Semantical Vacuity Detection in Declarative Process Mining
Download as PPTX, PDF0 likes311 views
The document discusses the detection of semantical vacuity in declarative process mining, introducing the concept of constraints and event logs in flexible process modeling. It highlights the current limitations of existing techniques in handling non-standard declare templates and proposes a need for a comprehensive framework for vacuity detection. The research aims to improve declarative process mining by addressing these challenges and exploring new template formulations.
Semantical Vacuity Detection in Declarative Process Mining
- 1. Semantical Vacuity Detection in Declarative Process Mining Fabrizio Maria Maggi, Marco Montali, Claudio Di Ciccio and Jan Mendling 14th International Conference on Business Process Management Rio de Janeiro, Brazil claudio.di.ciccio@wu.ac.at
- 2. Prelude Every unicorn is green SEITE 2 )(. xxUnicorn
- 3. Prelude Every unicorn is green SEITE 3 )(. xxUnicorn )(xGreen It is true. After all, have you ever seen a unicorn? We thank Prof. M. Lenzerini for inspiring this example
- 5. Declarative process modelling “Open model” Specify constraints for permitted behaviour Every execution that complies with them is acceptable Works best with flexible processes The set of DECLARE templates is extendible SEITE 5
- 6. A fragment of declarative process model If an abstract is submitted, a new paper had been written or will be written After the paper submission, a confirmation email is sent After the paper submission, the paper will be reviewed; there can be no review without a preceding submission A paper can be accepted only after it has been reviewed After the rejection, no further submission follows Paper cannot be both accepted and rejected SEITE 6 Submit abstract Write new paper Submit paper Send confirmation email Submit paper Review paper Review paper Accept paper Reject paper Submit paper Accept paper Reject paper = activation task Responded existence(Submit abstract, Write new paper) Response(Submit paper, Send confirmation email) Succession(Submit paper, Review paper) Precedence(Review paper, Accept paper) Not succession(Reject paper, Submit paper) Not co-existence(Accept paper, Reject paper) Template Tasks
- 7. A fragment of declarative process model SEITE 7 Submit abstract Write new paper Submit paper Send confirmation email Review paper Accept paper Reject paper
- 8. Every DECLARE constraint can be abstracted as an FSA SEITE 8 Accepting state Any task but ‘a’ or ‘b’ Any task but ‘b’Any task State FSA: Deterministic Finite State Automaton Task ‘a’Init
- 9. Declarative process mining Process discovery with DECLARE
- 10. Declarative process discovery SEITE 10 ? Objective: understanding the constraints that best define the allowed behaviour of the process behind the event log
- 11. Mining declarative processes: ingredients “Submit paper”, “Write new paper”, “Accept paper”, … SEITE 11 s, w, y, … Activities Process alphabet
- 13. Discovering a DECLARE model: example Example event log w a s e s e r r r y e s e a s e r r y e s e s e w w w s e r r r r x e s e r x e w a s e r r r y e s e w a s e r r r y e s e w a s e r r r x e s e s e s e r r r e x e a w s e s e r r e x e w a e s e r r r e y e s e R.Ex.(s,y) SEITE 13 Res.(w,y) Res.(s,e) Support: 50% 70% 100%
- 14. Discovering a DECLARE model: example Example event log w a s e s e r r r y e s e a s e r r y e s e s e w w w s e r r r r x e s e r x e w a s e r r r y e s e w a s e r r r y e s e w a s e r r r x e s e s e s e r r r e x e a w s e s e r r e x e w a e s e r r r e y e s e R.Ex.(s,y) SEITE 14 Res.(w,y) Res.(s,e) Support: 50% 70% 100%
- 15. Discovering a DECLARE model: example Example event log w a s e s e r r r y e s e a s e r r y e s e s e w w w s e r r r r x e s e r x e w a s e r r r y e s e w a s e r r r y e s e w a s e r r r x e s e s e s e r r r e x e a w s e s e r r e x e w a e s e r r r e y e s e R.Ex.(s,y) SEITE 15 Res.(w,y) Res.(s,e) Support: 50% 70% 100%
- 16. A fragment of declarative process model SEITE 16 Submit abstract (a) Write new paper (w) Submit paper (s) Send conf. email (e) Review paper (r) Accept paper (y) Reject paper (x) ?
