pm4py.algo.discovery.correlation_mining package

Submodules

pm4py.algo.discovery.correlation_mining.algorithm module

class pm4py.algo.discovery.correlation_mining.algorithm.Variants(value)[source]

Bases: enum.Enum

An enumeration.

CLASSIC = <module 'pm4py.algo.discovery.correlation_mining.variants.classic' from 'C:\\Users\\berti\\FRAUNHOFER\\pm4py-core\\pm4py\\algo\\discovery\\correlation_mining\\variants\\classic.py'>
CLASSIC_SPLIT = <module 'pm4py.algo.discovery.correlation_mining.variants.classic_split' from 'C:\\Users\\berti\\FRAUNHOFER\\pm4py-core\\pm4py\\algo\\discovery\\correlation_mining\\variants\\classic_split.py'>
TRACE_BASED = <module 'pm4py.algo.discovery.correlation_mining.variants.trace_based' from 'C:\\Users\\berti\\FRAUNHOFER\\pm4py-core\\pm4py\\algo\\discovery\\correlation_mining\\variants\\trace_based.py'>
pm4py.algo.discovery.correlation_mining.algorithm.apply(log, variant=<Variants.CLASSIC: <module 'pm4py.algo.discovery.correlation_mining.variants.classic' from 'C:\\Users\\berti\\FRAUNHOFER\\pm4py-core\\pm4py\\algo\\discovery\\correlation_mining\\variants\\classic.py'>>, parameters=None)[source]

Applies the Correlation Miner to the event stream (a log is converted to a stream)

The approach is described in: Pourmirza, Shaya, Remco Dijkman, and Paul Grefen. “Correlation miner: mining business process models and event correlations without case identifiers.” International Journal of Cooperative Information Systems 26.02 (2017): 1742002.

Parameters
  • log – Log object

  • variant – Variant of the algorithm to use

  • parameters – Parameters of the algorithm

Returns

  • dfg – Directly-follows graph

  • performance_dfg – Performance DFG (containing the estimated performance for the arcs)

pm4py.algo.discovery.correlation_mining.util module

pm4py.algo.discovery.correlation_mining.util.calculate_time_match_fifo(ai, aj, times0=None)[source]

Associate the times between two lists of timestamps using FIFO

Parameters
  • ai – First list of timestamps

  • aj – Second list of timestamps

  • times0 – Correspondence between execution times

Returns

Correspondence between execution times

Return type

times0

pm4py.algo.discovery.correlation_mining.util.calculate_time_match_rlifo(ai, aj, times1=None)[source]

Associate the times between two lists of timestamps using LIFO (start from end)

Parameters
  • ai – First list of timestamps

  • aj – Second list of timestamps

  • times0 – Correspondence between execution times

Returns

Correspondence between execution times

Return type

times0

pm4py.algo.discovery.correlation_mining.util.get_c_matrix(PS_matrix, duration_matrix, activities, activities_counter)[source]

Calculates the C-matrix out of the PS matrix and the duration matrix

Parameters
  • PS_matrix – PS matrix

  • duration_matrix – Duration matrix

  • activities – Ordered list of activities of the log

  • activities_counter – Counter of activities

Returns

C matrix

Return type

c_matrix

pm4py.algo.discovery.correlation_mining.util.greedy_match_return_avg_time(ai, aj)[source]

Matches two list of times with a greedy method and returns the average.

Parameters
  • ai – First list

  • aj – Second list

  • parameters – Parameters of the algorithm

Returns

Mean of times

Return type

times_mean

pm4py.algo.discovery.correlation_mining.util.match_return_avg_time(ai, aj, exact=False)[source]

Matches two list of times (exact or greedy) and returns the average.

Parameters
  • ai – First list

  • aj – Second list

Returns

Mean of times

Return type

times_mean

pm4py.algo.discovery.correlation_mining.util.resolve_LP(C_matrix, duration_matrix, activities, activities_counter)[source]

Formulates and solve the LP problem

Parameters
  • C_matrix – C_matrix

  • duration_matrix – Duration matrix

  • activities – Ordered list of activities of the log

  • activities_counter – Counter of activities

Returns

  • dfg – Directly-Follows Graph

  • performance_dfg – Performance DFG (containing the estimated performance for the arcs)

Module contents