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Title:Desire Lines in Big DataName:Wil M.P. van der AalstAffil./Addr.:Eindhoven University of TechnologyDepartment of Mathematics and Computer SciencePO Box 513, NL-5600 MB, Eindhoven, The NetherlandsE-mail: [email protected] Lines in Big DataSynonymsprocess mining, business process intelligence, distributed process mining, process discoveryGlossaryEvent log: multiset of traces.Trace: sequence of events.Event: occurrence of some discrete incident (e.g., completion of an activity).Process mining: collection of techniques to discover, monitor and improve real processes by extracting knowledge from event data.Process discovery: extracting process models from an event log.Conformance checking: monitoring deviations by comparing model and log.DefinitionProcesses leave footprints in information systems just like people leave footprints ingrassy spaces. Desire lines, i.e., the tracks formed by erosion showing where people

2really walk, may be very different from the formal pathways. When people deviatefrom the official path there is often a good reason and room for improvement. The goalof process mining is to extract desire lines from event logs, e.g., to automatically infera process model from raw events recorded by some information system.Process mining techniques and tools should be able to deal with huge heterogeneous event logs. For example, the increasing ability to record events (cf. sensor data,internet of things, remote monitoring, and service orientation) may make it infeasibleto store all events over an extended period. Therefore, on-the-fly discovery techniqueshave been developed, i.e., techniques to learn process models without storing excessiveamounts of events. Moreover, techniques to distribute process mining techniques overa network consisting of many computing nodes are being developed. The techniquesexploit modern computing infrastructures and make process mining scalable. This wayit is possible to discover desire lines in Big Data.IntroductionDesire lines refer to tracks worn across grassy spaces – where people naturally walk– regardless of formal pathways (see Figure 1). A desire line emerges through erosion caused by footsteps of humans (or animals) and the width and degree of erosionof the path indicates how frequently the path is used. Typically, the desire line follows the shortest or most convenient path between two points. Moreover, as the pathemerges more people are encouraged to use it, thus stimulating further erosion. DwightEisenhower is often mentioned as one of the persons that noted this emerging groupbehavior. Before becoming the 34th president of the United States, he was the president of Columbia University. When he was asked how the university should arrangethe sidewalks to best interconnect the campus buildings, he suggested letting the grassgrow between buildings and delay the creation of sidewalks. After some time the de-

3sire lines revealed themselves. The places where the grass was most worn by people’sfootsteps were turned into sidewalks.normative orexpected pathdesirelineFig. 1: Desire lines reveal the actual and not the assumed behavior of people, machines,and organizations.The term “desire line” has been used for decades in urban planning. A desireline shows where people naturally walk. The width and degree of erosion of such aninformal path indicates how frequently the path is used. Often the desire line is verydifferent from the formal pathway. Therefore, some planners simply let erosion tell werethe paths need to be. For example, the paths across Central Park in New York werereconstructed using this approach [24, 26].Good information systems do not show signs of erosion. Nevertheless, they oftencontain a wealth of event data providing clues about the paths followed by the usersof the system. Therefore, it is possible to determine desire lines in organizations, systems, and products. Besides visualizing such desire lines, we can also investigate howthese desire lines change over time, characterize the people following a particular de-

4sire line, etc. There may also be desire lines that are “undesirable” (unsafe, inefficient,unfair, etc.). Uncovering such phenomena is a prerequisite for process and productimprovement.The potential value of desire lines in “big data” (say event logs containing millions of events) is enormous. The identification of such information can be used toredesign procedures and systems (“reconstructing the formal pathways”), to recommend people taking the right path (“adding signposts were needed”), or to build insafeguards (“building fences to avoid dangerous situations”).More and more information about (business) processes is recorded by information systems in the form of so-called “event logs”. IT systems are becoming more andmore intertwined with these processes, resulting in an “explosion” of available data thatcan be used for analysis purposes. Today’s information systems already log enormousamounts of events. Classical workflow management systems (e.g. FileNet, TIBCO iProcess Suite, Global 360), ERP systems (e.g. SAP, Oracle), case handling systems (e.g.BPM one), PDM systems (e.g. Windchill), CRM systems (e.g. Microsoft DynamicsCRM, SalesForce), middleware (e.g., IBM’s WebSphere, Cordys), hospital informationsystems (e.g., Chipsoft, Siemens Soarian), etc. provide very detailed information aboutthe activities that have been executed. Not just information systems record data; manyphysical devices are connected to the Internet and objects (products and resources) aretagged and monitored. Providers of high-tech systems (ASML, Philips Healthcare, etc.)are recording terabytes of data on a daily basis. In fact, according to MGI, nearly allsectors in the US economy have at least an average of 200 terabytes of stored dataper company (for companies with more than 1,000 employees) and many sectors havemore than 1 petabyte in mean stored data per company [21]. Until 2000 most datawas still stored in analog form (books, photos, etc.). Since 2000 data storage has grownspectacularly, shifting markedly from analog to digital [18].