- 17. Discovering a DECLARE model: example Example event log w a s e s e r r r y e s e a s e r r y e s e s e w w w s e r r r r x e s e r x e w a s e r r r y e s e w a s e r r r y e s e w a s e r r r x e s e s e s e r r r e x e a w s e s e r r e x e w a e s e r r r e y e s e R.Ex.(s,y) SEITE 17 Res.(w,y) Res.( ,y) Support: 50% 70% 100%
- 18. A fragment of declarative process model SEITE 18 Submit abstract (a) Write new paper (w) Submit paper (s) Send conf. email (e) Review paper (r) Accept paper (y) Reject paper (x) ?
- 19. Vacuity detection A constraint is vacuously satisfied by a trace if it is verified yet never “triggered” E.g., Response(w,y) is vacuously satisfied in s e r x e a s e r r y e s e s e For standard DECLARE templates, techniques exist that detect the vacuous satisfaction of constraints SEITE 19
- 20. Discovering a DECLARE model: example with vacuity check (~) Example event log w a s e s e r r r y e s e a s e r r y e s e s e w w w s e r r r r x e s e r x e w a s e r r r y e s e w a s e r r r y e s e w a s e r r r x e s e s e s e r r r e x e a w s e s e r r e x e w a e s e r r r e y e s e R.Ex.(s,y) SEITE 20 Res.(w,y) ~ ~ ~ Res.( ,y) ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ Support: 50% 70% 100%
- 21. Discovering a DECLARE model: example with vacuity check (~) Example event log w a s e s e r r r y e s e a s e r r y e s e s e w w w s e r r r r x e s e r x e w a s e r r r y e s e w a s e r r r y e s e w a s e r r r x e s e s e s e r r r e x e a w s e s e r r e x e w a e s e r r r e y e s e R.Ex.(s,y) SEITE 21 Res.(w,y) ~ ~ ~ Res.( ,y) ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ Support: 50% 70% 100%
- 22. Res.(w,y) ~ ~ ~ Discovering a DECLARE model: example Example event log w a s e s e r r r y e s e a s e r r y e s e s e w w w s e r r r r x e s e r x e w a s e r r r y e s e w a s e r r r y e s e w a s e r r r x e s e s e s e r r r e x e a w s e s e r r e x e w a e s e r r r e y e s e R.Ex.(s,y) SEITE 22 Res.(s,e) Support: 50% 70% 100%
- 23. Summary of the status quo DECLARE mining techniques return a model made of those constraints that have a sufficient fraction of fulfilling traces Vacuity check already works for standard DECLARE templates with ad-hoc procedures DECLARE is extendible What happens with non-standard DECLARE templates? SEITE 23
- 24. Declarative process mining What happens with non-standard DECLARE templates?
- 25. The problem A general framework for the vacuity detection in the context of declarative process mining is missing. Existing techniques are either syntax-based… Different formulations of the same constraints lead to different results ... Or ad-hoc Not extendible Result: Mining non-standard Declare constraints can lead to loads of vacuously satisfied constraints returned as if they were interesting discovery results SEITE 25
- 26. An example of new template: “Progression response 3:2” SEITE 26 Prog.resp3:2(u1,u2,u3, v1,v2) Prog.resp3:2(Write paper,Submit abstract,Submit paper, Send notification email,Accept paper) Example:
- 27. Discovering a DECLARE model: example Example event log w a s e s e r r r y e s e a s e r r y e s e s e w w w s e r r r r x e s e r x e w a s e r r r y e s e w a s e r r r y e s e w a s e r r r x e s e s e s e r r r e x e a w s e s e r r e x e w a e s e r r r e y e s e Prog.resp3:2(w,a,s, e,y) SEITE 27 Support: 90%
- 28. Discovering a DECLARE model: example Example event log w a s e s e r r r y e s e a s e r r y e s e s e w w w s e r r r r x e s e r x e w a s e r r r y e s e w a s e r r r y e s e w a s e r r r x e s e s e s e r r r e x e a w s e s e r r e x e w a e s e r r r e y e s e Prog.resp3:2(w,a,s, e,y) SEITE 28 Support: 90%
- 29. Discovering a DECLARE model: example Example event log w a s e s e r r r y e s e a s e r r y e s e s e w w w s e r r r r x e s e r x e w a s e r r r y e s e w a s e r r r y e s e w a s e r r r x e s e s e s e r r r e x e a w s e s e r r e x e w a e s e r r r e y e s e Prog.resp3:2(w,a,s, e,y) SEITE 29 Support: 90%
