Documentation

Features

Here we present the main documentation of the pm4py library. It supports importing/exporting functionality of several different artifacts, basic process discovery and conformance checking functionalities, as well as computing different quality dimensions of process models.
Please use the navigation menu on the left to browse into the specific category of interest.

Importing

  • Importing event logs from IEEE XES files
  • Importing event logs from comma-separated-values (csv) files
  • Import Petri nets with initial and final marking from PNML files

Exporting

  • Exporting event logs to IEEE XES files
  • Exporting event logs to csv files
  • Exporting Petri nets with initial and final marking to PNML files

Process Discovery

  • Discovery of Directly-Follows Graphs (including frequency and performance decoration)
  • Discovery of Petri nets with the Alpha Miner
  • Discovery of Petri nets with the Inductive Miner (DFG-Based)

Replay/Conformance Checking techniques

  • Token-based replay
  • Alignments-based replay

Process model quality evaluation

  • Replay fitness
  • Precision
  • Generalization
  • Simplicity

Filtering

  • Filtering trace logs:
    • Filtering by timeframe (containing, intersecting, keep events)
    • Filtering by case performance
    • Filtering by start activities
    • Filtering by end activities
    • Filtering by variants
    • Filtering by attributes
    • Filtering by paths
  • Filtering Pandas dataframes:
    • Filtering by timeframe (containing, intersecting, keep events)
    • Filtering by case performance
    • Filtering by start activities
    • Filtering by end activities
    • Filtering by variants
    • Filtering by attributes

Graphs:

  • Graphicating trace logs:
    • Case duration
    • Events over time
    • Numeric attribute
  • Graphicating Pandas dataframes:
    • Case duration
    • Events over time
    • Numeric attribute

Big CSV management (Pandas):

  • Loading of a sample
  • Filtering of the dataframe
  • Lazy conversion of the timestamp columns
  • Obtaining the Directly-Follows Graph from the Pandas dataframe
  • Filtering the DFG given a noise threshold
  • Retrieving aggregated statistics without replay

Log management

  • Sampling
  • Sorting
  • Classifiers

Social Network Analysis

  • Handover of Work metric (NetworkX and Pyvis visualization)
  • Similar Activities metric (NetworkX and Pyvis visualization)

Petri net analysis

  • Soudness analysis
  • Discovery of cycles
  • Discovery of strongly-connected components

Stochastic Petri nets:

  • Building Stochastic Map for Petri net transitions
  • Tangible Reachability Graph from Stochastic Petri nets
  • Continuous-Time Markov Chains:
    • Transient Analysis
    • Steady-State Analysis
  • Linear Programming Performance Bounds

BETA FEATURES:

BPMN 2.0 Management:

  • Importing/Exporting BPMN 2.0 diagrams (without layouting)
  • Representation of BPMN diagrams with curved arcs
  • Conversion of a Petri net into a BPMN model
  • Transforming a decorated Petri net into a decorated BPMN model
  • Conversion of a BPMN model to a Petri net:
    • Decoration purposes