Other Analyses

This section collects additional analysis techniques available in PM4Py that complement the core process analysis modules.

Concept Drift

Detects sudden changes in process behavior over time by splitting the log into sub-logs, extracting global control-flow features, and applying permutation tests over sliding windows (based on Bose et al., CAiSE 2011).

Returns: a list of cumulative sub-logs up to each detected change point (plus final segment), the corresponding change timestamps (based on case start times), and p-values indicating significance.

Key parameters (in pm4py.algo.concept_drift.algorithm.Parameters):

  • SUB_LOG_SIZE: traces per sub-log (default: 50)
  • WINDOW_SIZE: sub-logs per window for comparison (default: 8)
  • NUM_PERMUTATIONS: permutations for the statistical test (default: 100)
  • THRESH_P_VALUE: p-value threshold for drift (default: 0.5)
  • MAX_NO_CHANGE_POINTS: maximum number of change points (default: 5)
  • ACTIVITY_KEY, TIMESTAMP_KEY, CASE_ID_KEY: attribute keys
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