The document describes a method for calculating the probability of an Reactive Constraint (RCon) specification being satisfied, violated, or unaffected given a trace or log of event data. An RCon consists of an activation formula (φα) and target formula (φτ). The probability of a single RCon is calculated by determining the maximum likelihood estimate of the target formula occurring given the activation occurred, based on event labellings in the traces. The probability of an RCon specification (a set of RCon rules) is also defined based on the probabilities of individual RCon rules. This allows interestingness measures to be applied to entire specifications rather than just individual rules.

1. 𝐿 = {⟨a, b, c, d, b, c, e, c, b⟩17,
⟨b, d, a, b, b, d, e, d, c⟩6,
⟨c, d, a, b, c, e, b, c, b, c⟩5
,
⟨b, c, a, c, e, a⟩12,
⟨b, b, b⟩5}
Log
R1: if ‘c’ occurs, then ‘a’ must have previously occurred
R2: if ‘d’ occurs, then ‘e’ must eventually occur
Rules
Confidence of R1: 82% Confidence of R2: 93%
Measures
If you consider a model consisting of R1 and R2 together, what is its confidence?
Question
79 % 82 % 87.5 % 100 %

2. Measurement of Rule-based
LTL𝑓 Declarative Process Specifications
4th Int. Conference on Process Mining, ICPM 2022, Bolzano (Italy)
Alessio Cecconi Claudio Di Ciccio Arik Senderovich
alessio.cecconi@wu.ac.at claudio.diciccio@uniroma1.it sariks@yorku.ca

3. “If d, then e will follow”
“If c, then previously a”
“If d, then 𝐅(e)”
“If c, then 𝐎(a)”
Rule: Reactive Constraint (RCon) 𝜓
3
[1] De Giacomo and Vardi, “Linear temporal logic and linear dynamic logic on finite traces,” in IJCAI, 2013
[2] Pesic, Bosnacki, van der Aalst, “Enacting declarative languages using LTL: avoiding errors and improving performance,” in SPIN, 2010
[3] C., DC., De Giacomo, and Mendling, “Interestingness of traces in declarative process mining: The Janus LTLp f approach,” in BPM, 2018
“If a viral infection is detected, then an intravenous antiviral administration will follow”
“If antibiotics are administered, then an antibiogram must have been previously registered”
LTL𝑓 operator (finally) [1]
LTL𝑓 operator (once)
d → 𝐅(e)
c → 𝐎(a)
Activation
Activation
Target
Target
𝜑𝛼1
𝜑𝛼2
𝜑𝜏1
𝜑𝜏2
LTL𝑓
fomulae
LTL𝑓
fomulae
Any
DECLARE
rule [2]
can be
encoded
as an
RCon [3]

4. Rule: Reactive Constraint (RCon) 𝜓
4
Cecconi, Di Ciccio, Senderovich: Interestingness of Specifications
Rule semantics
(Interestingly) satisfied
Violated
Unaffected
𝜑𝜏
𝜑𝛼 𝜓



 
Trace 𝑡𝟐 ⟨ b, d, a, b, b, d, e, d, c ⟩
d → 𝐅(e) ● ● ● ● ● ● ● ● ●
d → 𝐅(e)
c → 𝐎(a)
Activation
Activation
Target
Target
𝜑𝛼1
𝜑𝛼2
𝜑𝜏1
𝜑𝜏2
LTL𝑓
fomulae
LTL𝑓
fomulae

5. Rule: Reactive Constraint (RCon) 𝜓
5
Cecconi, Di Ciccio, Senderovich: Interestingness of Specifications
Rule semantics
(Interestingly) satisfied
Violated
Unaffected
𝜑𝜏
𝜑𝛼 𝜓



 
Trace 𝑡𝟐 ⟨ b, d, a, b, b, d, e, d, c ⟩
d → 𝐅(e) ● ● ● ● ● ● ● ● ●
d → 𝐅(e)
c → 𝐎(a)
Activation
Activation
Target
Target
𝜑𝛼1
𝜑𝛼2
𝜑𝜏1
𝜑𝜏2
LTL𝑓
fomulae
LTL𝑓
fomulae