- 30. Discovering a DECLARE model: example Example event log w a s e s e r r r y e s e a s e r r y e s e s e w w w s e r r r r x e s e r x e w a s e r r r y e s e w a s e r r r y e s e w a s e r r r x e s e s e s e r r r e x e a w s e s e r r e x e w a e s e r r r e y e s e Prog.resp3:2(w,a,s, e,y) SEITE 30 Support: 90%
- 31. Discovering a DECLARE model: example Example event log w a s e s e r r r y e s e a s e r r y e s e s e w w w s e r r r r x e s e r x e w a s e r r r y e s e w a s e r r r y e s e w a s e r r r x e s e s e s e r r r e x e a w s e s e r r e x e w a e s e r r r e y e s e Prog.resp3:2(w,a,s, e,y) SEITE 31 Support: 90%
- 32. Discovering a DECLARE model: example Example event log w a s e s e r r r y e s e a s e r r y e s e s e w w w s e r r r r x e s e r x e w a s e r r r y e s e w a s e r r r y e s e w a s e r r r x e s e s e s e r r r e x e a w s e s e r r e x e w a e s e r r r e y e s e Prog.resp3:2(w,a,s, e,y) SEITE 32 Support: 90%
- 33. Discovering a DECLARE model: example Example event log w a s e s e r r r y e s e a s e r r y e s e s e w w w s e r r r r x e s e r x e w a s e r r r y e s e w a s e r r r y e s e w a s e r r r x e s e s e s e r r r e x e a w s e s e r r e x e w a e s e r r r e y e s e Prog.resp3:2(w,a,s, e,y) SEITE 33 Support: 90%
- 34. Discovering a DECLARE model: example Example event log w a s e s e r r r y e s e a s e r r y e s e s e w w w s e r r r r x e s e r x e w a s e r r r y e s e w a s e r r r y e s e w a s e r r r x e s e s e s e r r r e x e a w s e s e r r e x e w a e s e r r r e y e s e Prog.resp3:2(w,a,s, e,y) SEITE 34 Support: 90%
- 35. Discovering a DECLARE model: example Example event log w a s e s e r r r y e s e a s e r r y e s e s e w w w s e r r r r x e s e r x e w a s e r r r y e s e w a s e r r r y e s e w a s e r r r x e s e s e s e r r r e x e a w s e s e r r e x e w a e s e r r r e y e s e Prog.resp3:2(w,a,s, e,y) SEITE 35 Support: 90%
- 36. Discovering a DECLARE model: example Example event log w a s e s e r r r y e s e a s e r r y e s e s e w w w s e r r r r x e s e r x e w a s e r r r y e s e w a s e r r r y e s e w a s e r r r x e s e s e s e r r r e x e a w s e s e r r e x e w a e s e r r r e y e s e Prog.resp3:2(w,a,s, e,y) SEITE 36 Support: 90%
- 37. Discovering a DECLARE model: example Example event log w a s e s e r r r y e s e a s e r r y e s e s e w w w s e r r r r x e s e r x e w a s e r r r y e s e w a s e r r r y e s e w a s e r r r x e s e s e s e r r r e x e a w s e s e r r e x e w a e s e r r r e y e s e Prog.resp3:2(w,a,s, e,y) SEITE 37 Support: 90%
- 38. An example of new template: “Progression response 3:2” SEITE 38 Prog.resp3:2(u1,u2,u3, v1,v2) Prog.resp3:2(Submit paper,Write paper,Submit abstract, Reject paper,Accept paper) Example: It makes no sense. Yet…
- 39. Discovering a DECLARE model: example Example event log w a s e s e r r r y e s e a s e r r y e s e s e w w w s e r r r r x e s e r x e w a s e r r r y e s e w a s e r r r y e s e w a s e r r r x e s e s e s e r r r e x e a w s e s e r r e x e w a e s e r r r e y e s e Prog.resp3:2(s,w,a, x,y) SEITE 39 Support: 100% “Impossible” activations make for a support of 100%
- 40. Discovering a DECLARE model: example Example event log w a s e s e r r r y e s e a s e r r y e s e s e w w w s e r r r r x e s e r x e w a s e r r r y e s e w a s e r r r y e s e w a s e r r r x e s e s e s e r r r e x e a w s e s e r r e x e w a e s e r r r e y e s e Prog.resp3:2(w,s, , y,e) SEITE 40 Support: 100%
- 41. Summary of the status quo DECLARE mining techniques return a model made of those constraints that have a sufficient count of fulfilling traces Vacuity check already works for standard DECLARE templates with ad-hoc procedures DECLARE is extendible With non-standard DECLARE templates, should unicorns hold the truth? SEITE 41
- 42. Problem How to return only interesting constraints when looking for any template?