5Data will continue to grow at a spectacular rate. Moreover, the digital universeand the physical universe are becoming more and more aligned, e.g., money has becomea predominantly digital entity. When booking a flight over the Internet, the customer isinteracting with many organizations (airline, travel agency, bank, and various brokers),often without actually realizing it. If the booking is successful, the customer receivesan e-ticket. Note that an e-ticket is basically a number, thus illustrating the tightcoupling between the digital and physical universe. When the SAP system of a largemanufacturer indicates that a particular product is out of stock, it is impossible to sellor ship the product even when it is available in physical form. Technologies such asRFID (Radio Frequency Identification), GPS (Global Positioning System), and sensornetworks will stimulate a further alignment of data and reality, e.g., RFID tags makeit possible to track and trace individual items. Hence, there will be more and morehigh-quality data that can be used to reveal desire lines in any industry.Since we are interested in analyzing processes based on the data recorded, wefocus on events that can be linked to relevant activities. The order of such events isimportant for deriving the actual process. Fortunately, most events have a timestampor can be linked to a particular date. Hence, the event data needed for process miningare omnipresent.Consider for example Philips Healthcare, a provider of medical systems that areoften connected to the Internet to enable logging, maintenance, and remote diagnostics.For example, more than 1500 Cardio Vascular (CV) systems (i.e., X-ray machines) aremonitored by Philips. On average each CV system produces 15,000 events per day,resulting in 22.5 million events per day for just their CV systems. The events arestored for about three years and have many attributes. The error logs of ASML’slithography systems have similar characteristics and also contain about 15,000 eventsper machine per day. These numbers illustrate the fact that many organizations are

6storing terabytes of event data. Earlier applications of process mining in organizationssuch as Philips and ASML, show that there are various challenges with respect toperformance (response times), capacity (storage space), and interpretation (discoveredprocess models may be composed of thousands of activities).Many organizations are using so-called Business Intelligence (BI) software, e.g.,Business Objects (SAP), Cognos (IBM), Hyperion (Oracle), etc. Common functionsoffered by these BI tools are reporting, online analytical processing, data mining, business performance management, benchmarks, and predictive analysis. However, thesetools assume that the process is known and they typically look at data-related aspects(e.g., correlations) or view the process at an aggregate level (e.g., a dashboard showingthe average response time). BI tools typically provide some form of data mining andthere are dedicated data mining tools such as Weka, SPSS Clementine, RapidMiner,etc. Typical techniques supported are classification, clustering, association rules, etc.However, these systems do not allow for the discovery of processes based on eventlogs. In fact, an explicit process notion is missing. This led to the formation of a newresearch domain: process mining.Key PointsThe spectacular growth of event data is providing opportunities and challenges forprocess mining. Process discovery and conformance checking can be used to analyze andimprove operational business processes in any sector. However, as event logs are growingin size it may be impossible to store, manage, and analyse event data using traditionalalgorithms and tools. Moreover, process mining is increasingly used on online settingswhere processes need to be analyzed on-the-fly. Process mining algorithms and toolsneed to be adapted to this new reality.

