Distributed Process Discovery and Conformance Checking

Transcription

1 Distributed Process Discovery and Conformance Checking prof.dr.ir. Wil van der Aalst

2 On the different roles of (process) models PAGE 1

3 Play-Out PAGE 2

4 Play-Out (Classical use of models) A BCD A E D A E D A C B D A B C D A E D A C B D A C B D PAGE 3

5 Play-In PAGE 4

6 Play-In A B C D A E D A E D A C B D A B C D A E D A C B D A C B D PAGE 5

7 Example Process Discovery (Vestia, Dutch housing agency, 208 cases, 5987 events) PAGE 6

8 Example Process Discovery (ASML, test process lithography systems, events) PAGE 7

9 Example Process Discovery (AMC, 627 gynecological oncology patients, events) PAGE 8

10 Replay PAGE 9

11 Replay A BCD PAGE 10

12 Replay A ED PAGE 11

13 Replay can detect problems ACD Problem! token left behind Problem! missing token PAGE 12

14 Conformance Checking (WOZ objections Dutch municipality, 745 objections, 9583 event, f= 0.988) PAGE 13

15 Replay can extract timing information A 5 B 8 C 9 D PAGE 14

16 Performance Analysis Using Replay (WOZ objections Dutch municipality, 745 objections, 9583 event, f= 0.988) PAGE 15

17 Big Data PAGE 16

18 Big Data All of the world's music 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 notebooks. Source: Big Data: The Next Frontier for Innovation, Competition, and Productivity McKinsey Global Institute, Indeed, we are 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. PAGE 17

19 Hilbert and Lopez. The World's Technological Capacity to Store, Communicate, and Compute Information. Science, 332(6025):60-65, PAGE 18

20 PAGE 19

21 PAGE 20

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23 Evidence-Based Computer Science PAGE 22

24 How good is my model? PAGE 23

25 Four Competing Quality Criteria able to replay event log Occam s razor not overfitting the log not underfitting the log PAGE 24

26 Example: one log four models start a register b examine thoroughly c examine casually d check ticket e decide f reinitiate g pay compensation h reject N 1 : fitness = +, precision = +, generalization = +, simplicity = + end # trace 455 acdeh 191 abdeg 177 adceh 144 abdeh able to replay event log fitness process discovery Occam s razor simplicity start start a register c examine casually examine thoroughly b d check ticket d check ticket decide pay compensation reject N 2 : fitness = -, precision = +, generalization = -, simplicity = + a register examine casually c reinitiate decide e f h reject N 3 : fitness = +, precision = -, generalization = +, simplicity = + e g h end end 111 acdeg 82 adceg 56 adbeh 47 acdefdbeh 38 adbeg 33 acdefbdeh 14 acdefbdeg 11 acdefdbeg 9 adcefcdeh 8 adcefdbeh generalization not overfitting the log precision not underfitting the log start a register a register a register a register d check ticket c examine casually d check ticket c examine casually c examine casually d check ticket c examine casually d check ticket e decide e decide e decide e decide g pay compensation g pay compensation h reject h reject end 5 adcefbdeg 3 acdefbdefdbeg 2 adcefdbeg 2 adcefbdefbdeg 1 adcefdbefbdeh 1 adbefbdefdbeg 1 adcefdbefcdefdbeg 1391 (all 21 variants seen in the log) a b d register examine thoroughly check ticket e decide g pay compensation a register d check ticket b examine thoroughly e decide h reject a register b examine thoroughly d check ticket decide reject N 4 : fitness = +, precision = +, generalization = -, simplicity = - e h PAGE 25

27 Model N 1 PAGE 26 acdeh abdeg adceh abdeh acdeg adceg adbeh acdefdbeh adbeg acdefbdeh acdefbdeg acdefdbeg adcefcdeh adcefdbeh adcefbdeg acdefbdefdbeg adcefdbeg adcefbdefbdeg adcefdbefbdeh adbefbdefdbeg adcefdbefcdefdbeg # trace 1391

28 Model N 2 PAGE 27 acdeh abdeg adceh abdeh acdeg adceg adbeh acdefdbeh adbeg acdefbdeh acdefbdeg acdefdbeg adcefcdeh adcefdbeh adcefbdeg acdefbdefdbeg adcefdbeg adcefbdefbdeg adcefdbefbdeh adbefbdefdbeg adcefdbefcdefdbeg # trace 1391

29 Model N 3 PAGE 28 acdeh abdeg adceh abdeh acdeg adceg adbeh acdefdbeh adbeg acdefbdeh acdefbdeg acdefdbeg adcefcdeh adcefdbeh adcefbdeg acdefbdefdbeg adcefdbeg adcefbdefbdeg adcefdbefbdeh adbefbdefdbeg adcefdbefcdefdbeg # trace 1391