- 43. The solution: sketch (1) SEITE 43 For every constraint FSA, activation-aware automata are built, i.e., states get labelled with: 1. the satisfaction status reached so far 1. temporarily/permanently satisfied/violated: ts, ps, tv, pv 2. the allowed tasks for the future moves Satisfaction Allowed tasksSatisfaction
- 44. The solution: sketch (1) SEITE 44 Satisfaction Allowed tasksSatisfaction
- 46. The solution: sketch (2) SEITE 46 Task executions are marked as relevant when they make the satisfaction status change, or they make the allowed tasks change Irrelevant Relevant Allowed tasksSatisfaction Irrelevant Relevant
- 47. The solution: sketch (3) SEITE 47 A trace is an interesting witness when a relevant task execution is performed A trace satisfies a constraint when its replay terminates in an accepting state We look for interesting traces that satisfy the constraints
- 48. Discovering a DECLARE model: example Example event log w a s e s e r r r y e s e a s e r r y e s e s e w w w s e r r r r x e s e r x e w a s e r r r y e s e w a s e r r r y e s e w a s e r r r x e s e s e s e r r r e x e a w s e s e r r e x e w a e s e r r r e y e s e Prog.resp3:2(w,a,s, e,y) ~ ~ ~ ~ ~ SEITE 48 Support: 90%
- 49. Discovering a DECLARE model: example Example event log w a s e s e r r r y e s e a s e r r y e s e s e w w w s e r r r r x e s e r x e w a s e r r r y e s e w a s e r r r y e s e w a s e r r r x e s e s e s e r r r e x e a w s e s e r r e x e w a e s e r r r e y e s e Prog.resp3:2(w,a,s, e,y) ~ ~ ~ ~ ~ SEITE 49 Support: 90%
- 50. Discovering a DECLARE model: example Example event log w a s e s e r r r y e s e a s e r r y e s e s e w w w s e r r r r x e s e r x e w a s e r r r y e s e w a s e r r r y e s e w a s e r r r x e s e s e s e r r r e x e a w s e s e r r e x e w a e s e r r r e y e s e Prog.resp3:2(w,s, , y,e) ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ SEITE 50 Support: 100%
- 51. Discovering a DECLARE model: example Example event log w a s e s e r r r y e s e a s e r r y e s e s e w w w s e r r r r x e s e r x e w a s e r r r y e s e w a s e r r r y e s e w a s e r r r x e s e s e s e r r r e x e a w s e s e r r e x e w a e s e r r r e y e s e Prog.resp3:2(w,s, , y,e) ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ SEITE 51 Support: 100%
- 52. Discovering a DECLARE model: example with relevance check Example event log w a s e s e r r r y e s e a s e r r y e s e s e w w w s e r r r r x e s e r x e w a s e r r r y e s e w a s e r r r y e s e w a s e r r r x e s e s e s e r r r e x e a w s e s e r r e x e w a e s e r r r e y e s e SEITE 52 Support: 70% Res.(w,y) ~ ~ ~
- 53. Discovering a DECLARE model: example with relevance check Example event log w a s e s e r r r y e s e a s e r r y e s e s e w w w s e r r r r x e s e r x e w a s e r r r y e s e w a s e r r r y e s e w a s e r r r x e s e s e s e r r r e x e a w s e s e r r e x e w a e s e r r r e y e s e SEITE 53 Support: 70% Res.(w,y) ~ ~ ~
- 54. Discovering a DECLARE model: example with vacuity check (~) Example event log w a s e s e r r r y e s e a s e r r y e s e s e w w w s e r r r r x e s e r x e w a s e r r r y e s e w a s e r r r y e s e w a s e r r r x e s e s e s e r r r e x e a w s e s e r r e x e w a e s e r r r e y e s e SEITE 54 Support: Res.( ,y) ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 100%
- 55. Discovering a DECLARE model: example with vacuity check (~) Example event log w a s e s e r r r y e s e a s e r r y e s e s e w w w s e r r r r x e s e r x e w a s e r r r y e s e w a s e r r r y e s e w a s e r r r x e s e s e s e r r r e x e a w s e s e r r e x e w a e s e r r r e y e s e SEITE 55 Support: Res.( ,y) ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 100%
