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Improving Accuracy and
Explainability in Event-Case
Correlation via Rule Mining
Dina Bayomie, Kate Revoredo, Claudio Di Ciccio, Jan Mendling
PAGE 2
Event correlation problem
© van der Aalst W. (2016) Process Mining: The Missing
Link. In: Process Mining. Springer.
PAGE 3
Event Correlation Engine
PAGE 4
EC-SA-RM
PAGE 5
EC-SA-Data – First iteration
Rand 𝑒, 𝑃𝑒 = 1 −
1
|𝑃𝑒|
PAGE 6
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
PAGE 7
EL-RM
PAGE 8
EC-SA-Data: Following iterations
Data rules
PAGE 9
EC-SA-RM: Final iteration
PAGE 10
Evaluation process
 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
PAGE 12
Evaluation: Experiment 1
Bigram similarity
Case similarity
Event time deviation
Case cycle time deviation
PAGE 13
Evaluation: Experiment 2
Bigram similarity
Case similarity
Event time deviation
Case cycle time deviation
PAGE 14
Evaluation: Experiment 3
 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
PAGE 16
Department of Information Systems &
Operations Management
Welthandelsplatz 1, 1020 Vienna, Austria
Dina Bayomie
dbayomie@wu.ac.at
PAGE 17

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Improving Accuracy and Explainability in Event-Case Correlation via Rule Mining

  • 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.
  • 5. PAGE 5 EC-SA-Data – First iteration Rand 𝑒, 𝑃𝑒 = 1 − 1 |𝑃𝑒|
  • 6. PAGE 6 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
  • 8. PAGE 8 EC-SA-Data: Following iterations Data rules
  • 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. PAGE 12 Evaluation: Experiment 1 Bigram similarity Case similarity Event time deviation Case cycle time deviation
  • 13. PAGE 13 Evaluation: Experiment 2 Bigram similarity Case similarity Event time deviation Case cycle time deviation
  • 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

Hinweis der Redaktion

  1. Obtaining the event logs is not a trivial task and requires domain knowledge.