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Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
Process Mining: Understanding and Improving Desire Lines in Big Data
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Process Mining: Understanding and Improving Desire Lines in Big Data

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We are pleased to announce the lecture: “Process Mining: Understanding and Improving Desire Lines in Big Data” …

We are pleased to announce the lecture: “Process Mining: Understanding and Improving Desire Lines in Big Data”
in honour of doctor honoris causa Wil van der Aalst.

Wednesday May 30th - 10.00 a.m. - 12 a.m.,

Hasselt University, campus Diepenbeek (Agoralaan, building D) - auditorium H5

The Faculty of Business Economics of Hasselt University is pleased to invite you to the lecture
“Process Mining: Understanding and Improving Desire Lines in Big Data”.

This lecture is organised to honour prof. dr. Wil van der Aalst, on whom the degree of ‘doctor honoris causa’ will be conferred by Hasselt University, Faculty of Business Economics (promotor prof. Koen Vanhoof). Professor van der Aalst is a full professor of Information Systems at the Technische Universiteit Eindhoven (TU/e). Currently he is also an adjunct professor at Queensland University of Technology (QUT).His research interests include workflow management, process mining, Petri nets, business process management, process modeling, and process analysis. Many of his ideas have influenced researchers, software developers and standardization committees working on process support.

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  • 1. Process MiningUnderstanding and Improving DesireLines in Big Dataprof.dr.ir. Wil van der Aalstwww.processmining.org
  • 2. Let’s Play: Play-Out, Play-In, ReplayBig DataDesire LinesProcess MiningHow Good is My Model? Process Discovery Conformance Checking Food for Thought: Lasagna and Spaghetti Google Maps and TomTom How to Get Started? Conclusion PAGE 2
  • 3. On the different roles of (process) models … PAGE 3
  • 4. Play-Out process model event log PAGE 4
  • 5. Play-Out (Classical use of models) B A p1 E p3 Dstart end p2 C p4 A B C D AED AED ABCD ACBD ACBD AED ACBD PAGE 5
  • 6. Play-Inevent log process model PAGE 6
  • 7. Play-InABCD AED AED ABCD ACBD ACBD AED ACBD B A p1 E p3 Dstart end p2 C p4 PAGE 7
  • 8. Example Process Discovery(Vestia, Dutch housing agency, 208 cases, 5987 events) PAGE 8
  • 9. Example Process Discovery(ASML, test process lithography systems, 154966 events) PAGE 9
  • 10. Example Process Discovery(AMC, 627 gynecological oncology patients, 24331 events) PAGE 10
  • 11. Replay • extended model showing times, frequencies, etc. • diagnostics • predictions • recommendationsevent log process model PAGE 11
  • 12. Replay A BC D B A p1 E p3 Dstart end p2 C p4 PAGE 12
  • 13. Replay AED B A p1 E p3 Dstart end p2 C p4 PAGE 13
  • 14. Replay can detect problems AC D Problem! Problem! token left behind B missing token A p1 E p3 Dstart end p2 C p4 PAGE 14
  • 15. Conformance Checking(WOZ objections Dutch municipality, 745 objections, 9583 event, f= 0.988) PAGE 15
  • 16. Replay can extract timing information A5 B8 C9 D13 8 5 6 7 4 3 B 2 5 8 A p1 E p3 Dstart end 5 13 4 p2 3 C p4 4 37 4 7 6 9 PAGE 16
  • 17. Performance Analysis Using Replay(WOZ objections Dutch municipality, 745 objections, 9583 event, f= 0.988) PAGE 17
  • 18. Big Data PAGE 18
  • 19. “All of the worlds music Big Data can be stored on a $600 disk drive.” “Enterprises globally stored more than 7 exabytes of new data on disk drives in 2010, while consumers stored more than 6 exabytes of new data on devices such as PCs and “Indeed, we are notebooks.” generating so much data today that it is physically impossible to store it all. Health care providers, for instance, discard 90 percent of the data that they generate.”Source: “Big Data: The Next Frontier for Innovation, Competition, andProductivity” McKinsey Global Institute, 2011. PAGE 19
  • 20. Hilbert and Lopez. The Worlds Technological Capacity to Store, Communicate, andCompute Information. Science, 332(6025):60-65, 2011. PAGE 20
  • 21. www.olifantenpaadjes.nl PAGE 21
  • 22. PAGE 22
  • 23. PAGE 23
