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process-mining-knowledge-base

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list of process mining resources

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# Process mining

## Core resourses
* [Process Mining Manifesto](https://www.win.tue.nl/ieeetfpm/downloads/Process%20Mining%20Manifesto.pdf)
This manifesto is written by members and supporters of the IEEE Task Force on Process Mining. The goal of this task force is to promote the research, development, education, implementation, evolution, and understanding of process mining.
* [Process mining](http://www.processmining.org/) The bridge between process science and data science.
* [PM^2: a Process Mining Project Methodology](https://link.springer.com/content/pdf/10.1007/978-3-319-19069-3_19.pdf) A methodology to guide the execution of process mining projects.
* [IEEE CIS Task Force on Process Mining](https://www.tf-pm.org/) The IEEE has established a Task Force on Process Mining. The goal of Task Force is to promote the research, development, education and understanding of process mining.

## Data
* [Real life Event Logs](https://data.4tu.nl/search?search=%22real%20life%20event%20logs%22)
* [Synthetic Event Logs](https://data.4tu.nl/search?search=%22Synthetic+Event+Logs%22)
* [Event logs](https://data.4tu.nl/search?search=%22Event%20logs%22)
* [BPI Challenge 2020 Event Logs](https://data.4tu.nl/collections/6bcb71ec-6e2e-4837-a899-624367f1c36b)
* [IEEE Task Force on Process Mining Event Logs](https://www.tf-pm.org/resources/logs)
* [About event logs](https://www.processmining.org/event-data.html)
* [IEEE 1849-2016 XES Standard](http://www.xes-standard.org/)

## Applications
### Healthcare
* [Using Process Mining in Healthcare](https://link.springer.com/chapter/10.1007/978-3-031-08848-3_14)
### Insurance 
* [Understanding Process Behaviours in a Large Insurance Company in Australia: A Case Study](https://www.researchgate.net/publication/262248084_Understanding_Process_Behaviours_in_a_Large_Insurance_Company_in_Australia_A_Case_Study)

## Algorithms
### Process Discovery
* Alpha (Classis). [Workflow Mining:Discovering process models from event logs](https://www.researchgate.net/publication/3297301_Workflow_Mining_Discovering_Process_Models_from_Event_Logs).
* Alpha+. [Process Mining for Ubiquitous Mobile Systems: An Overview and a Concrete Algorithm](https://www.researchgate.net/publication/220921191_Process_Mining_for_Ubiquitous_Mobile_Systems_An_Overview_and_a_Concrete_Algorithm)
* Alpha++. [Mining Process Models with Non-Free-Choice Constructs](https://www.researchgate.net/publication/220451684_Mining_process_models_with_non-free-choice_Constructs)
* Alpha#. [Mining Process Models with Prime InvisibleTasks](https://www.researchgate.net/publication/222684024_Mining_Process_Models_with_Prime_Invisible_Tasks)
* HeuristicsMiner. [Process Mining with the Heuristics Miner Algorithm](https://www.researchgate.net/publication/229124308_Process_Mining_with_the_Heuristics_Miner-algorithm)
* Genetic miner. [Genetic process mining: An experimental evaluation](https://www.researchgate.net/publication/226194368_Genetic_process_mining_An_experimental_evaluation)
* Fuzzy miner. [Fuzzy Mining – Adaptive Process Simplification Based on Multi-Perspective Metrics](hhttps://www.researchgate.net/publication/221586306_Fuzzy_Mining_-_Adaptive_Process_Simplification_Based_on_Multi-perspective_Metrics)
* Integer Linear Programming miner. [Process Mining using Integer Linear Programming](https://pdfs.semanticscholar.org/19c4/c1b7779fcf47cfb7bc2eb214ea27f5a1270c.pdf)

## Conferences
* [Process Mining Conference Series](https://icpmconference.org)

## Videos
* [Process Mining Data Science in Action - Wil van der Aalst](https://youtu.be/kIeLaNzw9hI)
* [Introduction to Event Log Mining with R](https://youtu.be/_KMiEEStB9g). Content: the scenarios and benefits of event log mining; The minimum data required for event log mining; Ingesting and analyzing event log data using R; Process Mining with ProM.
* [Process Mining in Python](https://www.youtube.com/watch?v=i6NGHvZyHXY). Short process mining overview by pm4py developer.