6. RCon Specification 𝑆 ≜ {𝜓1, 𝜓2, … 𝜓𝑛}
• Semantics: a specification is
• Satisfied iff an RCon is satisfied, but no violations occur
• Violated iff an RCon is violated
• Unaffected otherwise
• A specification is like a single RCon 𝑆 = 𝑆𝛼 → 𝑆τ
𝑆𝛼 =
𝑗=1
𝑆
𝜑𝛼𝑗 ; 𝑆τ =
𝑗=1
𝑆
¬(𝜑𝛼𝑗⋀¬𝜑𝜏𝑗
)
6
Cecconi, Di Ciccio, Senderovich: Interestingness of Specifications
d → 𝐅(e)
c → 𝐎(a)
Activation
Activation
Target
Target
𝜑𝛼1
𝜑𝛼2
𝜑𝜏1
𝜑𝜏2
LTL𝑓
fomulae
LTL𝑓
fomulae

7. d → 𝐅(e)
c → 𝐎(a)
Activation
Activation
Target
Target
𝜑𝛼2
𝜑𝛼1
𝜑𝜏1
𝜑𝜏2
LTL𝑓
fomulae
LTL𝑓
fomulae
RCon Specification 𝑆 ≜ {𝜓1, 𝜓2, … 𝜓𝑛}
• Semantics: a specification is
• Satisfied iff an RCon is satisfied, but no violations occur
• Violated iff an RCon is violated
• Unaffected otherwise
• A specification is like a single RCon 𝑆 = 𝑆𝛼 → 𝑆τ
𝑆𝛼 =
𝑗=1
𝑆
𝜑𝛼𝑗 ; 𝑆τ =
𝑗=1
𝑆
¬(𝜑𝛼𝑗⋀¬𝜑𝜏𝑗
)
7
Cecconi, Di Ciccio, Senderovich: Interestingness of Specifications
d (¬ d ∧ ¬𝐅(e))
∨ c ∧ (¬ c ∧ ¬𝐎(a))
𝑆𝛼 𝑆τ
LTL𝑓
fomula
LTL𝑓
fomula

8. RCon specification measurement?
𝑡𝟏 𝑡𝟐 𝑡𝟑 𝑡𝟒 … 𝑡𝒏
𝜓1 ● ● ● ● … ●
𝜓2 ● ● ● ● … ●
𝜓3 ● ● ● ● … ●
𝑆 ● ● ● ● … ●
8
Cecconi, Di Ciccio, Senderovich: Interestingness of Specifications
𝒎(𝑺) ? ? ? ? … ?

9. Interestingess measures
• Based on probabilities [4]
– We needed to define the probability of single rules (done [5])
• We want to apply them also to entire specifications!
→ We need to define probabilities of specifications first (bear with us)
9
Cecconi, Di Ciccio, Senderovich: Interestingness of Specifications
[4] Geng and Hamilton, “Interestingness measures for data mining: A survey,” ACM Comput. Surv., 2006.
[5] C., De Giacomo, DC., Maggi, Mendling, “Measuring the interestingness of temporal logic behavioral specifications in process mining,” Inf. Syst., 2021

10. Probability: from LTL𝑓 rules to RCon specifications
𝜑
𝜓1 ≜ 𝜑𝛼1
→ 𝜑𝜏1
𝑆 ≜ {𝜓1, 𝜓2, … , 𝜓𝑠}
↦ 𝑃(𝜑) [5]
↦ 𝑃(𝜓) [5]
↦ 𝑃 𝑆 = ?
10
Cecconi, Di Ciccio, Senderovich: Interestingness of Specifications
In traces and logs!
[5] C., De Giacomo, DC., Maggi, Mendling, “Measuring the interestingness of temporal logic behavioral specifications in process mining,” Inf. Syst., 2021

11. Trace
Probability of an event 𝑒
in a trace 𝑡 of length 𝑛
to satisfy an LTL𝑓 formula 𝜑
• Bernoulli MLE [6]
𝑃 𝜑 𝑡 =
𝑖=1
𝑛
𝛺 𝑒𝑖, 𝜑
𝑛
,
𝛺 𝑒𝑖, 𝜑 ∈ 0,1
Log
Probability of a trace 𝑡
in a log 𝐿 of cardinality 𝑚
to satisfy an LTL𝑓 formula 𝜑
• MLE
𝑃(𝜑 𝐿 ) =
𝑖=1
𝑚
𝑃(𝑇 = 𝑡𝑖) 𝑃(𝜑 𝑡𝑖 )
Probability of an LTL𝑓 formula 𝜑
11
Cecconi, Di Ciccio, Senderovich: Interestingness of Specifications
Labelling
[6] Bickel and Doksum, Mathematical statistics: basic ideas and selected topics, volumes I-II package. CRC Press, 2015.