  • 24. PAGE 24
  • 25. Evidence-BasedBusiness Process Management PAGE 25
  • 26. PAGE 26
  • 27. Process Mining PAGE 27
  • 28. Process Mining = Event Data + Processes Data Mining + Process AnalysisMachine Learning + Formal Methods PAGE 28
  • 29. Process Mining supports/ “world” business controls processes software people machines system components organizations records events, e.g., messages, specifies transactions, models configures analyzes etc. implements analyzes discovery (process) event conformance model logs enhancement
  • 30. Starting point: event log XES, MXML, SA-MXML, CSV, etc. PAGE 30
  • 31. Simplified event log a = register request, b = examine thoroughly, c = examine casually, d = check ticket, e = decide, f = reinitiate request, g = pay compensation, and h = reject request PAGE 31
  • 32. Processdiscovery b examine thoroughly g c1 c3 pay c compensation a examine estart register casually decide c5 end request h c2 d c4 reject check ticket request f reinitiate request PAGE 32
  • 33. Conformancechecking b case 7: e is executed examine without thoroughly case 8: g or h being g is missing enabled c1 c3 pay c compensation a examine estart register casually decide c5 end request case 10: e h is missing in c2 d c4 reject second check ticket round request f reinitiate request PAGE 33
  • 34. Extension: Adding perspectives tomodel based on event log The event log can be used to discover roles in the organization (e.g., groups of people with similar work patterns). These roles can be Performance information (e.g., the used to relate individuals and average time between two activities. subsequent activities) can be extracted from the event log and visualized on top of the model . Role A: Role E: Role M: Assistant Expert Manager Decision rules (e.g., a decision tree based on data known at the time a Pete Sue Sara particular choice was made) can be learned from the event log and used Mike Sean to annotated decisions. Ellen E b A examine thoroughly A g A M c1 c3 pay c compensation a examine e A start register casually A decide c5 end request h c2 d c4 M reject check ticket request f reinitiate PAGE 34 request
  • 35. How good is my model? PAGE 35
  • 36. Four Competing Quality Criteria “able to replay event log” “Occam’s razor” fitness simplicity process discoverygeneralization precision “not overfitting the log” “not underfitting the log” PAGE 36
  • 37. Example: one log four models b examine thoroughly g pay c compensation a examine e start register casually decide end # trace request h 455 acdeh d reject check ticket request 191 abdeg f reinitiate request 177 adceh N1 : fitness = +, precision = +, generalization = +, simplicity = + 144 abdeh 111 acdeg a c d e h 82 adceg start register examine check decide reject end request casually ticket request 56 adbeh N2 : fitness = -, precision = +, generalization = -, simplicity = + 47 acdefdbeh “able to replay event log” “Occam’s razor” 38 adbeg examine check thoroughly b d ticket g fitness simplicity pay compensation 33 acdefbdeh a 14 acdefbdeg start register examine c end 11 acdefdbeg request casually e f reinitiate h process decide request reject request 9 adcefcdeh discovery N3 : fitness = +, precision = -, generalization = +, simplicity = + 8 adcefdbeh 5 adcefbdeg a d c e g 3 acdefbdefdbeggeneralization precision register request check ticket examine casually decide pay compensation 2 adcefdbeg a c d e g 2 adcefbdefbdeg “not overfitting the log” “not underfitting the log” register examine check decide pay request casually ticket compensation 1 adcefdbefbdeh a d c e h 1 adbefbdefdbeg register check examine decide reject request ticket casually request 1 adcefdbefcdefdbeg a c d e h 1391 start end register examine check decide reject request casually ticket request … (all 21 variants seen in the log ) a b d e g register examine check decide pay request thoroughly ticket compensation a d b e h register check examine decide reject request ticket thoroughly request a b d e h register examine check decide reject request thoroughly ticket request PAGE 37 N4 : fitness = +, precision = +, generalization = -, simplicity = -