## Research divisions/Researchers
* [Wil van der Aalst](http://www.padsweb.rwth-aachen.de/wvdaalst/index.html)
* [Laboratory of Process-Aware Information Systems](https://pais.hse.ru/en/)
* [Andrea Burattin](https://andrea.burattin.net/)
* [Alessandro Berti](http://www.alessandroberti.it/)
* [Suriadi Suriadi](https://www.researchgate.net/profile/Suriadi_Suriadi)
* [Sebastiaan J. van Zelst](https://sebastiaanvanzelst.com/)
* [Maikel L. van Eck](https://research.tue.nl/en/persons/maikel-l-van-eck)
* [Sander Leemans](http://leemans.ch/leemansCH/Sander/)

## Books
* [Process Mining: Data Science in Action](http://www.processmining.org/book/start). This is the second edition of Wil van der Aalst’s seminal book on process mining, which now discusses the field also in the broader context of data science and big data approaches. It includes several additions and updates, e.g. on inductive mining techniques, the notion of alignments, a considerably expanded section on software tools and a completely new chapter of process mining in the large. It is self-contained, while at the same time covering the entire process-mining spectrum from process discovery to predictive analytics.
* [A Primer on Process Mining.Practical Skills with Python and Graphviz](https://www.springer.com/gp/book/9783319564265).The main goal of this book is to explain the core ideas of process mining, and to demonstrate how they can be implemented using just some basic tools that are available to any computer scientist or data scientist. It describes how to analyze event logs in order to discover the behavior of real-world business processes. The end result can often be visualized as a graph, and the book explains how to use Python and Graphviz to render these graphs intuitively. Overall, it enables the reader to implement process mining techniques on his or her own, independently of any specific process mining tool. An introduction to two popular process mining tools, namely Disco and ProM, is also provided + [github](https://github.com/diogoff/primer-process-mining)

## Competitions
* [First International Business Process Intelligence Challenge (BPIC’11)](https://www.win.tue.nl/bpi/2011/challenge.html). 
* [Second International Business Process Intelligence Challenge (BPIC’12)](https://www.win.tue.nl/bpi/2012/challenge.html)
* [Third International Business Process Intelligence Challenge (BPIC’13)](https://www.win.tue.nl/bpi/2013/challenge.html)
* [Fourth International Business Process Intelligence Challenge (BPIC’14)](https://www.win.tue.nl/bpi/2014/challenge.html)
* [Fifth International Business Process Intelligence Challenge (BPIC’15)](https://www.win.tue.nl/bpi/2015/challenge.html)
* [Sixth International Business Process Intelligence Challenge (BPIC’16)](https://www.win.tue.nl/bpi/2016/challenge.html)
* [Seventh International Business Process Intelligence Challenge (BPIC’17)](https://www.win.tue.nl/bpi/2017/challenge.html)
* [Eighth International Business Process Intelligence Challenge (BPIC’18)](https://www.win.tue.nl/bpi/2018/challenge.html)
* [Ninth International Business Process Intelligence Challenge (BPIC’19)](https://icpmconference.org/2019/icpm-2019/contests-challenges/bpi-challenge-2019/)
* [Process Discovery Contest 2017](https://www.win.tue.nl/ieeetfpm/doku.php?id=shared:process_discovery_contest)
* [Process Discovery Contest 2019](https://icpmconference.org/2019/process-discovery-contest)
* [Conformance Checking Challenge 2019](https://icpmconference.org/2019/icpm-2019/contests-challenges/1st-conformance-checking-challenge-2019-ccc19/)
* [Process Discovery Contest 2020](https://icpmconference.org/2020/process-discovery-contest/)
* [Tenth International Business Process Intelligence Challenge (BPIC’20)](https://icpmconference.org/2020/bpi-challenge/)

## Courses
* [Introduction to process mining with ProM](https://www.futurelearn.com/courses/process-mining)
* [Process Mining: Data science in Action](https://www.coursera.org/learn/process-mining)
* [Process mining in healthcare](https://www.futurelearn.com/courses/process-mining-healthcare)
* [Artificial Intelligence for Business](https://www.udemy.com/course/ai-for-business/)

## Software
* [PM4PY](http://pm4py.org/) is a python library that supports (state-of-the-art) process mining algorithms in python. It is completely open source and intended to be used in both academia and industry projects.
* [ProM](http://www.promtools.org/) is an extensible framework that supports a wide variety of process mining techniques in the form of plug-ins. It is platform independent as it is implemented in Java, and can be downloaded free of charge. We welcome and support practical applications of ProM, and we invite researchers and developers to contribute in the form of new plug-ins.
* [Disco](https://fluxicon.com/disco/)
* [ProcessExplorer](https://fileserver.tk.informatik.tu-darmstadt.de/AS/processexplorer/) is an academic process mining tool which guides analysts through event logs by suggesting subset and insights recommendations.
* [bupaR](https://www.bupar.net/) is an open-source, integrated suite of R-packages for the handling and analysis of business process data. It currently consists of 8 packages, including the central package, supporting different stages of a process mining workflow.
* [XESame](http://www.processmining.org/xesame/start) is an application that supports in the extraction of an event log from non-event log data sources.
* [Uma](https://github.com/dfahland/uma) is a Java-library for analyzing and synthesizing Petri net models using unfolding-based techniques.
* [MXMLib](http://www.processmining.org/tools/mxmlib) is a Java library, used by ProMimport among others, when converting logs to the MXML format.
* [CPN Tools](http://cpntools.org/). A tool for editing, simulating, and analyzing Colored Petri nets.
* [visual Miner](http://leemans.ch/visualminer/home/) is a free and open source process mining tool, which makes it easy to explore an event log.
* [QPR Process Analyzer](https://www.qpr.com/products/qpr-processanalyzer)

