pm4py.algo.discovery.temporal_profile.variants.log module#

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class pm4py.algo.discovery.temporal_profile.variants.log.Parameters(*values)[source]#

Bases: Enum

ACTIVITY_KEY = 'pm4py:param:activity_key'#
START_TIMESTAMP_KEY = 'pm4py:param:start_timestamp_key'#
TIMESTAMP_KEY = 'pm4py:param:timestamp_key'#
BUSINESS_HOURS = 'business_hours'#
BUSINESS_HOUR_SLOTS = 'business_hour_slots'#
WORKCALENDAR = 'workcalendar'#
pm4py.algo.discovery.temporal_profile.variants.log.apply(log: EventLog, parameters: Dict[Any, Any] | None = None) → Dict[Tuple[str, str], Tuple[float, float]][source]#

Gets the temporal profile from the log.

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).

Parameters:

  • log – Event log

  • parameters – Parameters, 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.BUSINESS_HOURS => calculates the difference of time based on the business hours not the total time. Default: False

    • Parameters.BUSINESS_HOURS_SLOTS => work schedule of the company. The following list defines that business hours are Mondays 07:00 - 17:00 and Tuesdays 07:00 - 12:00 and 13:00 - 17:00:

      [
    (7 * 60 * 60, 17 * 60 * 60),
    ((24 + 7) * 60 * 60, (24 + 12) * 60 * 60),
    ((24 + 13) * 60 * 60, (24 + 17) * 60 * 60),
]
Returns:

Temporal profile of the log

Return type:

temporal_profile