  • 38. # trace 455 acdeh Model N1 191 abdeg 177 adceh 144 abdeh 111 acdeg 82 adceg 56 adbeh b 47 acdefdbeh examine thoroughly 38 adbeg g 33 acdefbdeh pay c compensation 14 acdefbdeg a examine e 11 acdefdbegstart register casually decide end request 9 adcefcdeh h d reject 8 adcefdbeh check ticket request 5 adcefbdeg f reinitiate 3 acdefbdefdbeg requestN1 : fitness = +, precision = +, generalization = +, simplicity = + 2 adcefdbeg 2 adcefbdefbdeg 1 adcefdbefbdeh 1 adbefbdefdbeg 1 adcefdbefcdefdbeg PAGE 38 1391
  • 39. # trace 455 acdeh Model N2 191 abdeg 177 adceh 144 abdeh 111 acdeg 82 adceg 56 adbeh 47 acdefdbeh 38 adbeg a c d e h 33 acdefbdehstart register examine check decide reject end 14 acdefbdeg request casually ticket request N2 : fitness = -, precision = +, generalization = -, simplicity = + 11 acdefdbeg 9 adcefcdeh 8 adcefdbeh 5 adcefbdeg 3 acdefbdefdbeg 2 adcefdbeg 2 adcefbdefbdeg 1 adcefdbefbdeh 1 adbefbdefdbeg 1 adcefdbefcdefdbeg PAGE 39 1391
  • 40. # trace 455 acdeh Model N3 191 abdeg 177 adceh 144 abdeh 111 acdeg 82 adceg 56 adbeh 47 acdefdbeh examine check thoroughly b d ticket g 38 adbeg pay 33 acdefbdeh compensation a 14 acdefbdegstart register examine end 11 acdefdbeg request casually c e f reinitiate reject h 9 adcefcdeh decide request request 8 adcefdbeh N3 : fitness = +, precision = -, generalization = +, simplicity = + 5 adcefbdeg 3 acdefbdefdbeg 2 adcefdbeg 2 adcefbdefbdeg 1 adcefdbefbdeh 1 adbefbdefdbeg 1 adcefdbefcdefdbeg PAGE 40 1391
  • 41. # trace 455 acdehModel N4 191 abdeg 177 adceh 144 abdeh a d c e g 111 acdeg register check examine decide pay request ticket casually compensation 82 adceg a c d e g 56 adbeh register examine check decide pay request casually ticket compensation 47 acdefdbeh a d c e h 38 adbeg register check examine decide reject request ticket casually request 33 acdefbdeh a c d e h 14 acdefbdegstart end register examine check decide reject request casually ticket request 11 acdefdbeg … (all 21 variants seen in the log ) 9 adcefcdeh 8 adcefdbeh 5 adcefbdeg a b d e g register examine check decide pay 3 acdefbdefdbeg request thoroughly ticket compensation 2 adcefdbeg a d b e h register check examine decide reject 2 adcefbdefbdeg request ticket thoroughly request 1 adcefdbefbdeh a b d e h register examine check decide reject 1 adbefbdefdbeg request thoroughly ticket request 1 adcefdbefcdefdbeg N 4 : fitness = +, precision = +, generalization = -, simplicity = - PAGE 41 1391
  • 42. Process Discovery PAGE 42
  • 43. Process Discovery (small selection) distributed genetic mining automata-based learning language-based regions heuristic mining genetic mining state-based regions LTL miningstochastic task graphs neural networksfuzzy mining hidden Markov modelsmining block structures α algorithm conformal process graph multi-phase mining partial-order based mining α# algorithm ILP mining α++ algorithm PAGE 43
  • 44. Petri net view: Just discover the places … “able to replay event log” “Occam’s razor” fitness simplicity process discovery generalization precision “not overfitting the log” “not underfitting the log” a1 b1 a2 b2 ... p(A,B) ... am bn Adding a place limits behavior: •overfitting ≈ adding too many places •underfitting ≈ adding too few placesA={a1,a2, … am} B={b1,b2, … bn} PAGE 44
  • 45. Example: Process Discovery Using State-Based Regions01011001101101001011111101101000110110011110111000001101101001001100 d e [a,e] [a,d,e] [ a,b] a b event log [] [a] c c b d [a,c] [a,b,c] [a,b,c,d] b a p1 e p3 d start end p2 c p4 PAGE 45
  • 46. Example of State-Based Region d e [a,e] [a,d,e] [ a,b] a b [] [a] c c b d [a,c] [a,b,c] [a,b,c,d] enter: b,e leave: d do-not-cross: a,c b a p1 e p3 dstart end p2 c p4 PAGE 46
  • 47. Example: Process Discovery UsingLanguage-Based Regions A place is feasible if it can be added without f c1 disabling any of the traces in the event log. a1 b1 e c d pR a2 b2 X Y PAGE 47
  • 48. Example of Language-Based Regions• accd• bd ↓accd : 0 + 0 - 0 ≥ 0 c• bce a↓ccd : 0 + 1 - 1 ≥ 0• ace b d ac↓cd : 0 + 2 - 2 ≥ 0• acd acc↓d : 0 + 3 - 3 ≥ 0• bcce• ade a e ↓ade : 0 + 0 - 0 ≥ 0 X Y a↓de : 0 + 1 - 1 ≥ 0 ad↓e : 0 + 1 - 2 < 0 PAGE 48
  • 49. Example of a completely different processdiscovery technique: Genetic Mining PAGE 49
  • 50. Genetic process mining: Overview create initial population event log mutation next generation compute fitness elitismtermination tournament children crossover select best parents individual “dead” individuals PAGE 50