7case id12.event 300.Table 1: A fragment of some event log: each line corresponds to an event.Process MiningIn this section, we first introduce process mining using a small example. Then weelaborate on ways to deal with huge event sets.Process mining techniques attempt to extract non-trivial and useful informationfrom event logs [1, 19]. One aspect of process mining is control-flow discovery, i.e., automatically constructing a process model (e.g., a Petri net or BPMN model) describingthe causal dependencies between activities [7, 9, 29]. The basic idea of control-flowdiscovery is very simple: given an event log containing a set of traces, automaticallyconstruct a suitable process model “describing the behavior” seen in the log. Such discovered processes have proven to be very useful for the understanding, redesign, andcontinuous improvement of business processes [1].To illustrate the notion of process discovery, consider Table 1. The table shows asmall fragment of some larger event log. Only two traces are shown, both containing 4

8events. Each event has a unique id and several properties. For example, event 35654423is an instance of activity A that occurred on December 30th at 11.02, was executedby John, and costs 300 euros. The second trace starts with event 35655526 and alsorefers to an instance of activity A. Note that each trace corresponds to a case, i.e., acompleted process instance.1hA02 , B 06 , C 12 , D18 i2hA10 , C 14 , B 26 , D36 i3hA12 , E 22 , D56 i4hA15 , B 19 , C 22 , D28 i5hA18 , B 22 , C 26 , D32 i6hA19 , E 28 , D59 i7hA20 , C 25 , B 36 , D44 iTable 2: A simplified event log. Each line corresponds to a trace represented as asequence of activities with timestamps.The information depicted in Table 1 is the typical event data that can be extracted from today’s information systems. To make the example more manageable, wenow focus on the activities and their timestamps only. Table 2 shows another view onthe same event log. Now each line corresponds to a process instance, e.g., the first tracehA02 , B 06 , C 12 , D18 i refers to a process instance where activity A was executed at time2, activity B was executed at time 6, activity C was executed at time 12, and activityD was executed at time 18. Note that the first two traces in Table 2 correspond to thefragment shown in Table 1 (using simplified timestamps).Using existing process mining techniques it is possible to extract a process modelfrom Table 2. For example, by applying the α algorithm [9] we obtain the process modelshown in Fig. 2. This simple Petri net model [25] describes the process that starts with

9BAp1Ep3startDcompletep2Cp4Fig. 2: A process model discovered from Table 2 using the α algorithm.A and ends with D. In-between A and D either E or B and C are executed (in anyorder).Clearly, process mining – in particular control-flow discovery – is related tothe classical work on inductive inference. However, there are also notable differencesbecause, unlike most of the classical work, process mining focuses on higher orderrepresentations which explicitly model concurrency (e.g., Petri nets, UML ADs, EPCs,BPMN, etc.) rather than lower level representations (e.g., Markov chains, finite statemachines, or regular expressions). Moreover, we do not assume negative examples (i.e.,there are no events stating that an activity cannot happen) and deal with issues suchas incompleteness (i.e., if something did not happen, it may still be possible) andexceptional behavior. See [1] for an overview of existing process discovery approaches.Process mining is not limited to control-flow discovery [1]. First of all, besidesthe control-flow perspective (“How?”), other