## Reviews
* [Process mining techniques and applications – A systematic mapping study](https://www.sciencedirect.com/science/article/pii/S0957417419303161). The objective of this article is to map the active research topics of process mining and their main publishers by country, periodicals, and conferences. Also extract the reported application studies and classify these by exploration domains or industry segments that are taking advantage of this technique. The applied research method was systematic mapping, which began with 3713 articles. After applying the exclusion criteria, 1278 articles were selected for review.
* [Process Mining: A DATABASE OF APPLICATIONS](https://www.win.tue.nl/ieeetfpm/lib/exe/fetch.php?media=news:process_mining_database_applications_2018b.pdf). The idea of creating the present database of applications came up within HSPI in 2016, during an informal meeting about process mining technology and its spread over several business – and not only – situations. The need to collect, to put into an ordered system all the historical information about process mining techniques implementations has led the creation of the very first version of " Process Mining: A Database of Applications".
* [Automated Discovery of Process Models from Event Logs: Review and Benchmark](https://arxiv.org/abs/1705.02288). This article provides a systematic review and comparative evaluation of automated process discovery methods, using an open-source benchmark and covering twelve publicly-available real-life event logs, twelve proprietary real-life event logs, and nine quality metrics. The results highlight gaps and unexplored tradeoffs in the field, including the lack of scalability of some methods and a strong divergence in their performance with respect to the different quality metrics used.

## Implementation (github)
* [Predictive Performance Monitoring of Material Handling Systems Using the Performance Spectrum"](https://github.com/processmining-in-logistics/psm/tree/ppm). The Performance Spectrum Miner (PSM) is a visual analytics tool for event data. It takes as input an event log (of events, timestamps, and case identifier) of past process or system executions in CSV or XES format. The PSM visualizes the flow of all cases over all process over time, and gives detailed insights performance characteristics.
* [Assessing Big Data SQL Frameworks for Analyzing Event Logs; Classifying Processes Instances Using Recurrent Neural Networks; Exploiting Event Log Event Attributes in RNN Based Prediction; Structural Feature Selection from Event Logs](https://github.com/mhinkka/articles)
* [MINERful](https://github.com/cdc08x/MINERful). MINERful is a fast process mining tool for discovering declarative process models out of event logs. Event logs can be either real or synthetic, stored as XES, MXML, or text files (a collection of strings, in which every character is considered as an event, every line as a trace). Among the other things, MINERful can also create synthetic logs and export them as XES or MXML files, simplify existing Declare models, and import/export models written in JSON or in the ConDec native language.
* [Comprehensive Process Drift Detection with Visual Analytics (VDD technique)](https://github.com/yesanton/Process-Drift-Visualization-With-Declare). This technique supports the discovery of process drifts in the processes from event logs. Load your timestamp sorted csv and xes files to the tool to discover Drfit Maps, Drift charts, and Declare relations that see which part of the log is changing.
* [pmbot - A Telegram Bot for Process Mining tasks](https://github.com/delas/pmbot), [paper](https://andrea.burattin.net/public-files/publications/2019-bpm-demo.pdf). This projects contains a Telegram Bot for performinig Process Mining tasks and small prjects.
* [ProcessCubeExplorer](https://github.com/pgmpm/ProcessCubeExplorer) Process Mining extracts implicit knowledge from event logs and generates process models to visualize the underlying process. The Process Cube Explorer, realized by C#, can connect to various databases and load multidimensional event logs in which the characteristics of the process-instance are modelled as dimensions in a datawarehouse-like schema.

## Implementation (ProM)
* Inter-Level Replayer plug-in for the Security package. [Paper](https://www.sciencedirect.com/science/article/pii/S0167404817302262). [Dataset](https://svn.win.tue.nl/repos/prom/Packages/Security/Trunk/example/healthcareProcess/).