  • 51. Example: crossover b b examine examine thoroughly thoroughly g g pay pay c c compensation compensation a e a e examine examinestart register casually decide end start register casually decide end request request h h d d reject reject check ticket request check ticket request f f reinitiate reinitiate request request b b examine examine thoroughly thoroughly g g pay pay c c compensation compensation a e a e examine examinestart register casually decide end start register casually decide end request request h h d d reject reject check ticket request check ticket request f f reinitiate reinitiate request request PAGE 51
  • 52. Example: mutation remove place b b examine examine thoroughly thoroughly g g pay pay c c compensation compensation a e a e examine examinestart register casually decide end start register casually decide end request request h h d d reject reject check ticket request check ticket request f f reinitiate reinitiate request added arc request PAGE 52
  • 53. Conformance Checking PAGE 53
  • 54. Replaying trace “abeg”a b e g b examine thoroughly g pay c compensation a examine estart register casually decide end request r=1 h d m=1 reject check ticket request f reinitiate request 1 1 = 0.83333 6 6 PAGE 54
  • 55. # trace 455 acdeh Can be lifted to log level 191 abdeg 177 adceh N1 b 144 abdeh examine thoroughly g 111 acdeg p1 c p3 pay compensation 82 adceg a examine e start register casually decide p5 end 56 adbeh request h p2 d p4 reject 47 acdefdbeh check ticket request f reinitiate 38 adbeg request N2 b pay 33 acdefbdeh compensation examine g thoroughly 14 acdefbdeg a c d e start register p1 examine p2 check p3 p4 11 acdefdbeg decide end request casually ticket h 9 adcefcdeh f reject request reinitiate request 8 adcefdbeh N3 c p1 examine p3 5 adcefbdeg casually a e h 3 acdefbdefdbeg start register decide p5 reject end request d request 2 adcefdbeg p2 check p4 ticket 2 adcefbdefbdegN4 examine b d check 1 adcefdbefbdeh thoroughly ticket g pay compensation 1 adbefbdefdbeg a p1start register examine c end 1 adcefdbefcdefdbeg request casually e f reinitiate reject h PAGE 55 decide request request 1391
  • 56. From “playing the token game” tooptimal alignments … observed trace: “abeg” a b » e g a b d e g b examine thoroughly g pay move in a c examine e compensation model only start register request casually decide h end d reject check ticket request f reinitiate request PAGE 56
  • 57. Another alignment observed trace: “abcdeg” a b c d e g a b » d e g b examine thoroughly g pay c compensation a examine e start register casually decide end move in log request d h reject only check ticket f request reinitiate request PAGE 57
  • 58. Moves in an alignment move in log trace in event log a b » d e g a » c d e g possible run of model move in model move in bothOptimal alignment describes modeled behavior closest toobserved behavior PAGE 58
  • 59. Moves have costs… a … … » …… » … … a … … a … … a … … a … … b …• Standard cost function: − c(x,») = 1 − c(»,y) = 1 − c(x,y) = 0, if x=y − c(x,y) = ∞, if x≠y PAGE 59
  • 60. Non-fitting trace: abefdeg b examine thoroughly g abefdeg pay c compensation a examine estart register casually decide end request h d reject check ticket request f reinitiate request a b » e f d » e g 2 a b d e f d b e g a b e f d e g 2 a b » » d e g PAGE 60
  • 61. Any cost structure is possible … send-letter(John,2 … weeks, $400) … send-email(Sue,3 … weeks,$500)• Similar activities (more similarity implies lower costs).• Resource conformance (done by someone that does not have the specified role).• Data conformance (path is not possible for this customer).• Time conformance (missed the legal deadline) PAGE 61
