Slides of the presentation held at the Humboldt University of Berlin on 2016, December the 7th. Abstract: The declarative modelling of business processes is based upon the specification of behavioural rules that constrain the work-flows enactment. It is meant not to explicitly specify every possible execution path from the beginning to the end: The carry-out of the process is up to the actors, who can vary the execution dynamics as long as they do not violate the constraints imposed by the declarative model. The constraints specify the conditions that require or forbid the execution of activities, either considering them singularly or depending on the occurrence of other ones. In this talk, the recent advancements in the automated discovery of declarative control flows from event logs are discussed, together with open challenges in the field.

1. The Automated Discovery of Declarative Process
Models
Claudio Di Ciccio
claudio.di.ciccio@wu.ac.at
Humboldt-Universität zu Berlin, 7 December 2016

2. www.wu.ac.at
www.wu.ac.at/infobiz
www.wu.ac.at/infobiz/team/diciccio

3. Control-flow discovery
?
Objective: understanding the
temporal structure that best
describes the process behind
the event log
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4. Knowledge-intensive
Processes
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5. Knowledge-intensive
Processes
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6. Mining flexible processes
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7. Declarative modelling of
processes
 Usage of constraints
 “Open model”
 Declare
 state-of-the-art language
If A is performed,
B must be performed,
no matter if before or afterwards
(responded existence)
Whenever B is performed,
C must be performed afterwards
and B can not be repeated
until C is done
(alternate response)
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8. Workflow Nets
as process models
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9. Imperative v declarative
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10. Declarative processes
DECLARE

11. 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
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12. 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
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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

13. A fragment of declarative
process model
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Submit abstract
Write new paper Submit paper
Send
confirmation
email
Review paper Accept paper
Reject paper

14. Declare:
Existence Constraint Templates
Existence(n, A)
Activity A occurs at least n times in the process instance
BCAAC ✓ BCAAAC ✓ BCAC ✗ (for n = 2)
Absence(A)
Activity A does not occur in the process instance
BCC ✓ BCAC ✗
Absence(n+1, A)
Activity A occurs at most n-1 times in the process instance
BCAAC ✗ BCAC ✓ BCC ✓ (for n = 2)
Exactly(n, A)
Activity A occurs exactly n times in the process instance
BCAAC ✓ BCAAAC ✗ BCAC ✗ (for n = 2)
Init(A)
Activity A is the first to occur in each process instance
BCAAC ✗ ACAAAC ✓ BCC ✗
Absence(2, A) ≡ AtMostOne(A)
Existence(1, A) ≡ Participation(A)
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15. Declare:
Relation Constraint Templates
RespondedExistence(A, B)
If A occurs in the process instance, then B occurs as well
CAC ✗ CAACB ✓
BCAC ✓ BCC ✓
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16. Declare:
Relation Constraint Templates
RespondedExistence(A, B)
If A occurs in the process instance, then B occurs as well
CAC ✗ CAACB ✓
BCAC ✓ BCC ✓
Response(A, B)
If A occurs in the process instance, then B occurs after A
BCAAC ✗ CAACB ✓
CAC ✗ BCC ✓
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17. Declare:
Relation Constraint Templates
RespondedExistence(A, B)
If A occurs in the process instance, then B occurs as well
CAC ✗ CAACB ✓
BCAC ✓ BCC ✓
Response(A, B)
If A occurs in the process instance, then B occurs after A
BCAAC ✗ CAACB ✓
CAC ✗ BCC ✓
AlternateResponse(A, B)
Each time A occurs in the process instance, then B occurs
afterwards, before A recurs
BCAAC ✗ CAACB ✗ CACB ✓
CABCA ✗ BCC ✓ CACBBAB ✓
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18. Declare:
Relation Constraint Templates
RespondedExistence(A, B)
If A occurs in the process instance, then B occurs as well
CAC ✗ CAACB ✓
BCAC ✓ BCC ✓
Response(A, B)
If A occurs in the process instance, then B occurs after A
BCAAC ✗ CAACB ✓
CAC ✗ BCC ✓
AlternateResponse(A, B)
Each time A occurs in the process instance, then B occurs
afterwards, before A recurs
BCAAC ✗ CAACB ✗ CACB ✓
CABCA ✗ BCC ✓ CACBBAB ✓
ChainResponse(A, B)
Each time A occurs in the process instance, then B occurs
immediately afterwards
BCAAC ✗ BCAABC ✗ BCABABC ✓
Activation Target