30 # trace Model N acdeh abdeg adceh 144 abdeh a register d check ticket c examine casually e decide g pay compensation acdeg adceg a register c examine casually d check ticket e decide g pay compensation adbeh acdefdbeh a d c e h 38 adbeg register check ticket examine casually decide reject 33 acdefbdeh start a register c examine casually d check ticket e decide h reject end acdefbdeg acdefdbeg 9 adcefcdeh (all 21 variants seen in the log) 8 adcefdbeh a b d register a register a register examine thoroughly d check ticket b examine thoroughly check ticket b examine thoroughly d check ticket decide decide reject N 4 : fitness = +, precision = +, generalization = -, simplicity = - e decide e e g pay compensation h reject h adcefbdeg acdefbdefdbeg adcefdbeg adcefbdefbdeg adcefdbefbdeh adbefbdefdbeg adcefdbefcdefdbeg PAGE 29

31 Process Discovery PAGE 30

32 Process Discovery (small selection) automata-based learning heuristic mining distributed genetic mining language-based regions genetic mining stochastic task graphs state-based regions LTL mining neural networks fuzzy mining mining block structures hidden Markov models α algorithm α# algorithm conformal process graph multi-phase mining partial-order based mining α++ algorithm ILP mining PAGE 31

33 Petri net view: Just discover the places Adding a place limits behavior: overfitting adding too many places underfitting adding too few places PAGE 32

34 Example: Process Discovery Using State-Based Regions a b [ a,b] e [a,e] d [a,d,e] [ ] [a] c b c d [a,c] [a,b,c] [a,b,c,d] b a p1 e p3 d start end p2 c p4 PAGE 33

35 Example of Region a b [ a,b] e [a,e] d [a,d,e] [ ] [a] c b c 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 d start end p2 c p4 PAGE 34

36 Example: Process Discovery Using Language-Based Regions A place is feasible if it can be added without disabling any of the traces in the event log. R PAGE 35

37 Conformance Checking PAGE 36

38 Replaying trace abeg abeg r=1 m= = PAGE 37

39 # trace Can be lifted to log level acdeh abdeg adceh N 1 b 144 abdeh start a register p1 p2 examine thoroughly c examine casually d check ticket p3 p4 e decide f p5 reinitiate g pay compensation h reject end acdeg adceg adbeh acdefdbeh adbeg acdefbdeh 14 acdefbdeg 11 acdefdbeg 9 adcefcdeh 8 adcefdbeh 5 adcefbdeg 3 acdefbdefdbeg 2 adcefdbeg 2 adcefbdefbdeg 1 adcefdbefbdeh 1 adbefbdefdbeg 1 adcefdbefcdefdbeg 1391 PAGE 38

40 From playing the token game to optimal alignments observed trace: abeg a b» e g a b d e g move in model only PAGE 39

41 Another alignment observed trace: abcdeg a b c d e g a b» d e g move in log only PAGE 40

42 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 both Optimal alignment describes modeled behavior closest to observed behavior PAGE 41

43 Moves have costs a»» a a a Standard cost function: c(x,») = 1 c(»,y) = 1 c(x,y) = 0, if x=y c(x,y) =, if x y PAGE a b 42

44 Non-fitting trace: abefdeg abefdeg a b» e f d» e g a b d e f d b e g 2 a b e f d e g a b»» d e g 2 PAGE 43

45 Any cost structure is possible send-letter(john,2 weeks, $400) send- (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 44

46 Fitness 1.0 start a register b examine thoroughly c examine casually d check ticket e decide f reinitiate g pay compensation h reject N 1 : fitness = +, precision = +, generalization = +, simplicity = + end # trace 455 acdeh 191 abdeg 177 adceh 144 abdeh Our A* algorithm exploits the Petri net marking equation and uses other tricks to prune the search space start start start a register c examine casually examine thoroughly b d check ticket d check ticket decide pay compensation reject N 2 : fitness = -, precision = +, generalization = -, simplicity = + a register examine casually c reinitiate decide e f h reject N 3 : fitness = +, precision = -, generalization = +, simplicity = + a register a register a register a register d check ticket c examine casually d check ticket c examine casually c examine casually d check ticket c examine casually d check ticket e e decide e decide e decide e decide g h g g h reject h reject end end pay compensation pay compensation end 111 acdeg 82 adceg 56 adbeh 47 acdefdbeh 38 adbeg 33 acdefbdeh 14 acdefbdeg 11 acdefdbeg 9 adcefcdeh 8 adcefdbeh 5 adcefbdeg 3 acdefbdefdbeg 2 adcefdbeg 2 adcefbdefbdeg 1 adcefdbefbdeh 1 adbefbdefdbeg 1 adcefdbefcdefdbeg 1391 (all 21 variants seen in the log) Aligned event log is starting point for other types of analysis. a b d register a register a register examine thoroughly d check ticket b examine thoroughly check ticket b examine thoroughly d check ticket decide decide decide reject N 4 : fitness = +, precision = +, generalization = -, simplicity = - e e e g pay compensation h reject h PAGE 45