12. Probability of an LTL𝑓 formula 𝜑
Example: 𝜑 ≐ 𝐅(e)
Trace
𝑃 𝜑 𝑡2 =
7
9
= 0.78
12
Cecconi, Di Ciccio, Senderovich: Interestingness of Specifications
Trace 𝑡𝟐 ⟨ b, d, a, b, b, d, e, d, c ⟩
𝛺 𝑒, 𝜑 1 1 1 1 1 1 1 0 0
Trace Multiplicity 𝑃 𝜑 𝑡
𝑡1 17 0.78
𝑡2 6 0.78
𝑡3 5 0.60
𝑡4 12 0.83
𝑡5 5 0.00
Total 45
Labelling

13. Probability of an LTL𝑓 formula 𝜑
Example: 𝜑 ≐ 𝐅(e)
Trace
𝑃 𝜑 𝑡2 =
7
9
= 0.78
Log
𝑃(𝜑 𝐿 ) =
17
45
× 0.78 +
6
45
× 0.78 +
5
45
× 0.60 +
12
45
× 0.83 +
5
45
× 0.0
= 0.69
13
Cecconi, Di Ciccio, Senderovich: Interestingness of Specifications
Trace 𝑡𝟐 ⟨ b, d, a, b, b, d, e, d, c ⟩
𝛺 𝑒, 𝜑 1 1 1 1 1 1 1 0 0
Trace Multiplicity 𝑃 𝜑 𝑡
𝑡1 17 0.78
𝑡2 6 0.78
𝑡3 5 0.60
𝑡4 12 0.83
𝑡5 5 0.00
Total 45
Labelling

14. Probability of an RCon 𝝍 (activation 𝝋𝜶, target 𝝋𝝉)
Example: 𝜓 ≐ d → 𝐅(e)
14
Cecconi, Di Ciccio, Senderovich: Interestingness of Specifications
Trace Multiplicity 𝑃 𝝍 𝑡
𝑡1 17 1.00
𝑡2 6 0.67
𝑡3 5 1.00
𝑡4 12 NaN
𝑡5 5 NaN
Total 45
Trace 𝑡𝟐 ⟨ b, d, a, b, b, d, e, d, c ⟩
𝛺 𝑒, 𝜑𝛼 0 1 0 0 0 1 0 1 0
𝛺 𝑒, 𝜑𝜏 1 1 1 1 1 1 1 0 0

15. Trace 𝑡𝟐 ⟨ b, d, a, b, b, d, e, d, c ⟩
𝛺 𝑒, 𝜑𝛼 0 1 0 0 0 1 0 1 0
𝛺 𝑒, 𝜑𝜏 1 1 1 1 1 1 1 0 0
Probability of an RCon 𝝍 (activation 𝝋𝜶, target 𝝋𝝉)
Example: 𝜓 ≐ d → 𝐅(e)
Trace
𝑃 𝜓 𝑡 = 𝑃 𝜑𝜏 𝑡2 𝜑𝛼 𝑡2 =
2
2+1
= 0.67
15
Cecconi, Di Ciccio, Senderovich: Interestingness of Specifications
𝜑𝜏 ¬𝜑𝜏
𝜑𝛼 11 10
¬𝜑𝛼 01 00
Trace Multiplicity 𝑃 𝝍 𝑡
𝑡1 17 1.00
𝑡2 6 0.67
𝑡3 5 1.00
𝑡4 12 NaN
𝑡5 5 NaN
Total 45
Trace 𝑡𝟐 ⟨ b, d, a, b, b, d, e, d, c ⟩
𝛺 𝑒, 𝜑𝛼 0 1 0 0 0 1 0 1 0
𝛺 𝑒, 𝜑𝜏 1 1 1 1 1 1 1 0 0
𝜴𝑹 ⅇ, 𝝍 ⨯ 1 ⨯ ⨯ ⨯ 1 ⨯ 0 ⨯
Division by 0
New
labelling

