Abducing Compliance of Incomplete Event Logs
This addresses the challenge of handling incomplete data in process mining, which is a known bottleneck in the field, making it an incremental advancement.
The paper tackles the problem of evaluating process compliance with incomplete event logs by introducing an abductive reasoning framework and refining compliance notions into strong and conditional compliance, demonstrating feasibility through experimental performance evaluation.
The capability to store data about business processes execution in so-called Event Logs has brought to the diffusion of tools for the analysis of process executions and for the assessment of the goodness of a process model. Nonetheless, these tools are often very rigid in dealing with with Event Logs that include incomplete information about the process execution. Thus, while the ability of handling incomplete event data is one of the challenges mentioned in the process mining manifesto, the evaluation of compliance of an execution trace still requires an end-to-end complete trace to be performed. This paper exploits the power of abduction to provide a flexible, yet computationally effective, framework to deal with different forms of incompleteness in an Event Log. Moreover it proposes a refinement of the classical notion of compliance into strong and conditional compliance to take into account incomplete logs. Finally, performances evaluation in an experimental setting shows the feasibility of the presented approach.