  • 62. b examine thoroughly g pay c Fitness compensation 1.0 a examine e start register casually decide end # trace request h 455 acdeh d reject check ticket request 191 abdeg f reinitiate request 177 adceh N1 : fitness = +, precision = +, generalization = +, simplicity = + 144 abdeh 111 acdeg a c d e h 0.8 82 adcegOur A* algorithm start register request examine casually check ticket N2 : fitness = -, precision = +, generalization = -, simplicity = + decide reject request end 56 adbehexploits the Petri net 47 acdefdbeh 38 adbegmarking equation examine thoroughly b d check ticket pay g 33 acdefbdehand uses other compensation 14 acdefbdeg 1.0 a start register examine 11 acdefdbeg“tricks” to prune the c end request casually e f reinitiate h decide request reject 9 adcefcdeh requestsearch space. N3 : fitness = +, precision = -, generalization = +, simplicity = + 8 adcefdbeh 5 adcefbdeg a d c e g 3 acdefbdefdbeg register check examine decide pay request ticket casually compensation 2 adcefdbeg a c d e g 2 adcefbdefbdeg register examine check decide pay request casually ticket compensation 1 adcefdbefbdeh a d c e h 1 adbefbdefdbeg register check examine decide reject request ticket casually request 1 adcefdbefcdefdbeg 1.0 start a register request c examine casually d check ticket e decide h reject request end 1391 … (all 21 variants seen in the log ) a b d e g examineAligned event log is register check decide pay request thoroughly ticket compensation a d b e hstarting point for other register request check ticket examine thoroughly decide reject requesttypes of analysis. a register b d check e decide h reject examine request thoroughly ticket request PAGE 62 N4 : fitness = +, precision = +, generalization = -, simplicity = -
  • 63. Alignments are essential!•conformance checking to diagnose deviations•squeezing reality into the model to do model-basedanalysis PAGE 63
  • 64. Food for Thought PAGE 64
  • 65. We applied ProM in >100 organizations• Municipalities (e.g., Alkmaar, Heusden, Harderwijk, etc.)• Government agencies (e.g., Rijkswaterstaat, Centraal Justitieel Incasso Bureau, Justice department)• Insurance related agencies (e.g., UWV)• Banks (e.g., ING Bank)• Hospitals (e.g., AMC hospital, Catharina hospital)• Multinationals (e.g., DSM, Deloitte)• High-tech system manufacturers and their customers (e.g., Philips Healthcare, ASML, Ricoh, Thales)• Media companies (e.g. Winkwaves)• ... PAGE 65
  • 66. How can process mining help?• Uncover bottlenecks • Provide new insights• Detect deviations • Highlight important• Performance measurement problems• Auditing/compliance • An organization’s mirror• Business Process (in two ways) Redesign (BPR) • Helps to avoid ICT• Continuous improvement failures (Six Sigma) • Avoid “management by• Operational support (e.g., PowerPoint” recommendation and • From “politics” to prediction) “analytics” PAGE 66
  • 67. PAGE 67
  • 68. Example of a Lasagna process: WMO process of a Dutch municipalityEach line corresponds to one of the 528 requests that were handledin the period from 4-1-2009 until 28-2-2010. In total there are 5498events represented as dots. The mean time needed to handled acase is approximately 25 days. PAGE 68
  • 69. WMO process (Wet Maatschappelijke Ondersteuning)• WMO refers to the social support act that came into force in The Netherlands on January 1st, 2007.• The aim of this act is to assist people with disabilities and impairments. Under the act, local authorities are required to give support to those who need it, e.g., household help, providing wheelchairs and scootmobiles, and adaptations to homes.• There are different processes for the different kinds of help. We focus on the process for handling requests for household help.• In a period of about one year, 528 requests for household WMO support were received.• These 528 requests generated 5498 events. PAGE 69
  • 70. C-net discovered usingheuristic miner (1/3) PAGE 70
  • 71. C-net discovered usingheuristic miner (2/3) PAGE 71
  • 72. C-net discovered usingheuristic miner (3/3) PAGE 72
  • 73. Conformance check WMO process (1/3) PAGE 73
  • 74. Conformance check WMO process (2/3) PAGE 74
  • 75. Conformance check WMO process (3/3) The fitness of the discovered process is 0.99521667. Of the 528 cases, 496 cases fit perfectly whereas for 32 cases there are missing or remaining tokens. PAGE 75
  • 76. Bottleneck analysis WMO process (1/3) PAGE 76
  • 77. Bottleneck analysis WMO process (2/3) PAGE 77
  • 78. Bottleneck analysis WMO process (3/3)flow time ofapprox. 25 dayswith a standarddeviation ofapprox. 28 PAGE 78
  • 79. Two additional Lasagna processes RWS (“Rijkswaterstaat”) process WOZ (“Waardering Onroerende Zaken”) process PAGE 79