16. Probability of an RCon 𝝍 (activation 𝝋𝜶, target 𝝋𝝉)
Example: 𝜓 ≐ d → 𝐅(e)
Trace
𝑃 𝜓 𝑡 = 𝑃 𝜑𝜏 𝑡2 𝜑𝛼 𝑡2 =
2
2+1
= 0.67
Log
𝑃(𝜓 𝐿 ) =
17
45
× 1.0 +
6
45
× 0. 67 +
5
45
× 1.0 = 0.58
16
Cecconi, Di Ciccio, Senderovich: Interestingness of Specifications
𝜑𝜏 ¬𝜑𝜏
𝜑𝛼 11 10
¬𝜑𝛼 01 00
Trace Multiplicity 𝑃 𝝍 𝑡
𝑡1 17 1.00
𝑡2 6 0.67
𝑡3 5 1.00
𝑡4 12 NaN
𝑡5 5 NaN
Total 45
Trace 𝑡𝟐 ⟨ b, d, a, b, b, d, e, d, c ⟩
𝛺 𝑒, 𝜑𝛼 0 1 0 0 0 1 0 1 0
𝛺 𝑒, 𝜑𝜏 1 1 1 1 1 1 1 0 0
𝜴𝑹 ⅇ, 𝝍 ⨯ 1 ⨯ ⨯ ⨯ 1 ⨯ 0 ⨯
New
labelling
Division by 0

17. Trace
Conditional probability of an event
in a trace t to satisfy the target
given the satisfaction of the
activator
• Bivariate Bernoulli MLE [1]
𝑃 𝜓 𝑡 = 𝑃(𝜑τ(𝑡)|𝜑𝛼(𝑡)) =
𝑝11
𝑝01 + 𝑝11
where
𝑝11 = 𝑃(𝜑τ(𝑡) ∩ 𝜑𝛼(𝑡)) =
𝑖=1
𝑛 𝛺(𝑒𝑖,𝜑𝛼)𝛺(𝑒𝑖,𝜑τ)
𝑛
Log
Probability of a trace t
in a log 𝐿
to satisfy an RCon 𝜓
• MLE
𝑃 𝜓 𝐿 =
𝑡∈𝐿
𝑃(𝑇 = 𝑡)𝑃(𝜓(𝑡))
Probability of an RCon 𝝍 (activation 𝝋𝜶, target 𝝋𝝉)
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Cecconi, Di Ciccio, Senderovich: Interestingness of Specifications
Labelling mechanism
𝛺𝑅 𝑒, 𝜓 =
0, if 𝛺 𝑒, 𝜑𝛼 = 1 and 𝛺 𝑒, 𝜑τ = 0
1, if 𝛺 𝑒, 𝜑𝛼 = 1 and 𝛺 𝑒, 𝜑τ = 1
⨯, otherwise
[1] Dai, Ding, Wahba, “Multivariate bernoulli distribution,” Bernoulli, 2013.

18. Probability of a specification 𝑺
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Cecconi, Di Ciccio, Senderovich: Interestingness of Specifications
Trace 𝑡𝟐 ⟨ b, d, a, b, b, d, e, d, c ⟩
𝛺 𝑒, 𝜑𝛼1
0 0 0 0 0 0 0 0 1
𝛺 𝑒, 𝜑𝜏2
0 0 1 1 1 1 1 1 1
𝜴 ⅇ, 𝑺𝜶 0 1 0 0 0 1 0 1 1
𝜴 ⅇ, 𝑺𝝉 0 1 1 1 1 1 1 0 1
𝛺 𝑒, 𝜑𝛼2
0 1 0 0 0 1 0 1 0
𝛺 𝑒, 𝜑𝜏2
1 1 1 1 1 1 1 0 0
𝜓1 ≐ c → 𝐎(a)
𝜓2 ≐ d → 𝐅(e)
𝑆 ≐ {𝜓1, 𝜓2}
MEMO 𝑆 ≜ 𝑆𝛼 → 𝑆τ where
𝑆𝛼 =
𝑗=1
𝑆
𝜑𝛼𝑗
; 𝑆τ =
𝑗=1
𝑆
¬(𝜑𝛼𝑗
⋀¬𝜑𝜏𝑗
)
Trace Multiplicity 𝑃 𝑆 𝒕
𝑡1 17 1.00
𝑡2 6 0.75
𝑡3 5 0.80
𝑡4 12 0.50
𝑡5 5 NaN
Total 45

