Chapter 8 Mining Additional Perspectives

Transcription

1 Chapter 8 Mining Additional Perspectives prof.dr.ir. Wil van der Aalst

2 Overview Chapter 1 Introduction Part I: Preliminaries Chapter 2 Process Modeling and Analysis Chapter 3 Data Mining Part II: From Event Logs to Process Models Chapter 4 Getting the Data Chapter 5 Process Discovery: An Introduction Chapter 6 Advanced Process Discovery Techniques Part III: Beyond Process Discovery Chapter 7 Conformance Checking Chapter 8 Mining Additional Perspectives Chapter 9 Operational Support Part IV: Putting Process Mining to Work Chapter 10 Tool Support Chapter 11 Analyzing Lasagna Processes Chapter 12 Analyzing Spaghetti Processes Part V: Reflection Chapter 13 Cartography and Navigation Chapter 14 Epilogue PAGE 1

3 Mining additional perspectives (one type of enhancement, cf. repair in context of conformance checking) world business processes people machines components organizations models analyzes supports/ controls specifies configures implements analyzes software system records events, e.g., messages, transactions, etc. (process) model discovery conformance enhancement event logs PAGE 2

4 Play-In Replay: Connecting events to model elements is essential for process mining event log process model Play-Out process model event log Replay event log process model extended model showing times, frequencies, etc. diagnostics predictions recommendations PAGE 3

5 Remember: Replay! A B C D B A p1 E p3 D start end p2 C p4 PAGE 4

6 Replay can detect problems AC D Problem! token left behind B Problem! missing token A p1 E p3 D start end p2 C p4 PAGE 5

7 Replay can extract timing information A 5 B 8 C 9 D B A p1 E p3 D start 5 4 p C 9 p end PAGE 6

8 Decision mining: Red cases A B C D B A p1 E p3 D start end p2 C p4 PAGE 7

9 Decision mining: Blue cases A E D If red then B+C; If blue then E; B A p1 E p3 D start end p2 C p4 PAGE 8

10 Starting point: connected event log and model process a 1 * activity model level 1 1 b c * * case d 1 * activity instance instance level 1 1 e f * * event g 1 * attribute event level h i j k timestamp resource costs... transaction PAGE 9

11 Process the initial process model is made by hand or discovered from the event log integrated model showing multiple perspectives world business processes people machines components organizations models analyzes 5 2 (process) model supports/ controls specifies configures implements analyzes discovery 3 conformance 4 enhancement software system records events, e.g., messages, transactions, etc. event logs 1 events have attributes relating to various perspectives conformance checking is used to relate the initial model and event log the model is extended using the additional information in the event log PAGE 10

12 Attributes in event logs PAGE 11

13 Cases may also have attributes PAGE 12

14 Helicopter view: Dotted charts class each dot corresponds to an event activity : decide type : start time : :11.18 resource : Sara cost : - custid : 9911 name : Smith type : gold region : south amount : the color and shape of a dot may depend on attributes of the event time time can be absolute or relative and real or logical each line corresponds to a class, e.g., a case, a resource, a customer, or an activity PAGE 13

15 Dotted chart for a process of a housing agency using absolute time PAGE 14

16 Zooming in PAGE 15

17 Same log, relative time PAGE 16

18 Organizational mining PAGE 17

19 Resource-activity matrix mean number of times a resource performs an activity per case Activity a is executed exactly once for each case (take the sum of the first column). Pete, Mike, and Ellen are the only ones executing this activity. In 30% of the cases, a is executed by Pete, 50% is executed by Pete, and 20% is executed by Ellen. Activities e and f are always executed by Sara. Activity e is executed, on average, 2.3 times per case. Etc. PAGE 18

20 Social network analysis organizational entity (resource, person, role, department, etc.) the thickness of the arc indicates the weight of the relationship relationship w=0.90 w=0.98 y w=0.80 the size of the oval indicates the weight of the entity x w=0.15 z w=0.30 w=0.08 w=0.35 PAGE 19

21 Handover of work matrix Count the number of times work is handed over from one resource to another (on average per case). The causal dependencies in the process model are used to count handovers in the event log. PAGE 20

22 Social network based on handover of work (threshold of 0.1) Pete Sue Sean Mike Sara Ellen In this figure only the thickness of the arcs is based on frequencies. PAGE 21

23 Handover of work at role level Assistant w=5.45 w=1.5 w=0.5 w=1.3 w=3.45 w=2,95 w=0.65 Expert w=1.15 In this figure also the size of each node is based on frequencies. Manager w=3.6 w=1.15 PAGE 22

