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

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

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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.

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.

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

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

Notes de l'éditeur

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

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