pm4py.algo.discovery.dfg.variants.performance module

class pm4py.algo.discovery.dfg.variants.performance.Parameters(*values)

Bases: Enum

ACTIVITY_KEY = 'pm4py:param:activity_key'

START_TIMESTAMP_KEY = 'pm4py:param:start_timestamp_key'

TIMESTAMP_KEY = 'pm4py:param:timestamp_key'

AGGREGATION_MEASURE = 'aggregationMeasure'

BUSINESS_HOURS = 'business_hours'

BUSINESS_HOUR_SLOTS = 'business_hour_slots'

WORKCALENDAR = 'workcalendar'

pm4py.algo.discovery.dfg.variants.performance.apply(log: EventLog | EventStream, parameters: Dict[str | Parameters, Any] | None = None) → Dict[Tuple[str, str], float]

pm4py.algo.discovery.dfg.variants.performance.performance(log: EventLog | EventStream, parameters: Dict[str | Parameters, Any] | None = None) → Dict[Tuple[str, str], float]

Measure performance between couples of attributes in the DFG graph

Parameters:

• log – Log

• parameters –

  Possible parameters passed to the algorithms:

  aggregationMeasure -> performance aggregation measure (min, max, mean, median) activity_key -> Attribute to use as activity timestamp_key -> 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:

DFG graph

Return type:

dfg