Slides of the paper presented at the 4th Int. Conference on Process Mining (ICPM 2022, Bolzano, Italy). Abstract: Process mining analyzes business processes’ behavior and performance using event logs. An essential requirement is that events are grouped in cases representing the execution of process instances. However, logs extracted from different systems or non-process-aware information systems do not map events with unique case identifiers (case IDs). In such settings, the event log needs to be pre-processed to group events into cases – an operation known as event correlation. Existing techniques for correlating events work with different assumptions: some assume the generating processes are acyclic, while others require extra domain knowledge, such as the relation between the events and event attributes or heuristic information about the activities’ execution time behavior. However, domain knowledge is not always available or easy to acquire, compromising the quality of the correlated event log. In this paper, we propose a new technique called EC-SA-RM, which correlates the events using a simulated annealing technique and iteratively learns the domain knowledge as a set of association rules. The technique requires a sequence of timestamped events (i.e., the log without case IDs) and a process model describing the underlying business process. A possible correlated log is generated at each iteration of the simulated annealing. Then, EC-SA-RM uses this correlated log to learn a set of association rules that represent the relationship between the events and the changing behavior over the events’ attributes in an understandable way. These rules enrich the input and improve the event correlation process for the next iteration. EC-SA-RM returns an event log in which events are grouped in cases and a set of association rules that explain the correlation over the events. We evaluate our approach using four real-life datasets. Paper: D. Bayomie, K. Revoredo, C. Di Ciccio and J. Mendling, "Improving Accuracy and Explainability in Event-Case Correlation via Rule Mining," 2022 4th International Conference on Process Mining (ICPM), Bolzano, Italy, 2022, pp. 24-31, doi: 10.1109/ICPM57379.2022.9980684.

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. PAGE 3
Event Correlation Engine

4. PAGE 4
EC-SA-RM

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

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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

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EL-RM

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

9. PAGE 9
EC-SA-RM: Final iteration

10. PAGE 10
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. 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

14. PAGE 14
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

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