Abducing Compliance of Incomplete Event Logs

TL;DR

This paper introduces an abductive framework to assess process compliance in incomplete event logs, enhancing flexibility and effectiveness compared to traditional methods that require complete traces.

Contribution

It proposes a novel abductive approach for handling incomplete logs and refines compliance notions into strong and conditional compliance.

Findings

Framework effectively manages different types of incompleteness.

Experimental results demonstrate computational feasibility.

Refined compliance notions improve assessment accuracy.

Abstract

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.