Reverse Dependencies of pm4py

The following projects have a declared dependency on pm4py:

• aqudem — Activity and Sequence Detection Performance Measures: A package to evaluate activity detection results, including the sequence of events given multiple activity types.
• automatise — Automatise: A Multiple Aspect Trajectory Data Mining Tool Library
• automatize — Automatize: A Multiple Aspect Trajectory Data Mining Tool Library
• cdesf — Concept-drift on Event Stream Framework
• cpnpy — A Python-based library designed to simulate Colored Petri Nets (CPNs) with optional time semantics.
• declare4py — Python library to perform discovery, conformance checking and query checking of DECLARE constraints.
• dependency-miner-pm4py — It mines long-term dependencies between events and results into a Precise model
• derive-event-pm4py — It derives new events based on rules provided as inputs.
• distributed-discovery — A process mining library for distributed processes.
• dspygen — A Ruby on Rails style framework for the DSPy (Demonstrate, Search, Predict) project for Language Models like GPT, BERT, and LLama.
• dtween — Digital Twin of an Organization realized with Action-Oriented Process Mining
• exdpn — Tool to mine and evaluate explainable data Petri nets using different classification techniques.
• feeed — Feature Extraction from Event Data
• ficus-pm — The modern Process Mining toolkit
• Gedi — Generating Event Data with Intentional Features for Benchmarking Process Mining
• hlem-framework — A Process Mining Framework for High-Level Event Mining
• hmmconf — HMM-based conformance checker
• jxes — Python implementation of JXES
• langgraph_log_parser — Parser for logs from LangGraph
• logicsponge-processmining — A real-time data processing pipeline
• mdata — no summary
• movelets — Movelets for Multiple Aspect Trajectory Data Mining
• mpdfg — Package for multi perspective DFG visualization
• ocpa — Object-Centric Process Analysis (OCPA)
• optimos — Optimos is a resource allocation optimization engine for business processes with differentiated resources. Part of PIX toolset
• ordinor — Python toolkit for organizational model mining
• p-connector-dfg — Privacy-preserving Process Discovery Using Connector Method
• p-privacy-qt — Quantifying privacy in process mining
• p-tlkc-privacy — TLKC-privacy model for process mining
• p-tlkc-privacy-ext — TLKC-privacy model for process mining
• PBLES — Private Bi-LSTM Event Log Synthesizer (PBLES)
• PetriNet2Vec — Process Mining Embeddings: Learning Representations for Petri Nets
• pm-cedp-qdp — Quantifying Temporal Privacy Leakage in Continuous Event Data Publishing
• pm4py-model-repair — This Algorithm implements a version of a process model repair Algorithm inspired by the paper "Repairing process models to reflect reality" by Dirk Fahland and Wil van der Aalst. DOI: 10.1016/j.is.2013.12.007. This implementation was created by Durborough on GitHub. I only added the setup.py file to make it installable via pip.
• pm4py-pn-unfoldings — Library for Petri net unfoldings based on pm4py
• pm4py-wrapper — pm4py wrapper to call the original package from CLI
• pm4pybenchmark — Process Mining for Python - Benchmark
• pm4pybpmn — Process Mining for Python - BPMN support
• pm4pycvxopt — Process Mining for Python - CVXOpt Support
• pm4pydistr — Support for distributed logs and computations in PM4Py
• pm4pygpu — PM4Py GPU
• pm4pymdl — Process Mining for Python - Multi-Dimensional Event Logs
• pm4pyserialization — Process Mining for Python (PM4Py) - Support for advanced serializations
• pmabstraction — Log Simplification in Process mining
• pmentropy — Compute in python entropy for process mining describe in Back, C.O., Debois, S. & Slaats, T. Entropy as a Measure of Log Variability. J Data Semant 8, 129–156 (2019). https://doi.org/10.1007/s13740-019-00105-3 (https://rdcu.be/dJMwH)
• pp-cedp — Privacy-Preserving Continuous Event Data Publishing
• pp-iopm — An Abstraction-Based Approach for Privacy-Aware Inter-Organizational Process Mining
• pp-pripel — Privacy-preserving Event Log Publishing with contextual Information
• pp-role-mining — Privacy Aware Role Mining in Process mining
• ppdp-anonops — This project implemets basic anonymization operations for event data which are used by process mining techniques.
• processmining — Python processmining Package
• procon — Conformance Checking on BPMN models
• prolothar-common — algorithms for process mining and data mining on event sequences
• proved — PROVED (PRocess mining OVer uncErtain Data) is a collection of tools to perform process mining on uncertain event data.
• pybeamline — Python version of Beamline (based on ReactiveX)
• qprsim — no summary
• rain-dm — Rain library.
• sapextractor — SAP Extractor
• sax4bpm — Open source Python library for deriving explanations about business processes based on process,causal and XAI perspectives
• special4pm — A Python package for the species-based analysis and computation of event logs
• unfolding-vis — Library for generating thorough web-visualization of Petri nets' unfoldings
• verona — The predictive process monitoring library for Python