perspectives such as the organizationalperspective (“Who?”) and the case/data perspective (“What?”) may be considered.Second, process mining is not restricted to discovery. Typically three basic types ofprocess mining are considered: (a) discovery, (b) conformance, and (c) enhancement[1]. In this article we will focus on process discovery, i.e., discovering a model from rawevents. Discovery serves as the starting point for the two other types of process mining.The second type of process mining is conformance [27, 23]. Here, an existing processmodel is compared with an event log of the same process. Conformance checking can beused to check if reality, as recorded in the log, conforms to the model and vice versa. The

10third type of process mining is enhancement [8]. Here, the idea is to extend or improvean existing process model using information about the actual process recorded in someevent log. Whereas conformance checking measures the alignment between model andreality, this third type of process mining aims at changing or extending the a-priorimodel. For instance, by using timestamps in the event log one can extend the modelto show bottlenecks, service levels, throughput times, and frequencies.For example, the event log in Table 2 shows timestamps. When replaying theevent log on the process model shown in Fig. 2, we can measure the time spent in theplaces in-between the various activities. This can be used to identify bottlenecks andpredict the remaining flow time for running cases [1, 8].010 Registreren huuropzegging(complete)2080,8576THORAX 2R 386002(complete)1260,510,51ABO RH 370604(complete)286070 Is 1e inspectie uitgevoerd ?(complete)204TOTAAL T4 376406B(complete)10,51CT B.BUIK MC 387042(complete)20,51GEB. A.S.ERY 378609M(complete)60,903224EIW.SPEC.KWN 370433F(complete)20,66770,66720,520,52CHOLESTEROL 370425(complete)30,510,6675HEUP R. 2R 389202R(complete)10,51THORAX 1R 386001(complete)60,55MYCOBAC PCR 378697F(complete)10,51DUPLEX-VEN. 339849W(complete)60,510,66720,66750,53AFWEZIGH.DAG RAX 386000(complete)1ICC-KL.NEURL 413409(complete)10,58RIB STERN.2R . 614400(complete)10650,510,510,51CYTOL.LYMFEK 355409(complete)7ANTI-HAV.IG 2R 383302(complete)10,51LEUKO TELLEN 370712B(complete)289ICC-KL.LONGZ 50,510,510,510,9551760,889320,7550,91713MAGN.DIV. S 0,891560,54O2-SATURATIE 378458(complete)2290,977560,53STAGLAP.OMCT 335512J(complete)40,962420,86UREUMS 370403S(complete)780,942292URINEZUUR 370416(complete)40,51LDH KINET. 370488J(complete)1840,917150,754MAGN. DIV. 378858(complete)610,9781560,97864AMMONIAK 370483(complete)10,510,6672LCR 378546(complete)50,520,51TROPONINE-T 378468P(complete)7OSMOLALITEIT 372107(complete)10,66740,7540,510,51BNP 376425A(complete)6CYTOL.DIVER. 355499(complete)20,510,510,923130,917210,87511ECHO HALS 382970(complete)10,94419SGOT KIN. S 10,520,510,51DUPL.BEEN EZ 389073F(complete)10,75120,510,7540,7558ECHO ROUTINE 339486G(complete)30,6673LYMFSC.SCH.W 302211F(complete)50,66740,51BEKKEN LIGG. 389101(complete)30,51VIT. E 376451(complete)10,510,8960,820,75270,5 0,51 1L.A.C. 375552C(complete)10,6674VULVECT.LIES 337451(complete)10,520,7530,51ICC-KL.INTER 413413(complete)1HIST.GR.PREP 356133(complete)540,75140,51MRI BEKKEN 389190(complete)200,7518KRUISPR. 