24 Profile PAGE 23

25 Social network based on similarity of profiles Pete Sean Sue Mike Ellen Sara Resources that execute similar collections of activities are related. Sara is the only resource executing e and f. Therefore, she is not connected to other resources. Self-loops are suppressed as they contain no information (self-similarity) PAGE 24

26 Discovering organizational structures Expert Sue Sean b Manager Sara examine thoroughly g start a register request p1 p2 c examine casually d check ticket p3 p4 e decide f p5 pay compensation h reject request end Assistant reinitiate request Mike Ellen Pete PAGE 25

27 Another example process model organizational model resources oe1 r1 a1 oe2 oe3 r2 r3 a2 a3 oe4 oe5 r4 a4 r5 oe6 oe7 oe8 r6 r7 a5 r8 r9 PAGE 26

28 Analyzing resource behavior, e.g., Yerkes-Dodson law of arousal PAGE 27

29 Learning time and probabilities Replay, as before, but now considering timestamps. Let us replay the first three cases in the event log: case 1 starts at time 12 and ends at time 54, case 2 starts at time 17 and ends at time 73, case 3 starts at time 25 and ends at time 98. PAGE 28

30 PAGE 29 a start register request b examine thoroughly c examine casually d check ticket decide pay compensation reject request reinitiate request e g h f end p3 p1 p2 p4 p5 1,c:19 1,s:12 2,c:23 2,s:17 3,c:30 3,s:25 1,c:32 1,s:25 3,c:65 3,s:60 2,c:38 2,s:30 3,c:35 3,s:32 1,c:33 1,s:26 2,c:32 2,s:28 3,c:40 3,s:35 3,c:67 3,s:62 3,c:55 3,s:50 1,c:40 1,s:35 2,c:59 2,s:50 3,c:50 3,s:45 3,c:87 3,s:80 1,c:54 1,s:50 2,c:73 2,s:70 3,c:98 3,s:90 1:6 2:7 3:2 3:5 1:10 2:11 3:0 3:3 1:3 2:12 3:10 3:15 1:7 2:5 3:5 3:7 1:2 2:18 3:5 3:13 1:12 2:17 3:25 1:54 2:73 3:

31 Another view on the timed replay of the first three cases a b case 1 d e h time case 2 a d c e g case 3 a b c d d e f e g PAGE 30

32 Timed replay projected onto resources Pete Mike Ellen Sue Sean Sara a a a d d b c c d e e h e f b d g e g time PAGE 31

33 Decision mining start a register request decision point #1 c1 c2 b examine thoroughly c examine casually d check ticket c3 c4 e decide f decision point #2 c5 reinitiate request g pay compensation h reject request end PAGE 32

34 Example: XOR-split type region amount activity gold south z silver north z gold south y silver west z silver east z silver south z gold north y silver west z silver south z gold west y silver south z gold south z silver north z gold south y silver north z silver west z gold east y type=gold and amount<500 x type=silver or amount 500 x type=gold and amount<500 x type=silver or amount 500 y z y type=gold and amount<500 type=silver or amount 500 z y z What are the features (predictor variables) influencing the decision? A classification technique like decision tree learning can be used to find such rules: :explain response variable (dependent variable) in terms of predictor variables (independent variables). PAGE 33

35 Example: OR-split type region amount activity gold south y and z silver north y and z gold south just y silver west just z silver east y and z silver south y and z gold north just y silver west y and z silver south just z type=gold or amount<500 x type=silver or amount 500 x y z y type=gold or amount<500 type=silver or amount 500 z PAGE 34

36 Classification in process mining The application of classification techniques like decision tree learning is not limited to decision mining based on event/case data only. Additional predictor variables may be used: behavioral information (count number of loops) performance information (processing times) contextual information (weather, queues, etc.) Alternative response variables can be analyzed: uncover reasons for non-conformance (split instances in two groups) uncover reasons for delays PAGE 35

37 Bringing it all together Step 1: obtain an event log event log b Step 2: create or discover a process model Step 3: connect events in the log to activities in the model start A a register request c1 c2 examine thoroughly A c examine casually A d check ticket c3 c4 decide f M e c5 reinitiate request g pay compensation h reject request end Step 4: extend the model Role A: Assistant Role E: Expert Role M: Manager Pete Sue Sara add organizational perspective add time perspective add case perspective Step 5: return integrated model add other perspectives start Mike Ellen A a register request Sean c1 c2 E b examine thoroughly A c examine casually A d check ticket c3 c4 M e decide f M c5 reinitiate request A g pay compensation A h reject request end PAGE 36