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Process Mining for Javascript

pm4js-core

Process Mining for Javascript - Core Package

PM4Js is a library for process mining in Javascript. The execution is supported in modern browsers (Chrome, ...) and Node.JS

Install

Browser

Include the script dist/pm4js_latest.js in your web page to use the functionalities of PM4Js.

Node (package)

Install pm4js by doing npm install -g pm4js. Then, PM4Js can be used in Node by doing: require('pm4js').

Node (local installation)

Clone the Git project, and install the required packages by doing npm install. Then, PM4Js can be used in Node by doing: require('./init.js').

Documentation

Extensive documentation is available here. Some examples are included in the examples/html folder (HTML pages) and examples/node folder (Node.JS examples).

Features

The library currently offers the following set of features:

  • Objects
    • Working with Event Logs
      • Importing XES logs
      • Importing CSV logs
      • Exporting XES logs
      • Exporting CSV logs
      • Conversion to Event Stream
    • Petri Nets
      • Data Structure
      • Creation of a Petri net
      • Execution Semantics
      • Importing Exporting
      • Visualization (vanilla Graphviz)
    • Process Trees
      • Data Structure
      • Importing
      • Exporting
      • Visualization (vanilla Graphviz)
      • Conversion to an accepting Petri net
    • Directly-Follows Graphs
      • Frequency DFG
      • Performance DFG
      • Importing a Frequency DFG
      • Exporting a Frequency DFG
      • DFG capacity maximization
    • BPMN
      • BPMN objects
      • Importing BPMN
      • Exporting BPMN
      • Converting a BPMN to an accepting Petri net
      • Converting an accepting Petri net to BPMN
    • OCEL
      • Supported Formats
      • Importing OCEL
      • Exporting OCEL
      • Flattening OCEL
      • Statistics on OCEL
    • Business Hours configuration
  • Algorithms
    • Process Discovery
      • Inductive Miner
      • Inductive Miner Directly Follows
      • Log Skeleton
      • Directly Follows Graphs
      • Temporal Profile Discovery
    • Conformance Checking
      • Token-Based Replay
      • Alignments on Petri nets
      • Alignments on Directly Follows Graphs
      • Conformance Checking using the Log Skeleton
      • Temporal Profile Conformance Checking
    • Evaluation
      • Replay Fitness of Petri nets
      • ETConformance precision of Petri nets
      • Generalization of Petri nets
      • Simplicity of Petri nets
    • Filtering
      • Filtering Event Logs
      • Sliding Directly Follows Graphs
    • Simulation
      • Playout of a DFG
    • Feature Extraction
      • Feature Extraction on Event Logs
      • Object Based Feature Extraction on Object Centric Event Logs
      • Event Based Feature Extraction on Object Centric Event Logs
    • Interval Analysis
    • Network Analysis
      • Link Analysis on Event Logs
      • Network Analysis Algorithm
  • Statistics
    • Log
      • General Statistics
  • Support for Celonis
    • Celonis Connector
    • Traditional Celonis Wrapper
    • Object Centric Celonis Wrapper

Support

Support is provided by mail at the following mail address: Alessandro Berti.