1.
Improving Accuracy and
Explainability in Event-Case
Correlation via Rule Mining
Dina Bayomie, Kate Revoredo, Claudio Di Ciccio, Jan Mendling

2.
PAGE 2
Event correlation problem
© van der Aalst W. (2016) Process Mining: The Missing
Link. In: Process Mining. Springer.

3.
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Event Correlation Engine

4.
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EC-SA-RM

5.
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EC-SA-Data – First iteration
Rand 𝑒, 𝑃𝑒 = 1 −
1
|𝑃𝑒|

6.
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EL-RM
Filter the cases that contain at least
two events with low randomization
factor (Rand) and selects only
these events for the further
analysis

7.
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EL-RM

8.
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EC-SA-Data: Following iterations
Data rules

9.
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EC-SA-RM: Final iteration

10.
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Evaluation process

11.
 Bigram similarity
L2L2gram(𝐿, 𝐿′
)
=
1
|𝐿|
𝜎𝜖𝐿 𝑖=1
𝜎 −1
𝑜𝑐𝑐𝑢𝑟𝑠2 𝜎 𝑖 , 𝜎 𝑖 + 1 , 𝐿′
 Case similarity
L2Lcase 𝐿, 𝐿′
=
|𝐿 ∩ 𝐿′
|
|𝐿|
 Event time deviation
SMAPEET 𝐿, 𝐿′
=
𝑒∈𝐸
|ET 𝜎, 𝑒 − ET 𝜎′
, 𝑒 |
ET 𝜎, 𝑒 + |ET 𝜎′, 𝑒 |
𝐸 − |𝐿|
 Case cycle time deviation
SMAPECT 𝐿, 𝐿′
=
1
|𝐿|
𝜎∈𝐿
𝜎′∈𝐿′
𝜎 1 =𝜎′ 1
|CT 𝜎 − CT 𝜎′
|
CT 𝜎 + |CT 𝜎′ |
PAGE 11
Evaluation Measures

12.
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Evaluation: Experiment 1
Bigram similarity
Case similarity
Event time deviation
Case cycle time deviation

13.
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Evaluation: Experiment 2
Bigram similarity
Case similarity
Event time deviation
Case cycle time deviation

14.
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Evaluation: Experiment 3

15.
 A key quality that EC-SA-RM enjoys is thus its flexibility concerning the prior
knowledge of the analyst, on which other techniques heavily rely instead.
 Also, EC-SA-RM returns the data rules which can be used as a means to illustrate
the rationale behind the assignment of cases to events, thereby equipping our
technique with an additional explainability lens.
 Future work
 Measure the impact of the rules over iterations, to provide more accurate explanation
for the process analysts about the correlation decisions.
 Investigate methods to learn other types of rules
PAGE 15
Conclusion

16.
PAGE 16
Department of Information Systems &
Operations Management
Welthandelsplatz 1, 1020 Vienna, Austria
Dina Bayomie
dbayomie@wu.ac.at

17.
PAGE 17