Abducing Compliance of Incomplete Event Logs
  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.
