pm4py.algo.conformance.temporal_profile package


pm4py.algo.conformance.temporal_profile.algorithm module

pm4py.algo.conformance.temporal_profile.algorithm.apply(elog: Union[pm4py.objects.log.obj.EventLog, pandas.core.frame.DataFrame], temporal_profile: Dict[Tuple[str, str], Tuple[float, float]], parameters: Optional[Dict[Any, Any]] = None) → List[List[Tuple[float, float, float, float]]][source]

Checks the conformance of the log using the provided temporal profile.

Implements the approach described in: Stertz, Florian, Jürgen Mangler, and Stefanie Rinderle-Ma. “Temporal Conformance Checking at Runtime based on Time-infused Process Models.” arXiv preprint arXiv:2008.07262 (2020).

  • elog – Event log

  • temporal_profile – Temporal profile

  • parameters

    Parameters of the algorithm, including:
    • Parameters.ACTIVITY_KEY => the attribute to use as activity

    • Parameters.START_TIMESTAMP_KEY => the attribute to use as start timestamp

    • Parameters.TIMESTAMP_KEY => the attribute to use as timestamp

    • Parameters.ZETA => multiplier for the standard deviation


A list containing, for each trace, all the deviations. Each deviation is a tuple with four elements: - 1) The source activity of the recorded deviation - 2) The target activity of the recorded deviation - 3) The time passed between the occurrence of the source activity and the target activity - 4) The value of (time passed - mean)/std for this occurrence (zeta).

Return type


Module contents