375075(complete)2920,909610,51LA2 710170B(complete)20,510,510,510,51AFW. VULVA 337419(complete)1EC PUN.LEVER 387677(complete)10,8350,9091230,6672LISEXC.CERV. 337202(complete)1DUO SCOP.ECH 339141J(complete)10,530,510,510,51ECHO GEN.INT 717CYTOL. LEVER 355431(complete)2CA-19.9 379414(complete)3AS-ERY. SCR. 378607(complete)2850,510,51BLD.GRP.KELL 375004(complete)16HEP-B SURF. 375138A(complete)1010,51IUD 337292(complete)1COUPE INZAGE CRP0,51S 378452S(complete)430,510,510,8335EXT. UTERUS 337105F(complete)3CONISATIE 337220(complete)40,92919CHLORIDE S 370420S(complete)220,510,83321OP.BUIK 335519B(complete)10,510,917180,8110,51AMMONIAK S 370483S(complete)10,66770,817115DUPLEXSCAN 339848H(complete)1DOPPL.O.EXTR 339848D(complete)1ONDERZ.KWEEK 370504A(complete)228SINUS 2R 382102(complete)1KLIN.KRT.INW 20113(complete)10,51AS-HBS. KWN 375140(complete)10,51VIT. B3 370474G(complete)10,51AUD KRT 1.5 659030(complete)1MORFOMETRIE 355107(complete)10,52ECHO ONDBUIK 388070A(complete)1240 Registreren voorl. huurovereenkomst IGOXINE 376454A(complete)20,95207DIFF.AUTOM. 370701(complete)2840,51CHLORIDE 370119A(complete)10,83350,6679DIFF.HANDM. 379000A(complete)140,947260,5 0,51 1260 Is contract getekend en geld ontvangen ?(complete)166HEMATOCR. S RIET 370711(complete)390,9932100,97150TROMBO S 370715S(complete)290FDP DIMEER 376467E(complete)70,51COLON INLOOP 387511(complete)10,667150,52KALIUM S 370136S(complete)2ANTITROMB. 375553D(complete)40,510,9160,93831INR TROMBOPL 370737Z(complete)440,85713PROTROMB. S 370707S(complete)450,91720ANF 375408B(complete)1BSE 378729(complete)140,510,510,87516IGG-A.CARD. 375421C(complete)10,510,66720,80886TT 375518(complete)230,510,9931590,983980,90931270 Verwijderen voorlopige huurovereenkomst(complete)10,7530,8578FIBRINOGEEN 370487A(complete)20,51CYTOL.NIERC. 355426(complete)4CYTOL.PUNCT. 350507(complete)30,989165300 Wijzigen status WMS (definitief geaccepteerd)(complete)94CEFALINETIJD 370737C(complete)290,51PTT 370737S(complete)500,98194290 Definitief maken Huurovereenkomst(complete)165TROMB TELLEN 370715A(complete)2630,5 0,75240,95440,6673EC-BIOP.BEKK 389177(complete)1305 Vastleggen huishoudgrootte en inkomen(complete)30,51205 Bepalen kandidaat huurder(complete)1240,66720,753PROTROMBINET 378720(complete)280,51310 Aanpassen factureerafspraak(complete)162IGM-A.CARD. 375421B(complete)10,510,98162TROMBINETIJD 375517(complete)10,51AS.(a) hospital0,911340,51LEUCO ELEC S 377121S(complete)2940,9236180,51TEL.CONS. KO 415100(complete)183570 Archiveren ,7530,994167230 Registreren/controleren kandidaat (WMS)(complete)450,941900,51HAEMOGLOB. S 370701S(complete)5020,6673560 Opstellen eindnota(complete)1690,976450,510,51URODYN.5 KAN 339869K(complete)10,530,855920,51GYN.-AANV.KO 10207(complete)36220 Aanbieden zelfstandige woning (WMS)(complete)450,51LIPASE 370415A(complete)10,923570,510,510,6676KLASSE 3B 613000(complete)1388BEZOEK 410500(complete)370,5 0,51 1CO-HB 370440(complete)189OP.BUIK ,7530,510,52CYSTOSCOPIE 339161(complete)20,85727DIR.COOMBS 375005(complete)60,520,51GAMMA-GT S 372417S(complete)34FOSFAAT S 370421S(complete)200,510,510,6674ALFA-AMYLASE 370117(complete)1210 Aanmaken leegmelding en exporteren (WMS)(complete)46FQ1 - FQ2 710290(complete)6LIGDAG IC 40034(complete)90,909130,87510AS-ERY.SPEC. 378609K(complete)70,510,66780,810,756IRREG.AS ERY 378609R(complete)110,95529HCVR PCR 378639U(complete)10,87541DARM SCINT. 306332C(complete)2ECHO BEEN 389070(complete)10,510,97848550 Vastleggen bonussen / kosten(complete)48IRREG.AS ERY 378609S(complete)120,51BILT BILG S 370401S(complete)470,889170,83330,510,757CALCIUM S 370426S(complete)2370,968610,923270,51ANTI-HEPAT-C 377479A(complete)2540 Worden er bonussen/ kosten toegekend ?(complete)1670,66720,7540,7550,7761470,6672CITO HISTOL. 359999(complete)29CT ABDOM.MC 387042A(complete)90ANTIST.KOUD 375009(complete)20,94420AMYLASE S 370415S(complete)100,947270,51ICCV-KL.CHIR 414403(complete)10,97310,51MET-SULF-HB 370407C(complete)1900,870,8338ALBUMINE SP 378453S(complete)53ALK.FOSFAT.S 370423T(complete)440,9821130,819DOPPLER HART 339494C(complete)10,510,87514ZWARE DAGVPL 619700(complete)10,66714BICARBONAAT 370424(complete)2140,510,66760,510,510,510,6679OP. UTERUS 337105(complete)50,51ICC-KL.CHIR. 413403(complete)20,82MELKZUUR S 376482S(complete)1350,51GEFILT.ERYT 710170(complete)187VRIESCOUPE 355105(complete)100,92315BLAASKATHET. 