19. Declare:
Relation Constraint Templates
RespondedExistence(B, A)
If B occurs in the process instance, then A occurs as well
CAC ✓ CAACB ✓
BCAC ✓ BCC ✗
Precedence(A, B)
B occurs in the process instance only if preceded by A
BCAAC ✗ CAACB ✓
CAC ✓ BCC ✓
AlternatePrecedence(A, B)
Each time B occurs in the process instance, it is preceded by A
and no other B can recur in between
BCAAC ✗ CAACB ✓ CACB ✓
CABCA ✓ BCC ✗ CACBAB ✓
ChainPrecedence(A, B)
Each time B occurs in the process instance, then B occurs
immediately beforehand
BCAAC ✗ BCAABC ✗ CABABCA ✓
Target Activation

20. Declare:
Relation Constraint Templates
CoExistence(A, B) ≡ Resp’dEx.(A, B) ∧ Resp’dEx.(B, A)
If B occurs in the process instance, then A occurs, and viceversa
CAC ✗ CAACB ✓
BCAC ✓ BCC ✗
Succession(A, B) ≡ Response(A, B) ∧ Precedence(A, B)
A occurs if and only if it is followed by B in the process instance
BCAAC ✗ CAACB ✓
CAC ✗ BCC ✗
AlternateSuccession(A, B) ≡ Alt.Resp.(A, B) ∧ Alt.Prec.(A, B)
A and B occur in the process instance if and only if the latter
follows the former, and they alternate each other in the trace
BCAAC ✗ CAACB ✗ CACB ✓
CABCA ✗ BCC ✗ CACBAB ✓
ChainSuccession(A, B) ≡ ChainResp.(A, B) ∧ ChainPrec.(A, B)
A and B occur in the process instance if and only if the latter
immediately follows the former
BCAAC ✗ BCAABC ✗ CABABC ✓
Activation Target
Target Activation

21. Declare: Negative
Relation Constraint Templates
NotCoExistence(A, B)
A and B never occur together in the process instance
CAC ✓ CAACB ✗
BCAC ✗ BCC ✓
NotSuccession(A, B)
A can never occur before B in the process instance
BCAAC ✓ CAACB ✗
CAC ✓ BCC ✓
NotChainSuccession(A, B)
A and B occur in the process instance if and only if the latter
does not immediately follows the former
BCAAC ✓ BCAABC ✗ CBACBA ✓
Activation Target
Target Activation

22. Subsumption hierarchy
of constraint templates
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23. Declarative processes
Discovery of DECLARE models

24. Constraints mining
?
Objective: understanding the
constraints that best define
the allowed behaviour of the
process behind the event log
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25. Mining declarative processes:
ingredients
“Submit abstract”,
“Submit paper”,
“Accept paper”,
…
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A,
B,
C,
…
Activities Process alphabet

26. Mining declarative processes
RespondedExistence(A, B) ?
RespondedExistence(A, C) ?
…
Response(A, B) ?
Response(A, C) ?
…
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• Support:
fraction of cases fulfilling the constraint
• Confidence:
support scaled by fraction of traces in
which the activation occurs
• Interest factor:
confidence scaled by fraction of traces in
which the target occurs
Support Conf. I.F.

27. Mining declarative processes
RespondedExistence(A, B) ?
RespondedExistence(A, C) ?
…
Response(A, B) ?
Response(A, C) ?
…
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Support Conf. I.F.
• Support:
fraction of cases fulfilling the constraint
• Confidence:
support scaled by fraction of traces in
which the activation occurs
• Interest factor:
confidence scaled by fraction of traces in
which the target occurs

28. Mining declarative processes
RespondedExistence(A, B) 
RespondedExistence(A, C) 
…
Response(A, B) ?
Response(A, C) 
…
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Support Conf. I.F.
• Support:
fraction of cases fulfilling the constraint
• Confidence:
support scaled by fraction of traces in
which the activation occurs
• Interest factor:
confidence scaled by fraction of traces in
which the target occurs

29. Mining declarative processes
RespondedExistence(A, B) 
RespondedExistence(A, C) 
…
Response(A, B) 
Response(A, C) 
…
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Support Conf. I.F.
• Support:
fraction of cases fulfilling the constraint
• Confidence:
support scaled by fraction of traces in
which the activation occurs
• Interest factor:
confidence scaled by fraction of traces in
which the target occurs