47 Distributing/Decomposing Big Data Process Mining Problems PAGE 46

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49 What if? there are more than events? there are more than 1000 different activities? acefgijkl acddefhkjil abdefjkgil acdddefkhijl acefgijkl abefgjikl... process discovery b skip extra insurance conformance checking d change booking c4 c5 add extra insurance skip extra insurance g h i c8 c9 a c select car in book car c1 add extra insurance c2 e confirm c3 f initiate check-in c6 j check driver s license c10 l supply car out there are more than cases? c7 k charge credit card c11 PAGE 48

50 Distributed computing multicore CPU manycore GPU cluster computing grid computing cloud computing PAGE 49

51 How to distribute process discovery? PAGE 50

52 How to distribute conformance checking? abcdeg adcefbcfdeg abdceg abcdefbcdeg abdfcefdceg acdefbdceg abcdeg abdceg abdcefbdcefbdceg abcdeg abcdefbcdefbdceg abcdefbdceg acdefg adcfeg abdcefcdfeg abcdeg in a c1 b c2 c3 f c d c4 c5 e c6 g out abcdeg abdceg abcdefbcdeg abcdeg abdceg abdcefbdcefbdceg abcdeg abcdefbcdefbdceg abcdefbdceg abcdeg f occurs too often f c a b c2 c4 e g in c1 d c6 out adcefbcfdeg abdfcefdceg acdefbdceg acdefg adcfeg abdcefcdfeg b is often skipped c3 c5 PAGE 51

53 Classification based on partitioning of event log: vertical and horizontal sets of cases sets of activities PAGE 52

54 Replication: Same event log on all computing nodes Only makes sense if random elements, e.g., genetic process mining. PAGE 53

55 Vertical distribution I: Split cases arbitrarily sets of cases abcdeg abdcefbcdeg abdceg abcdefbcdeg abdcefbdceg abcdefbdceg abcdeg abdceg abdcefbdcefbdceg abcdeg abcdefbcdefbdceg abcdefbdceg abcdeg abdceg abdcefbcdeg abcdeg abcdeg abdcefbcdeg abdceg abcdefbcdeg abdcefbdceg abcdefbdceg abcdeg abdceg abdcefbdcefbdceg abcdeg abcdefbcdefbdceg abcdefbdceg abcdeg abdceg abdcefbcdeg abcdeg PAGE 54

56 Vertical distribution II: Split cases based on a specific feature abcdeg abdcefbcdeg abdceg abcdefbcdeg abdcefbdceg abcdefbdceg abcdeg abdceg abdcefbdcefbdceg abcdeg abcdefbcdefbdceg abcdefbdceg abcdeg abdceg abdcefbcdeg abcdeg abcdeg abdceg abcdeg abdceg abcdeg abcdeg abdceg abcdeg abdcefbcdeg abcdefbcdeg abdcefbdceg abcdefbdceg abcdefbdceg abdcefbcdeg abdcefbdcefbdceg abcdefbcdefbdceg PAGE 55

57 Horizontal distribution sets of activities PAGE 56

58 Horizontal distribution: The key idea projected on a,b,e,f,g projected on b,c,d,e PAGE 57

59 Passages for Horizontal Distribution PAGE 58

60 Passages PAGE 59

61 Passage P=(X,Y) causal dependency: may trigger or enable PAGE 60

62 Minimal passages a passage is minimal if it does not contain smaller passages PAGE 61

63 Passages define an equivalence relation on the edges in the graph PAGE 62

64 Minimal passage 1: ({a},{b,c}) PAGE 63

65 Minimal passage 2: ({b,c,d},{d,e,f}) PAGE 64

66 Minimal passage 3: ({e},{g}) PAGE 65

67 Minimal passage 4: ({f},{h}) PAGE 66

68 Minimal passage 5: ({g,h},{i}) PAGE 67

69 So What? Any process model can be partitioned in minimal passages. Discovery and conformance checking can be done per passage! i a b d e f g h i clouds may contain arbitrary subprocesses not k n explicitly recorded in the event log (invisible activities o or small networks used for routing, e.g. XOR/AND/ORsplit/joins) l p o c j m PAGE 68

70 Example result for Petri nets The event log fits all passages if and only if the event log fits the whole model. i a b c d e f g h i j k l m n o p o Key insight: interface transitions controlled by event log PAGE 69

71 Discovery example a g in out f f b e a g c c causal b structure obtained e using heuristics d & domain d knowledge f c a b c2 c4 e g in c1 d c6 out c3 c5 PAGE 70

72 Conformance checking acefl acddefl abdefl acdddefl acefl abefl... b skip extra insurance d change booking c4 c5 add extra insurance skip extra insurance g h i c8 c9 a c select car in book car c1 add extra insurance c2 e confirm c3 f initiate check-in c6 j check driver s license c10 l supply car out c7 k charge credit card c11 PAGE 71

73 Create Skeleton PAGE 72

74 Net fragments per passage PAGE 73

75 Initial implementation in ProM PAGE 74

76 Super linear speedups possible (even when using a single computer decomposition helps) PAGE 75

77 Conclusion PAGE 76

78 Conclusion a d f h k n o i b e g i l p o c j m Big Data PAGE 77

79 PAGE 78