336272(complete)1COLPOSCOPIE 339170(complete)30,510,9941670,510,51NO SHOW 380000(complete)2DUN.DARM MC 387411(complete)10,889100,87531ECHO BLAAS 339488A(complete)10CT BEKKEN MC 389142(complete)20,83350,52B-SUBUN. HCG 370828A(complete)60,95102530 Aanmaken werkopdracht(complete)1670,530,540,756PROLACTINE 372443(complete)20,510,52DAGVERPL. 619600(complete)640,510,66750,510,667220,51BLOED 372440A(complete)40,520,754ECHO ABDOMEN 387070A(complete)20,909170,6672ERY-ELUAAT 371LHDIEET NNO 709999(complete)370,838197COLPOSCOPIE 339171A(complete)20,52ONTSTEK.TOT. 302622H(complete)10,52PACLITAXEL 686405(complete)500,9931610,86ERYS ELEKTR. 378731(complete)10,51BOTDICHT.FEM 304360F(complete)10,7530,6672IMM.PATH.OND 350503(complete)650,51HYSTEROSCOP. 339186(complete)50,88930OP.BUIK 335519A(complete)30,66730,51ECHO CAROT.L 381670L(complete)10,510,510,51TESTOSTERON 376487D(complete)2STAGLAP.OMCT 335512N(complete)20,66770,51KLASSE 3A PLEG. 40016(complete)660,530,510,51VULVECTOMIE 337452(complete)3AFW. VULVA 337419C(complete)5ECHO BUIKW. 387970(complete)10,51AFW. VAGINA 337380(complete)20,51ECHO NIER 388170(complete)100,6673UTERUSCURETT 337190C(complete)50,51AFW. VAGINA 337319(complete)10,860,510,510,6672CYTOL.LONGP. 355411(complete)10,510,51VAGINA-TOUCH 339988E(complete)340,769190,51EPI.ANALG.AN 339090B(complete)1LYMFEKL.BIOP 333780(complete)10,75110,753MAM.GR.THWND 386902(complete)70,51EXC. UTERUS 337101B(complete)30,520,52CYTOL.ASCIT. 355401(complete)300,75170,85714EXC. UTERUS 337101(complete)70,6672AFW.VRW.ORG. 337180(complete)30,51CYTOL.ECTOC. 355201(complete)340,51AFW. VULVA 337480(complete)20,752CYST.UR.SCOP 339160(complete)220,66740,9331430,667 0,25120,510,6678OV.OP.CLITOR 337436(complete)10,51NATRIUM S 370135S(complete)30,94469MRI ABDOMEN 387090(complete)41GYN.-KORT-KO 10107(complete)1370,6672HIST.KL.PREP 356132(complete)26VULVECT.LIES 337440(complete)50,51HIST.BIOPTEN 356134(complete)49VIT. B6 370474A(complete)10,66720ANESTHESIE 339090N(complete)2KLIN.OPNAME 610001(complete)3120,510,510,520,510,51FSH EIA 372439(complete)30,51PROGESTERON 372442A(complete)20,510,520,51GEB.A.S.ERY 378609Y(complete)1ECHO DOPPLER 339482A(complete)10,81000,6673EIWIT BEP. 700050(complete)30,530,51BLD.GRP.MNSP 378490E(complete)60,510,875200,510,51FACT. 8 ACT. 375552A(complete)10,520,83350,51EIWITFRACT. 376478(complete)10,75120,9928910,51BLD.GRP.KIDD 378610(complete)20,51SCINT.LYMFEK 302282F(complete)50,8370,6672OESTRADIOL 378431(complete)4BOTDICHT.LWK 304360E(complete)10,51TARIEF CONS. 419100(complete)4950,510,84ECHO MAMMA 386970(complete)30,510,51PLEURAPUNCTI 332610(complete)10,8335LYMFES.SCH.W 302213E(complete)50,8337520 Aanmaken 2e in gebreke stelling(complete)7GYN.-JAAR-KO 10307(complete)610,83347C.V.V.H.D. 339970J(complete)1B.BEEN L. 2R 389302L(complete)10,510,51EXC.ADNEX DZ 510,540,51VIT. A 377439(complete)10,51510 Is opleveringsformulier ondertekend ?(complete)1680,510,510,520,52490 Versturen brief Niet thuis(complete)20,9771770,52INBR.KATHET. 333698(complete)8O.BEEN L. 2R 389502L(complete)1ORDERTARIEF 379999(complete)14430,51RETI TELLEN 370716(complete)40,530,52VERV.CONSULT 411100(complete)6760,51RENOGR.LASIX 307031G(complete)2500 Beoordelen/wijzigen RAIN.THORAX TRANS 372417(complete)1850,510,830,6673SHBG 377447(complete)20,520,8335480 Is de 2e eindinspectie uitgevoerd ?