  • 80. RWS Process• The Dutch national public works department, called “Rijkswaterstaat” (RWS), has twelve provincial offices. We analyzed the handling of invoices in one of these offices.• The office employs about 1,000 civil servants and is primarily responsible for the construction and maintenance of the road and water infrastructure in its province.• To perform its functions, the RWS office subcontracts various parties such as road construction companies, cleaning companies, and environmental bureaus. Also, it purchases services and products to support its construction, maintenance, and administrative activities. PAGE 80
  • 81. C-net discovered using heuristic miner PAGE 81
  • 82. Social network constructed based onhandovers of work Each of the 271 nodes corresponds to a civil servant. Two civil servants are connected if one executed an activity causally following an activity executed by the other civil servant PAGE 82
  • 83. Social network consisting of civil servants thatexecuted more than 2000 activities in a 9 month period. The darker arcs indicate the strongest relationships in the social network. Nodes having the same color belong to the same clique. PAGE 83
  • 84. WOZ process• Event log containing information about 745 objections against the so-called WOZ (“Waardering Onroerende Zaken”) valuation.• Dutch municipalities need to estimate the value of houses and apartments. The WOZ value is used as a basis for determining the real-estate property tax.• The higher the WOZ value, the more tax the owner needs to pay. Therefore, there are many objections (i.e., appeals) of citizens that assert that the WOZ value is too high.• “WOZ process” discovered for another municipality (i.e., different from the one for which we analyzed the WMO process). PAGE 84
  • 85. Discovered process modelThe log contains events related to 745 objections against the so-called WOZ valuation. These 745 objections generated 9583 events.There are 13 activities. For 12 of these activities both start andcomplete events are recorded. Hence, the WF-net has 25 PAGE 85transitions.
  • 86. Conformance checker:(fitness is 0.98876214) PAGE 86
  • 87. Performance analysis bottleneck detection: places are colored based on average durations time required to move from one activity to another information on total flow time PAGE 87
  • 88. Resource-activity matrix(four groups discovered) clique 1 clique 2 clique 3 clique 4 PAGE 88
  • 89. PAGE 89
  • 90. Example of a Spaghetti processSpaghetti process describing the diagnosis and treatment of 2765 patientsin a Dutch hospital. The process model was constructed based on an eventlog containing 114,592 events. There are 619 different activities (takingevent types into account) executed by 266 different individuals (doctors,nurses, etc.). PAGE 90
  • 91. Fragment18 activities of the 619 activities (2.9%) PAGE 91
  • 92. Another example(event log of Dutch housing agency) The event log contains 208 cases that generated 5987 events. There are 74 different activities. PAGE 92
  • 93. PAGE 93
  • 94. Google Maps and TomTom PAGE 94
  • 95. Process models should be treated aselectronic maps PAGE 95
  • 96. Business process movies PAGE 96
  • 97. Business information systems shouldoffer “TomTom” functionalityRecommend: How to get home ASAP? Take a left turn! Detect: You drive too fast! Predict: When will I be home? At 11.26! PAGE 97
  • 98. How to get started? PAGE 98
  • 99. Hundreds of plug-ins available covering the whole process mining spectrum open-source (L-GPL)Download from: www.processmining.org PAGE 99
  • 100. How to Get Started?Collect event data Collect questions• Minimal requirement: • What kind problems would events referring to an you like to address (cost, activity name and a time, risk, compliance, process instance. service, etc.)?• Good to have: • Related to discovery, timestamps, resource conformance, information, additional enhancement? data elements. • Iterative process: can be• Challenges: scoping and “curiosity driven” initially. sometimes correlation. PAGE 100
  • 101. Conclusion PAGE 101
  • 102. Conclusion PAGE 102
  • 103. www.processmining.org www.win.tue.nl/ieeetfpm/ PAGE 103

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