This document discusses user-guided declarative process discovery. Declarative process discovery describes process behavior as rules rather than explicitly specifying all possible behaviors. The core algorithm discovers constraints from event logs. Users can guide discovery by tuning parameters like percentage of events and instances. The paper also discusses techniques for handling truncated logs and detecting vacuously satisfied constraints. The goal is to discover accurate declarative models while allowing user steering toward desired properties.

1. User-Guided Discovery of
Declarative Process Models
Fabrizio Maria Maggi, Arjan Mooij,
Wil van der Aalst

2. Environment with a lot of variability
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3. Environment with a lot of variability
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4. Environment with a lot of variability
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5. Environment with a lot of variability
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6. Environment with a lot of variability
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7. Discovery of Spaghetti-like models
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8. Discovery of Spaghetti-like models
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9. Declarative approaches
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10. Declarative process discovery
• Avoid the discovery of spaghetti-like models
• Traditional discovery techniques explicitly specify all
possible behaviours (closed models)
• Declarative process discovery: process behaviour
described as a compact set of rules (open models)
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11. Declarative process discovery
• Possibility to guide
the discovery process
towards specific
properties of interest
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12. Declarative process discovery
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13. Declarative process discovery
A is always
eventually
followed by
B
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14. Declarative process discovery
A is always
eventually
followed by
B
A or B
always
occur but
never
together
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15. Declarative process discovery
A is always
eventually
followed by
B
A or B
always
occur but
never
together
A and B
never
occur in
sequence
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16. Declare
• A is always eventually followed by B
• RESPONSE
• User-friendly graphical representation
• Semantics specified through LTL (for finite
traces)
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17. Core algorithm
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LOG

18. User-guided discovery
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19. Core algorithm
• W = {(A C B C), (C B A C), (A C A C A C B)}
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20. Core algorithm
• W = {(A C B C), (C B A C), (A C A C A C B)}
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21. Core algorithm
• W = {(A C B C), (C B A C), (A C A C A C B)}
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22. Core algorithm
• W = {(A C B C), (C B A C), (A C A C A C B)}
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23. Core algorithm
• W = {(A C B C), (C B A C), (A C A C A C B)}
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24. Tuning the discovery process: PoE
• Percentage of Events (PoE) avoids the discovery of less-relevant
constraints referring to event classes which rarely
occur in the log
• This parameter has also a positive effect on the execution
time of the algorithm
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25. PoE parameter
• W = {(A C B C), (C B A C), (A C A C A C B)}
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26. PoE parameter
• W = {(A C B C), (C B A C), (A C A C A C B)}
− f(A) = 5/15
− f(B) = 3/15
− f(C) = 7/15
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27. PoE parameter
• W = {(A C B C), (C B A C), (A C A C A C B)}
− f(A) = 5/15
− f(B) = 3/15
− f(C) = 7/15
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28. PoE parameter
• W = {(A C B C), (C B A C), (A C A C A C B)}
− f(A) = 5/15
− f(B) = 3/15
− f(C) = 7/15
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29. PoE parameter
• W = {(A C B C), (C B A C), (A C A C A C B)}
− f(A) = 5/15
− f(B) = 3/15
− f(C) = 7/15
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30. Tuning the discovery process: PoI
• Percentage of Instances (PoI) specifies that a constraint can
still be discovered even if it does not hold for all process
instances of the log
• This parameter is useful in case of noisy logs, where rules
are violated in exceptional cases, but hold for most cases
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31. PoI parameter
• W = {(A C B C), (C B A C), (A C A C A C B)}
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32. PoI parameter
• W = {(A C B C), (C B A C), (A C A C A C B)}
2/3 3/3
1/3 2/3
1/3 1/3
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33. PoI parameter
• W = {(A C B C), (C B A C), (A C A C A C B)}
2/3 3/3
1/3 2/3
1/3 1/3
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34. Truncated semantics for DECLARE constraints
• W = {(A C D B C D A E F B A),
(C A D B C A D C B F D A D B C A D E F),
(A C B D A E B F A E F)}
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35. Truncated semantics for DECLARE constraints
• W = {(A C D B C D A E F B A),
(C A D B C A D C B F D A D B C A D E F),
(A C B D A E B F A E F)}
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36. Truncated semantics for DECLARE constraints
• Relevant when logs are not complete
• Literature on Truncated Semantics
• C. Eisner, D. Fisman, J. Havlicek, A. Mcisaac, Y. Lustig, and D. V.
Campenhout, “Reasoning with Temporal Logic on Truncated
Paths,” in In CAV Proceedings, LNCS 2725, pp. 27–40, 2003
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37. Truncated semantics for DECLARE constraints
• After every prefix, four evaluations of a constraint
− Satisfied : independent of future
− Temporarily satisfied : satisfied if this is the end of the log
− Temporarily violated : violated if this is the end of the log
− Violated : independent of future
• Three kinds of semantics:
• Weak: temporarily xxx  satisfied
• Neutral: temporarily xxx  xxx
• Strong: temporarily xxx  violated
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38. Truncated semantics for DECLARE constraints
• W = {(A C D B C D A E F B A),
(C A D B C A D C B F D A D B C A D E F),
(A C B D A E B F A E F)}
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39. Truncated semantics for DECLARE constraints
• W = {(A C D B C D A E F B A),
(C A D B C A D C B F D A D B C A D E F),
(A C B D A E B F A E F)}
(Weak semantics)
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40. Vacuity detection in DECLARE discovery
• W = {(C B C B E F ),
(C B C B C F B C B E F), (C B E F E F)}
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41. Vacuity detection in DECLARE discovery
• W = {(C B C B E F ),
(C B C B C F B C B E F), (C B E F E F)}
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42. Vacuity detection in DECLARE discovery
• Literature on Vacuity Detection
• O. Kupferman and M. Y. Vardi, “Vacuity Detection in Temporal
Model Checking,” International Journal STTT, vol. 4, pp. 224–233,
2003
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43. Vacuity detection in DECLARE discovery
• A constraint is vacuously satisfied if the constraint
is not really “activated”
• Instead of checking the validity of a constraint c we
check the validity of witness(c) to be sure that the
constraint is non-vacuously satisfied
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44. Vacuity detection in DECLARE discovery
c
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45. Vacuity detection in DECLARE discovery
witness(c)
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46. Vacuity detection in DECLARE discovery
• W = {(C B C B E F ),
(C B C B C F B C B E F), (C B E F E F)}
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47. Vacuity detection in DECLARE discovery
• W = {(C B C B E F ),
(C B C B C F B C B E F), (C B E F E F)}
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48. Conclusion
• Novel approach to discover declarative models from
logs that allows users to guide the discovery
process towards specific properties
• Results on truncated semantics can be used to
obtain significant results in the case that only partial
logs are available
• Vacuity detection to identify the percentage of
process instances where a constraint is really
activated
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49. Present and future work
• Better performance of the discovery algorithm
• equivalent combinations
• combination of event classes occurring in the same
trace
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50. Present and future work
• Application of the approach to several case studies
• Given a constraint and a process instance where it is
non-vacuously satisfied  how many times it has
been “activated” in the process instance
• Given a constraint and a process instance where it is
violated  level of “healthiness” of the process
instance based on the number of violations
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51. Visit the website
http://www.win.tue.nl/declare/declare-miner/
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