nts. In Lcturs on Ptri Nts I: Basic Modls, W. Risig and G. Roznbrg, Eds. Lctur Nots in Computr Scinc Sris, vol.. Springr-Vrlag, Brlin,. DONGEN, B. VAN AND AALST, W. VAN DER 00. Multi-Phas Procss Mining: Building Instanc Graphs. In Intrnational Confrnc on Concptual Modling (ER 00), P. Atzni, W. Chu, H. Lu, S. Zhou, and T. Ling, Eds. Lctur Nots in Computr Scinc Sris, vol.. Springr-Vrlag, Brlin,. DONGEN, B. AND AALST, W. VAN DER 00. Multi-Phas Mining: Aggrgating Instancs Graphs into EPCs and Ptri Nts. In Procdings of th Scond Intrnational Workshop on Applications of Ptri Nts to Coordination, Workflow and Businss Procss Managmnt, D. Marinscu, Ed. Florida Intrnational Univrsity, Miami, Florida, USA,. DONGEN, B. VAN, BUSI, N., PINNA, G., AND AALST, W. VAN DER 00. An Itrativ Algorithm for Applying th Thory of Rgions in Procss Mining. In Procdings of th Workshop on Formal Approachs to ACM Transactions on Managmnt Information Systms, Vol., No., Articl, Publication dat: Fbruary 0.

16 : W. van dr Aalst Businss Procsss and Wb Srvics (FABPWS 0), W. Risig, K. H, and K. Wolf, Eds. Publishing Hous of Univrsity of Podlasi, Sidlc, Poland,. EHRENFEUCHT, A. AND ROZENBERG, G.. Partial (St) -Structurs - Part and Part. Acta Informatica,,. GRECO, G., GUZZO, A., PONTIERI, L., AND SACCÀ, D. 00. Discovring Exprssiv Procss Modls by Clustring Log Tracs. IEEE Transaction on Knowldg and Data Enginring,, GÜNTHER, C. AND AALST, W. VAN DER 00. Fuzzy Mining: Adaptiv Procss Simplification Basd on Multi-prspctiv Mtrics. In Intrnational Confrnc on Businss Procss Managmnt (BPM 00), G. Alonso, P. Dadam, and M. Rosmann, Eds. Lctur Nots in Computr Scinc Sris, vol.. Springr-Vrlag, Brlin,. HAND, D., MANNILA, H., AND SMYTH, P. 00. Principls of Data Mining. MIT prss, Cambridg, MA. HERBST, J A Machin Larning Approach to Workflow Managmnt. In Procdings th Europan Confrnc on Machin Larning. Lctur Nots in Computr Scinc Sris, vol. 0. Springr-Vrlag, Brlin,. TFPM IEEE TASK FORCE ON PROCESS MINING. 0. Procss Mining Manifsto. In BPM Workshops. Lctur Nots in Businss Information Procssing Sris, vol.. Springr-Vrlag, Brlin. MANYIKA, J., CHUI, M., BROWN, B., BUGHIN, J., DOBBS, R., ROXBURGH, C., AND BYERS, A. 0. Big Data: Th Nxt Frontir for Innovation, Comptition, and Productivity. McKinsy Global Institut. MEDEIROS, A., WEIJTERS, A., AND AALST, W. VAN DER 00. Gntic Procss Mining: An Exprimntal Evaluation. Data Mining and Knowldg Discovry,, 0. MUNOZ-GAMA, J. AND CARMONA, J. 0. Enhancing Prcision in Procss Conformanc: Stability, Confidnc and Svrity. In IEEE Symposium on Computational Intllignc and Data Mining (CIDM 0), N. Chawla, I. King, and A. Sprduti, Eds. IEEE, Paris, Franc. ROZINAT, A. AND AALST, W. VAN DER 00. Dcision Mining in ProM. In Intrnational Confrnc on Businss Procss Managmnt (BPM 00), S. Dustdar, J. Fiadiro, and A. Shth, Eds. Lctur Nots in Computr Scinc Sris, vol. 0. Springr-Vrlag, Brlin, 0. ROZINAT, A. AND AALST, W. VAN DER 00. Conformanc Chcking of Procsss Basd on Monitoring Ral Bhavior. Information Systms,,. SOLE, M. AND CARMONA, J. 00. Procss Mining from a Basis of Rgions. In Applications and Thory of Ptri Nts 00, J. Lilius and W. Pnczk, Eds. Lctur Nots in Computr Scinc Sris, vol.. Springr-Vrlag, Brlin,. SONG, M. AND AALST, W. VAN DER 00. Towards Comprhnsiv Support for Organizational Mining. Dcision Support Systms,, 00. VERBEEK, H., BUIJS, J., DONGEN, B. VAN, AND AALST, W. VAN DER 00. ProM : Th Procss Mining Toolkit. In Proc. of BPM Dmonstration Track 00, M. L. Rosa, Ed. CEUR Workshop Procdings Sris, vol... WEIJTERS, A. AND AALST, W. VAN DER 00. Rdiscovring Workflow Modls from Evnt-Basd Data using Littl Thumb. Intgratd Computr-Aidd Enginring 0,,. WERF, J., DONGEN, B. VAN, HURKENS, C., AND SEREBRENIK, A. 00. Procss Discovry using Intgr Linar Programming. Fundamnta Informatica,. WESKE, M. 00. Businss Procss Managmnt: Concpts, Languags, Architcturs. Springr-Vrlag, Brlin. ACM Transactions on Managmnt Information Systms, Vol., No., Articl, Publication dat: Fbruary 0.

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