(complete)80,510,875120,51470 Wijzigen einddatum huurovereenkomst(complete)80,94120270 Archiveren nieuwe verhuring(complete)320,510,51420 Wijzigen einddatum 0,917130,510,51CAPNOGRAFIE 339832C(complete)140,833202DRL.BUIK 387000(complete)10,909200,510,510,55FOSFAAT 370421(complete)35CT THORAX ZK 386041(complete)50,51B.O.Z. 2R 387002(complete)4ART.PUNCT.CR 339954A(complete)60,530,83328GENTAMYCINE 377410D(complete)40,929310,53ANES.VERV. 339992Z(complete)10,510,520,510,991141260 After sales(complete)320,9152230,857120,510,51430 Herplannen eindinspectie(complete)30,8576460 (Her)plannen 2e ESTH. 40032(complete)40,520,510,510,510,95796CEA MEIA BL 376400D(complete)1070,540,520,753450 Krijgt de huurder tijd om te herstellen ?(complete)270,96832250 Aanpassen CHLORIDE 370420(complete)52VIT B12 370466C(complete)20,510,755410 Versturen brief 'niet thuis'(complete)30,96427CALCIUM 377498A(complete)240NATRIUM VLAM 370135(complete)50,7550,75290,510,510,92315240 Definitief maken 0,9921510,53TOT.EIW. 370480A(complete)7CA-125 MEIA 378619A(complete)1880,51CA 15.3 MEIA 378619E(complete)60,51IMMUNOFORESE ITINE 372454A(complete)50,51PARAPROT.TYP 375128(complete)20,754TR.FERRINE 378808(complete)50,510,510,510,51MICROALBUM. 378173B(complete)1IJZER 241BILI TOTAAL 370401C(complete)1930,510,66720,966330,51200 Toewijzen 51VANCOMYCINE 377410G(complete)2AMYLASE 370415(complete)11HAPTO. 375101(complete)40,51FOLIUMZUUR 370465Q(complete)30,6673440 Zijn er nieuwe of niet herstelde gebreken ?(complete)1680,941570,51MELKZUUR 376482C(complete)180,510,7570,9751370,6673230 Verwijderen voorlopige 0CREATININE 370129(complete)40,9931690,6674ALK.FOSFAT. 370423(complete)1870,53UREUM 370403(complete)2460,510,510,510,754NATRIUM VLAM 377842C(complete)20,9921440,90921BILI. GECON. 370401(complete)1440,9842070,51CPK-MB S 378403S(complete)60,945ECHO CAROT.R 381670R(complete)1ALBUMINE 378453A(complete)2380,5 0,66714HS-CRP 378452A(complete)1SGPT-ALAT 370488G(complete)2170,66760,510,95560,84236CRP 378452(complete)95BLD.GRP.LEW. 378490G(complete)10,66750,95229SGOT-ASAT 370488E(complete)2150,520,994168220 Is contract getekend en geld ontvangen 0,550,994201400 Is eindinspectie uitgevoerd 5258A1-FETOPROT. 378449(complete)80,51NATRIUM VLAM 370442(complete)4940,96420,6672CDE FENOTYP 375003A(complete)130,530,52TRIGLYCERIDE 370460E(complete)40,51CT ABDOMEN NINE 370419(complete)483APCA 330398A(complete)20,9791590,510,97332210 Registreren voorl. huurovereenkomst afdrukken(complete)350,7530,5713KALIUM POTEN E 370402(complete)2150,510,8578EIWIT COLOR. 370172(complete)40,51190 Actualiseren huurprijs(complete)340,993181CLOSTRIDIUM 378216A(complete)70,857480,91711190 Harmoniseren huurprijs(complete)1690,7525EIW.TOT. S 370480S(complete)80,7530,857116LYMFADENECT 333742(complete)1ANTI-HIV 378644(complete)2PH-PCO2-BIC. 372414(complete)2120,51075 Bepalen leegstandssoort bedr/gar/berg/park/op(complete)120,993 0,97115834470 Archiveren huuropzegging(complete)340,909490,825129SGPT ALAT SP 370488S(complete)590,96633OVARIUMCARC. 337106A(complete)50,52065 Aanmaken bevest.brief huuropzegging(b/g/bso/p)(complete)10,753460 Opstellen 5143SGOT ASAT SP 370489S(complete)620,880,520,929199NATRIUM S 370442S(complete)3730,987ALUMINIUM 378437(complete)20,66760,510,510,9628MELKZUUR SP 370488T(complete)320,947200,857210,51CREATININE S 370419S(complete)2060,51450 Vastleggen bonussen / kosten(complete)40,9941680,8899180 Aanpassen woningwaardering(complete)191OVARIUMCARC. 337106(complete)2KALIUM S 370443S(complete)3790,51SPRAAKAUD.ST

an e-ticket. Note that an e-ticket is basically a number, thus illustrating the tight coupling between the digital and physical universe. When the SAP system of a large manufacturer indicates that a particular product is out of stock, it is impossible to sell or ship the product even when it is available in physical form. Technologies such as