- 56. The algorithm: recap SEITE 56 Response(a,b) Responded existence(a,b) … Prog.resp3:2(u1,u2,u3,v1,v2) ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ …
- 57. Ending Evaluation, conclusion and future work
- 58. Evaluation SEITE 58 Implemented in Java Extension of MINERful Real-world logs BPIC 2013: 9,442 msec 426 for the automata 9,016 for the checking
- 59. Conclusion Contribution: A generalised approach for the discovery of interesting declarative constraints Check based on the relevance of task executions w.r.t. the semantics of the constraints Future work: Differentiation of positive and negative interestingness Extended declarative mining integrating general vacuity detection Data-awareness SEITE 59
- 60. Semantical Vacuity Detection in Declarative Process Mining Fabrizio Maria Maggi, Marco Montali, Claudio Di Ciccio and Jan Mendling 14th International Conference on Business Process Management Rio de Janeiro, Brazil No unicorns were harmed in the making of this paper
- 61. Semantical Vacuity Detection in Declarative Process Mining Fabrizio Maria Maggi, Marco Montali, Claudio Di Ciccio and Jan Mendling Extra slides
- 62. A fragment of declarative process model SEITE 62 Submit abstract Write new paper Submit paper Send confirmation email Submit paper Review paper Review paper Accept paper Reject paper Submit paper Accept paper Reject paper Responded existence(Submit abstract, Write new paper) Response(Submit paper, Send confirmation email) Succession(Submit paper, Review paper) Precedence(Review paper, Accept paper) Not succession(Reject paper, Submit paper) Not co-existence(Accept paper, Reject paper) Template Tasks = activation task
- 63. A fragment of declarative process model SEITE 63 Submit abstract (a) Write new paper (w) Submit paper (s) Send conf. email (e) Review paper (r) Accept paper (y) Reject paper (x)
- 64. Semantics of Declare: LTL and LTLf Linear Temporal Logic (LTL) initially was a specification language for the execution of (endless) concurrent programs (Pnueli, 1977) Syntax (let A be a propositional symbol): DECLARE was initially based on LTL SEITE 64 “Until” “Eventually”“Always” “Next”
- 66. Declarative process modelling “Open model” Specify constraints for permitted behaviour Every execution that complies with them is acceptable Works best with flexible processes The set of DECLARE templates is extendible SEITE 66
- 67. Extendibility of DECLARE: A clear business impact SEITE 67
- 68. Semantics of Declare: LTL and LTLf Linear Temporal Logic (LTL) initially was a specification language for the execution of (endless) concurrent programs (Pnueli, 1977) Syntax (let A be a propositional symbol): Interpretation over infinite traces, i.e., an infinite sequence of consecutive instants of time LTLf formulae are meant to be interpreted over finite traces “Until” “Eventually”“Always” “Next” SEITE 68
- 72. Semantics of Declare: FOL over finite traces SEITE 72
- 73. Semantics of Declare: Regular expressions SEITE 73
- 74. More alternatives for DECLARE spec.: A clear business impact SEITE 74
- 75. Discovering a DECLARE model: example with relevance check Example event log w a s e s e r r r y e s e a s e r r y e s e s e w w w s e r r r r x e s e r x e w a s e r r r y e s e w a s e r r r y e s e w a s e r r r x e s e s e s e r r r e x e a w s e s e r r e x e w a e s e r r r e y e s e SEITE 75 Support: Res.(s,e) 100%