Reconstructing invisible deviating events: A conformance checking approach for recurring events.

Area of Science:

• Process Mining
• Business Process Management
• Data Science

Background:

• Conformance checking verifies if processes align with their intended models.
• Current methods struggle with recurring activities and complex temporal rules, hindering accurate compliance assessment.
• Recurring activities and temporal complexities present significant challenges in process conformance checking.

Purpose of the Study:

• To present a novel approach for conformance checking that addresses the limitations of existing methods when dealing with recurring activities.
• To enable accurate conformance checking even in the presence of complex temporal rules and recurring process steps.
• To improve the robustness and applicability of conformance checking techniques.

Main Methods:

• Reconstructing missing events in event logs using defined rules.
• Incorporating specified temporal event characteristics into the reconstruction process.
• Utilizing native conformance checking algorithms after event log enhancement.

Main Results:

• The proposed approach effectively handles recurring activities in conformance checking.
• Reconstruction of missing events using temporal characteristics enables the use of standard conformance checking algorithms.
• Successful application and evaluation in a case study involving melanoma surveillance event logs.

Conclusions:

• The novel approach overcomes limitations in conformance checking related to recurring activities and temporal complexity.
• Reconstructing event logs with temporal information enhances the accuracy of process compliance verification.
• This method offers a practical solution for organizations seeking to improve process governance and compliance.