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

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, provided as a list of tuples where each tuple represents one time slot of business hours. One slot i.e. one tuple consists of one start and one end time given in seconds since week start, e.g. [

    (7 * 60 * 60, 17 * 60 * 60), ((24 + 7) * 60 * 60, (24 + 12) * 60 * 60), ((24 + 13) * 60 * 60, (24 + 17) * 60 * 60),

    ] meaning that business hours are Mondays 07:00 - 17:00 and Tuesdays 07:00 - 12:00 and 13:00 - 17:00

Returns:

Temporal profile of the log

Return type:

temporal_profile