30. Constraints mining
An example
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A A B C A B C A C B C D
A C C A B B C B C A C B B D
C C C C C A A B C A A B A A B
A B B B D
C B A B D
A B B D
A A A C A C B D
A B C D
C A B A A C C B B D
B C C D
C A A C C C A A B C B C C B D

31. The role of Support
(event-based)
A A B C A B C A C B C D
A C C A B B C B C A C B B D
C C C C C A A B C A A B A A B
A B B B D
C B A B D
A B B D
A A A C A C B D
A B C D
C A B A A C C B B D
B C C D
C A A C C C A A B C B C C B D
 Support for
 Response(A, B)
 1.0
 Precedence(A, B)
 0.926 (25/27)
 Pruning on the basis of a
support threshold
 E.g., 0.95
 A threshold equal to 1.0
for a constraint means
“always valid in the log”
Activation
Target
Target
Activation
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http://github.com/cdc08x/minerful
Prom Nightly Builds > “Declare MINERful” plug-in

32. The role of Support
(trace-based)
A A B C A B C A C B C D
A C C A B B C B C A C B B D
C C C C C A A B C A A B A A B
A B B B D
C B A B D
A B B D
A A A C A C B D
A B C D
C A B A A C C B B D
B C C D
C A A C C C A A B C B C C B D
 Support for
 Response(A, B)
 1.0
 Precedence(A, B)
 0.818 (9/11)
 Pruning on the basis of a
support threshold
 E.g., 0.95
 A threshold equal to 1.0
for a constraint means
“always valid in the log”
SEITE 32 http://sourceforge.net/projects/prom/files/ProM/6.3

33. Declarative processes
Challenges

34. SEITE 34

35. Objective
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36. Make mined models
intelligible
 Prune irrelevant/redundant information
 Detect inconsistencies (yes, it happens)
 Picture what the model tells
 Increase the expressiveness of discovered
models
 Specify templates
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37. Make mined models
intelligible
 Prune irrelevant/redundant information
 Detect inconsistencies (yes, it happens)
 Picture what the model tells
 Increase the expressiveness of discovered
models
 Specify templates
SEITE 37
© Granger, NYC, source: https://www.granger.com/

38. Redundancies in discovered
constraints
 E.g.,
 A trace like
A B A B C A B C C
satisfies (w.r.t. A and B):
 RespondedExistence(A, B), RespondedExistence(B, A),
Response(A, B), AlternateResponse(A, B), ChainResponse(A, B),
Precedence(A, B), AlternatePrecedence(A, B), ChainPrecedence(A, B),
CoExistence(A, B), CoExistence(B, A),
Succession(A, B), AlternateSuccession(A, B), ChainSuccession(A, B)
 Among them, the mining algorithm should return only
the most restricting constraint – i.e., ChainSuccession(A, B)
 In order to face this issue, returned constraints
are pruned on the basis of the subsumption
hierarchy of constraints
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39. Pruning redundant constraints
1.0
1.0
1.0
1.01.0
1.0
1.0
1.0
1.0
1.0
1.0
✖
✖
✖
✖
✖
✖✖
✖✖
✖
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40. Pruning redundant constraints
0.8
0.8
0.9
0.90.9
0.9
0.7
0.7
0.9
0.9
0.9
✖ ✖
?
?
✖
✖
✖
?
?
S
u
p
p
o
r
t
✖
✖
✖
✖
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41. Mining declarative processes
RespondedExistence(A, B) 
RespondedExistence(A, C) ?
…
Response(A, B) 
Response(A, C) 
…
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Support Conf. I.F.

42. Mining declarative processes
RespondedExistence(A, B) 
RespondedExistence(A, C) ?
…
Response(A, B) 
Response(A, C) 
…
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Support Conf. I.F.

43. Mining declarative processes
RespondedExistence(A, B) 
RespondedExistence(A, C) 
…
Response(A, B) 
Response(A, C) 
…
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Support Conf. I.F.
http://github.com/cdc08x/minerful
Prom Nightly Builds > “Declare MINERful” plug-in
http://sourceforge.net/projects/prom/files/ProM/6.3

44. Make mined models
intelligible
 Prune irrelevant/redundant information
 Detect inconsistencies (yes, it happens)
 Picture what the model tells
 Increase the expressiveness of discovered
models
 Specify templates
SEITE 44