19. Probability of a specification 𝑺
Trace: 𝑃 𝑆 𝑡 = 𝑃 𝑆τ 𝑆𝛼 =
3
3+1
= 0.75
Log: 𝑃(𝑆 𝐿 ) =
17
45
× 1.0 +
6
45
× 0.75 +
5
45
× 0.80 +
12
45
× 0.50 = 0.7
19
Cecconi, Di Ciccio, Senderovich: Interestingness of Specifications
Trace 𝑡𝟐 ⟨ b, d, a, b, b, d, e, d, c ⟩
𝛺 𝑒, 𝜑𝛼1
0 0 0 0 0 0 0 0 1
𝛺 𝑒, 𝜑𝜏2
0 0 1 1 1 1 1 1 1
𝜴 ⅇ, 𝑺𝜶 0 1 0 0 0 1 0 1 1
𝜴 ⅇ, 𝑺𝝉 0 1 1 1 1 1 1 0 1
𝛺 𝑒, 𝜑𝛼2
0 1 0 0 0 1 0 1 0
𝛺 𝑒, 𝜑𝜏2
1 1 1 1 1 1 1 0 0
𝜴 ⅇ, 𝑺𝜶 0 1 0 0 0 1 0 1 1
𝜴 ⅇ, 𝑺𝝉 0 1 1 1 1 1 1 0 1
𝜓1 ≐ c → 𝐎(a)
𝜓2 ≐ d → 𝐅(e)
𝑆 ≐ {𝜓1, 𝜓2}
Trace Multiplicity 𝑃 𝑆 𝒕
𝑡1 17 1.00
𝑡2 6 0.75
𝑡3 5 0.80
𝑡4 12 0.50
𝑡5 5 NaN
Total 45

20. Probability of a specification 𝑺
Trace: 𝑃 𝑆 𝑡 = 𝑃 𝑆τ 𝑆𝛼 =
3
3+1
= 0.75
Log: 𝑃(𝑆 𝐿 ) =
17
45
× 1.0 +
6
45
× 0.75 +
5
45
× 0.80 +
12
45
× 0.50 = 0.7
20
Cecconi, Di Ciccio, Senderovich: Interestingness of Specifications
Trace 𝑡𝟐 ⟨ b, d, a, b, b, d, e, d, c ⟩
𝛺 𝑒, 𝜑𝛼1
0 0 0 0 0 0 0 0 1
𝛺 𝑒, 𝜑𝜏2
0 0 1 1 1 1 1 1 1
𝜴 ⅇ, 𝑺𝜶 0 1 0 0 0 1 0 1 1
𝜴 ⅇ, 𝑺𝝉 0 1 1 1 1 1 1 0 1
𝛺 𝑒, 𝜑𝛼2
0 1 0 0 0 1 0 1 0
𝛺 𝑒, 𝜑𝜏2
1 1 1 1 1 1 1 0 0
𝜴 ⅇ, 𝑺𝜶 0 1 0 0 0 1 0 1 1
𝜴 ⅇ, 𝑺𝝉 0 1 1 1 1 1 1 0 1
𝜓1 ≐ c → 𝐎(a)
𝜓2 ≐ d → 𝐅(e)
𝑆 ≐ {𝜓1, 𝜓2}
Trace Multiplicity 𝑃 𝑆 𝒕
𝑡1 17 1.00
𝑡2 6 0.75
𝑡3 5 0.80
𝑡4 12 0.50
𝑡5 5 NaN
Total 45

21. Trace
Conditional probability of an event
in a trace t to satisfy the target
given the satisfaction of the
activator
• MLE
𝑃 𝑆 𝑡 = 𝑃(𝑆τ(𝑡)|𝑆𝛼(𝑡)) =
𝑝11
𝑝01 + 𝑝11
where
𝑝11 = 𝑃(𝑆τ(𝑡) ∩ 𝑆𝛼(𝑡)) =
𝑖=1
𝑛 𝛺(𝑒𝑖,𝑆𝛼)𝛺(𝑒𝑖,𝑆τ)
𝑛
Log
Probability of a trace t
in a log 𝐿
to satisfy a specification 𝑆
• MLE
𝑃 𝑆 𝐿 =
𝑡∈𝐿
𝑃(𝑇 = 𝑡)𝑃(𝑆(𝑡))
Probability of a specification 𝑺
21
Cecconi, Di Ciccio, Senderovich: Interestingness of Specifications
MEMO 𝑆 ≜ 𝑆𝛼 → 𝑆τ where
𝑆𝛼 =
𝑗=1
𝑆
𝜑𝛼𝑗
; 𝑆τ =
𝑗=1
𝑆
¬(𝜑𝛼𝑗
⋀¬𝜑𝜏𝑗
)