## Process Mining Conference 2023 https://icpmconference.org/2023/programme/
### PODS4H23
* https://drive.google.com/file/d/1w1ivYeeKD5b5ohnsO5FcBSO0IJsMnXlH/view?usp=sharing
* https://drive.google.com/file/d/1fXD_fMI2z7Q3MBV9BU6IrTXx5dBrn5dl/view?usp=sharing

### Research track
* https://icpmconference.org/2023/wp-content/uploads/sites/8/2023/11/Data-Driven-Goal-Recognition-in-Transhumeral-Prostheses-Using-Process-Mining-Techniques.pdf
* https://icpmconference.org/2023/wp-content/uploads/sites/8/2023/11/Plan-Recognition-as-Probabilistic-Trace-Alignment.pdf
* https://www.slideshare.net/MarlonDumas/discovery-and-simulation-of-business-processes-with-probabilistic-resource-availability-calendars
* https://icpmconference.org/2023/wp-content/uploads/sites/8/2023/11/Runtime-Integration-of-Machine-Learning-and-Simulation-for-Business-Processes.pdf
* https://icpmconference.org/2023/wp-content/uploads/sites/8/2023/11/Manifold-Learning-for-Adversarial-Robustness-in-Predictive-Process-Monitoring.pdf https://alexanderpaulstevens.github.io
* https://icpmconference.org/2023/wp-content/uploads/sites/8/2023/12/ICPM2023_kabierski_log_completeness.pdf
* https://adamburkeware.net/papers/burke_et_al_ssnap_slides_icpm2023.pdf https://github.com/adamburkegh/statesnap-miner
* https://icpmconference.org/2023/wp-content/uploads/sites/8/2023/11/A-Fresh-Approach-To-Analyze-Process-Outcomes.pdf https://github.com/IBM/rulelearn
* https://icpmconference.org/2023/wp-content/uploads/sites/8/2023/12/ICPM-presentation.pdf
* https://icpmconference.org/2023/wp-content/uploads/sites/8/2023/11/Business-Process-Deviation-Prediction.pdf
*https://icpmconference.org/2023/wp-content/uploads/sites/8/2023/11/Enhancing-the-Applicability-of-the-eST-Miner-Efficient-Precision-Guided-Implicit-Place-Avoidance.pdf

### Research track keynote
* https://icpmconference.org/2023/wp-content/uploads/sites/8/2023/11/Stochstically-Known-Logs.pdf

### Industry track keynote
* https://www.slideshare.net/MarlonDumas/walking-the-way-from-process-mining-to-aidriven-process-optimization https://github.com/AutomatedProcessImprovement/Simod

### Doctoral Consortium keynote
https://icpmconference.org/2023/wp-content/uploads/sites/8/2023/10/ICPM2023_DC_keynote.pdf

### ML4PM 2023 FOURTH INTERNATIONAL WORKSHOP ON LEVERAGING MACHINE LEARNING IN PROCESS MINING
* https://ml4pm2023.di.unimi.it/preproceedings/Keynote-ML4PM2023-JochenDeWeerdt.pdf
* https://ml4pm2023.di.unimi.it/preproceedings/ICPM_2023_paper_904.pdf
* https://ml4pm2023.di.unimi.it/preproceedings/ICPM_2023_paper_2001.pdf
* https://ml4pm2023.di.unimi.it/preproceedings/ICPM_2023_paper_3400.pdf
* https://ml4pm2023.di.unimi.it/preproceedings/ICPM2023presentation.pptx
* https://ml4pm2023.di.unimi.it/preproceedings/ICPM_2023_paper_5347.pdf
* https://ml4pm2023.di.unimi.it/preproceedings/ICPM_2023_paper_1709.pdf
* https://ml4pm2023.di.unimi.it/preproceedings/ICPM_2023_paper_3645-2.pdf

### EdbA'23 - Fourth International Workshop on Event Data and  Behavioral Analytics
* http://edba.science/wp-content/uploads/2023/10/ICPM_2023_paper_2170.pdf
* http://edba.science/wp-content/uploads/2023/10/ICPM_2023_paper_1065.pdf
* http://edba.science/wp-content/uploads/2023/10/ICPM_2023_paper_980.pdf
* http://edba.science/wp-content/uploads/2023/10/ICPM_2023_paper_2954.pdf
* http://edba.science/wp-content/uploads/2023/10/ICPM_2023_paper_3071.pdf
* https://drive.google.com/file/d/1Cpg0uqB6OGUCRt-FuuytETh8rocYV5kG/view?usp=drivesdk
* http://edba.science/wp-content/uploads/2023/10/ICPM_2023_paper_8632.pdf
* http://edba.science/wp-content/uploads/2023/10/ICPM_2023_paper_8898.pdf
* http://edba.science/wp-content/uploads/2023/10/ICPM_2023_paper_5670.pdf
* http://edba.science/wp-content/uploads/2023/10/ICPM_2023_paper_7205.pdf

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