45. Make mined models
intelligible
 Prune irrelevant/redundant information
 Detect inconsistencies (yes, it happens)
 Picture what the model tells
 Increase the expressiveness of discovered
models
 Specify templates
Interlude
DECLARE & Regular Expressions
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46. Mining declarative processes
RespondedExistence(A, B)
RespondedExistence(A, C)
and
Response(A, B)
Response(A, C)
and
…
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47. Semantics of Declare:
Regular Expressions
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48. From constraints-based model
to FSA
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[^a]*((a.*b.*)|(b.*a.*))*[^a]* [^a]*(a.*c)*[^a]*

Regular
Expression
Deterministic
Finite
State
Automaton
RespondedExistence(A, B)
RespondedExistence(A, C)
and
Response(A, B)
Response(A, C)
and
…

49. Make mined models
intelligible
 Prune irrelevant/redundant information
 Detect inconsistencies (yes, it happens)
 Picture what the model tells
 Increase the expressiveness of discovered
models
Reprise
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50. While mining a real-life log…
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51. Time to challenge the X
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

52. The result
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53. The problem
 When support threshold is lower than 100%,
constraints can be valid through most of the log, though being in conflict
 Example: an event log consists of two traces:
1. <A, B, A, B, A, B, C>
2. <A, B, A, B, A, C>
 Support threshold: 0.7
• a is always the first
 Init(A)
• c is always the last
 End(C)
• In 6 cases over 8 (75%), a and c do not directly follow
each other
 NotChainSuccession(A, C)
• In 5 cases over 7 (71.143%), b and c do not directly follow
each other
 NotChainSuccession(B, C)
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54. The problem
 When support threshold is lower than 100%,
constraints can be valid through most of the log, though being in conflict
 Example: an event log consists of two traces:
1. <A, B, A, B, A, B, C>
2. <A, B, A, B, A, C>
 Support threshold: 0.7
• a is always the first
 Init(A)
• c is always the last
 End(C)
• In 6 cases over 8 (75%), a and c do not directly follow
each other
 NotChainSuccession(A, C)
• In 5 cases over 7 (71.143%), a and b do not directly follow
each other
 NotChainSuccession(B, C)
 Question: what can be done right before C?
 inconsistency!
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55. The algorithm to detect
inconsistencies
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
Init(a)
Participation(b)
AtMostOne(c)
End(c)
ChainPrecedence(a, b)
…
ChainSuccession(a, b)
Response(a, b)
ChainResponse(b, a)
NotChainSuccession(a, c)
ChainSuccession(b, a)
NotChainSuccession(b, c)
…
…
…
1

56. The algorithm to detect
inconsistencies

Init(a)
Participation(b)
AtMostOne(c)
End(c)
ChainPrecedence(a, b)
…
ChainSuccession(a, b)
Response(a, b)
ChainResponse(b, a)
NotChainSuccession(a, c) 
ChainSuccession(b, a)
NotChainSuccession(b, c)
…
…
…
1
2
SEITE 56 http://github.com/cdc08x/minerful

57. SEITE 57

58. Make mined models
intelligible
 Prune irrelevant/redundant information
 Detect inconsistencies (yes, it happens)
 Picture what the model tells
 Increase the expressiveness of discovered
models
SEITE 58
© Granger, NYC, source: https://www.granger.com/

59. The application of the method
to minimise the model
 Rationale:
1. How to find redundancies among constraints?
 Use the automaton-model correspondence
 Same language recognised after the product?
 Main difference with the inconsistency-
checking algorithm
 Constraints having support 100% are checked for
redundancies
 More details in the paper
SEITE 59 http://github.com/cdc08x/minerful

60. The application of the method
to minimise the model
SEITE 60
BPIC 2012

61. Make mined models
intelligible
 Prune irrelevant/redundant information
 Detect inconsistencies (yes, it happens)
 Picture what the model tells
 Inspect the imperative counterpart
 Simulate runs
 Increase the expressiveness of discovered
models
 Specify templates
SEITE 61

62. From declarative to imperative
models
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Response(Queued, Accepted)
NotChainSuccession(Queued, Completed)
Response(Queued, Completed)
NotChainSuccession(Queued, Unmatched)
Response(Accepted, Completed)
End(Completed)
NotSuccession(Completed, Unmatched)
AtMostOne(Unmatched)
RespondedExistence(Unmatched, Accepted)
AlternateResponse(Unmatched, Completed)



…

63. Make mined models
intelligible
 Prune irrelevant/redundant information
 Detect inconsistencies (yes, it happens)
 Picture what the model tells
 Inspect the imperative counterpart
 Simulate runs
 Increase the expressiveness of discovered
models
 Specify templates
SEITE 63