22. Probability: From LTL𝑓 rules to RCon specifications
𝜑
𝜓1 ≜ 𝜑𝛼1
→ 𝜑𝜏1
𝑆 ≜ {𝜓1, 𝜓2, … , 𝜓𝑠}
↦ 𝑃(𝜑)
↦ 𝑃(𝜓) = 𝑃 𝜑𝜏 𝜑𝛼
↦ 𝑃 𝑆 = 𝑃(𝑆𝜏|𝑆𝛼)
22
Cecconi, Di Ciccio, Senderovich: Interestingness of Specifications
Done!
For traces and logs!

23. Specification measurements
23
Cecconi, Di Ciccio, Senderovich: Interestingness of Specifications
Measure Formula for 𝐴 ⇒ 𝐵
Support 𝑃(𝐴𝐵)
Recall 𝑃(𝐴|𝐵)
Confidence 𝑃(𝐵|𝐴)
Specificity 𝑃(¬𝐵|¬𝐴)
Lift
𝑃(𝐴𝐵)
𝑃 𝐴 𝑃(𝐵)
…
…
Measure Formula for 𝑆 (trace) Formula for 𝑆 (log)
Support 𝑃(Sα ∩ Sτ, t) 𝑃(Sα ∩ Sτ, 𝐿)
Recall 𝑃(Sα|Sτ, t) 𝑃(Sα|Sτ, 𝐿)
Confidence 𝑃(Sτ|Sα, t) 𝑃(Sτ|Sα, 𝐿)
Specificity 𝑃(¬Sτ|¬Sα, t) 𝑃(¬Sτ|¬Sα, 𝐿)
Lift
𝑃(Sα ∩ Sτ, t)
𝑃 Sα, t 𝑃(Sτ, t)
𝑃(Sα ∩ Sτ, 𝐿)
𝑃 Sα, 𝐿 𝑃(Sτ, 𝐿)
… … …

24. Evaluation: Discovery setup vs discovered quality
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
1 0.95 0.9 0.85 0.8 0.75 0.7 0.65 0.6 0.55 0.5 0.45 0.4 0.35 0.3 0.25 0.2 0.15 0.1 0.05 0
Discovred
specification
confidence
Confidence threshold
24
Cecconi, Di Ciccio, Senderovich: Interestingness of Specifications

25. Evaluation: Discovery setup vs discovered quality
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
1 0.95 0.9 0.85 0.8 0.75 0.7 0.65 0.6 0.55 0.5 0.45 0.4 0.35 0.3 0.25 0.2 0.15 0.1 0.05 0
Discovred
specification
confidence
Confidence threshold
JANUS
MINERful
PERRACOTTA
25
Cecconi, Di Ciccio, Senderovich: Interestingness of Specifications

26. Recap and limitations
• Computation of quality measures
for declarative process
specifications
• Control-flow only
• Assumption of probabilistic
independence
Future work
• Multi-perspective approach
• Upgrade to Bayesian networks
• Logistic regression & statistical
analysis
• Applications of data featurization
– E.g., for trace clustering
Conclusions
26
Cecconi, Di Ciccio, Senderovich: Interestingness of Specifications

27. 𝐿 = {⟨a, b, c, d, b, c, e, c, b⟩17,
⟨b, d, a, b, b, d, e, d, c⟩6,
⟨c, d, a, b, c, e, b, c, b, c⟩5
,
⟨b, c, a, c, e, a⟩12,
⟨b, b, b⟩5}
Log
R1: if ‘c’ occurs, then ‘a’ must have previously occurred
R2: if ‘d’ occurs, then ‘e’ must eventually occur
Rules
Confidence of R1: 82% Confidence of R2: 93%
Measures
If you consider a model consisting of R1 and R2 together, what is its confidence?
Question
79 % 82 % 87.5 % 100 %

28. Measurement of Rule-based
LTL𝑓 Declarative Process Specifications
4th Int. Conference on Process Mining, ICPM 2022, Bolzano (Italy)
Alessio Cecconi Claudio Di Ciccio Arik Senderovich
alessio.cecconi@wu.ac.at claudio.diciccio@uniroma1.it sariks@yorku.ca