64. Generation of logs
SEITE 64
http://github.com/cdc08x/minerful
http://github.com/processmining/synthetic-log-generator

65. Make mined models
intelligible
 Prune irrelevant/redundant information
 Detect inconsistencies (yes, it happens)
 Picture what the model tells
 Inspect the imperative counterpart
 Simulate runs
 Increase the expressiveness of discovered
models
 Specify templates
SEITE 65

66. Target-Branched Declare
Support for
 Response(A, {B, C})
 1.0
B A A B A B C D
A A D B B A A D C
B A D D A D D A B E
C A B C
D A D B D
A E A C
A A A D B
D A D D B D
A A A D C D
A D D D A C
A A B
E D E B C E E C B E
SEITE 66
Activation
Target
Branching factor = 2

67. Objective
SEITE 67
+ Expressive Power
+ Conciseness

68. Evaluation:
constraints pruning
SEITE 68
BPI Challenge 2012
(0) 6,654,480 (1) 10,676 (2) 1446 (3) 12

69. Evaluation:
performance
SEITE 69
BPI Challenge 2012
(KB) 00:07.274 (Const’s) 25:51.678 (Total) 26:11.380
http://github.com/cdc08x/minerful

70. Make mined models
intelligible
 Prune irrelevant/redundant information
 Detect inconsistencies (yes, it happens)
 Picture what the model tells
 Inspect the imperative counterpart
 Simulate runs
 Increase the expressiveness of discovered
models
 Specify templates
SEITE 70

71. 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 71
“Until”
“Eventually”“Always”
“Next”

72. Semantics of Declare:
LTL
SEITE 72

73. 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 73

74. Extendibility of DECLARE:
A clear business impact
SEITE 74
Source: http://businessvalueexchange.com/

75. 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 75

76. Semantics of Declare:
LTLf
SEITE 76

77. Semantics of Declare:
SCIFF
SEITE 77

78. Semantics of Declare:
R/I-nets
SEITE 78

79. Semantics of Declare:
FOL over finite traces
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80. Semantics of Declare:
Regular expressions
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81. More alternatives for DECLARE
spec.: A clear business impact
SEITE 81
Source: http://businessconsultantsnewyork.blogspot.co.at/

82. My long-term objective
 Establish an algebra of constraint templates,
built on a basis of behavioural relations that
are:
 orthogonal to one another (no entailments,
subsumptions…)
 covering multiple trace-based behavioural relations
definition languages by linear composition
 have clear semantics (last but for sure NOT least)
 ...
SEITE 82

83. Further reading
 Presented:
 About MINERful:
 Claudio Di Ciccio, Massimo Mecella: On the Discovery of Declarative Control Flows for Artful Processes. ACM Trans.
Management Inf. Syst. 5(4): 24:1-24:37 (2015)
 Simulation of DECLARE models:
 Claudio Di Ciccio, Mario Luca Bernardi, Marta Cimitile, Fabrizio Maria Maggi: Generating Event Logs Through the
Simulation of Declare Models. EOMAS@CAiSE 2015: 20-36
 Discovery of target-branched DECLARE models:
 Claudio Di Ciccio, Fabrizio Maria Maggi, Jan Mendling: Efficient discovery of Target-Branched Declare constraints. Inf.
Syst. 56: 258-283 (2016)
 Getting rid of redundancies and inconsistencies:
 Claudio Di Ciccio, Fabrizio Maria Maggi, Marco Montali, Jan Mendling: Ensuring Model Consistency in Declarative
Process Discovery. BPM 2015: 144-159
 From DECLARE to Petri nets:
 Johannes Prescher, Claudio Di Ciccio, Jan Mendling: From Declarative Processes to Imperative Models. SIMPDA 2014:
162-173
 More:
 First steps towards data awareness of discovered DECLARE models:
 Stefan Schönig, Claudio Di Ciccio, Fabrizio Maria Maggi, Jan Mendling: Discovery of Multi-perspective Declarative
Process Models. ICSOC 2016: 87-103
 Against the little learnibility of DECLARE:
 Michael Hanser, Claudio Di Ciccio, Jan Mendling: A New Notational Framework for Declarative Process Modeling.
Softwaretechnik-Trends 36(2) (2016)
 Guarantee of the significance of discovered DECLARE models:
 Fabrizio Maria Maggi, Marco Montali, Claudio Di Ciccio, Jan Mendling: Semantical Vacuity Detection in Declarative
Process Mining. BPM 2016: 158-175
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