{"id":24552999,"url":"https://github.com/fmannhardt/course-applied-processmining","last_synced_at":"2025-04-15T23:13:45.893Z","repository":{"id":62372753,"uuid":"358913866","full_name":"fmannhardt/course-applied-processmining","owner":"fmannhardt","description":"Introduction to Applied Process Mining with Python and R notebooks.","archived":false,"fork":false,"pushed_at":"2022-12-12T18:21:34.000Z","size":19529,"stargazers_count":17,"open_issues_count":0,"forks_count":4,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-04-15T23:13:21.963Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/fmannhardt.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2021-04-17T15:25:53.000Z","updated_at":"2025-03-27T00:18:55.000Z","dependencies_parsed_at":"2023-01-27T23:30:46.054Z","dependency_job_id":null,"html_url":"https://github.com/fmannhardt/course-applied-processmining","commit_stats":null,"previous_names":[],"tags_count":0,"template":true,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fmannhardt%2Fcourse-applied-processmining","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fmannhardt%2Fcourse-applied-processmining/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fmannhardt%2Fcourse-applied-processmining/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fmannhardt%2Fcourse-applied-processmining/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/fmannhardt","download_url":"https://codeload.github.com/fmannhardt/course-applied-processmining/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":249167448,"owners_count":21223506,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2025-01-23T01:33:47.570Z","updated_at":"2025-04-15T23:13:45.864Z","avatar_url":"https://github.com/fmannhardt.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Applied Process Mining\n\nThe notebooks in this repository are part of a course on Applied Process Mining course given by Dr. Felix Mannhardt ([@fmannhardt](https://twitter.com/fmannhardt)) of [Process Analytics group](https://pa.win.tue.nl/) at Eindhoven University of Technology. In total there are currently *4* lectures and the associated hands-on notebooks in this repository. The collection of notebooks is a *living document* and subject to change. Each lecture is accompanied by a notebook in both R and Python using the Process Mining frameworks bupaR and PM4Py, respectively.\n\n\n## Table of Contents\n\n### Block 1 - 'Event Logs and Process Visualization'\n\n* Lecture Notebooks\n    *  [R](r/lecture1-eventlogs.ipynb) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/fmannhardt/course-applied-processmining/HEAD?urlpath=lab%2Ftree%2Fr%2Flecture1-eventlogs.ipynb)\n    *  [Python](python/lecture1-eventlogs.ipynb) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/fmannhardt/course-applied-processmining/HEAD?urlpath=lab%2Ftree%2Fpython%2Flecture1-eventlogs.ipynb)\n\n### Block 2 - 'Process Discovery'\n\n* Lecture Notebooks\n    *  [R](r/lecture2-discovery.ipynb) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/fmannhardt/course-applied-processmining/HEAD?urlpath=lab%2Ftree%2Fr%2Flecture2-discovery.ipynb) \n    *  [Python](python/lecture2-discovery.ipynb) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/fmannhardt/course-applied-processmining/HEAD?urlpath=lab%2Ftree%2Fpython%2Flecture2-discovery.ipynb)\n\n### Block 3 - 'Conformance Checking'\n\n* Lecture Notebooks\n    *  🚧 (there is currently not conformance checking functionality in R)\n    *  [Python](python/lecture3-conformance.ipynb) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/fmannhardt/course-applied-processmining/HEAD?urlpath=lab%2Ftree%2Fpython%2Flecture3-conformance.ipynb)\n\n### Block 4 - 'Predictive Process Mining'\n\n* Lecture Notebooks\n    *  🚧 (R version is under construction)\n    * [Python](python/lecture4-prediction.ipynb) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/fmannhardt/course-applied-processmining/HEAD?urlpath=lab%2Ftree%2Fpython%2Flecture4-prediction.ipynb)\n\n## Installation \\\u0026 Usage\n\n### Using MyBinder\n\nSimply click on the `launch binder` links for either the R or the Python notebook. You may also use the Google Colab service by clicking on the Google Colab links, however, you may need to prepare the Google Colab environment to have the respective packages installed.\n\n### Run locally\n\n#### Docker\n\nSimply build a Docker image with the provided Dockerfile:\n\n```\ndocker build -t fmannhardt/course-applied-processmining .\n```\n\nAnd start the Docker container running Jupyter on [localhost:8888](http://localhost:8888?token=processmining):\n\n```\ndocker run --rm -ti -e JUPYTER_TOKEN=processmining -p 8888:8888 fmannhardt/course-applied-processmining\n```\n\nor use the Jupyter Lab interface:\n\n```\ndocker run --rm -ti -e JUPYTER_TOKEN=processmining -p 8888:8888 fmannhardt/course-applied-processmining sh -c \"jupyter lab --ip 0.0.0.0 --no-browser\"\n```\n\n#### Jupyter\n\nYou should be able to run the Jupyter notebooks directly in a Jupyter environment. Please make sure to have installed the following requirements:\n\n**Python**\n\n```\npip install -r requirements.txt\n```\n\nMake sure to install GraphViz for the visualization. On Windows with Chocolately this should work:\n```\nchoco install graphviz\n```\nConsult the [PM4Py documentation](https://pm4py.fit.fraunhofer.de/install) for further details.\n\n**R**\n\nInstall the Jupyter kernel for R:\n```\ninstall.packages(c(\"IRkernel\"))\n```\n\nand install the nessecary packages:\n```\nR --quiet -f install.R\n```\n\nDepending on your system configuration, it can be tricky to make the `IRkernel` known to Jupyter. Please follow the instructions on their [Github page](https://github.com/IRkernel/IRkernel). \nAs a hint, you may need to open the R console from an Anaconda console and perform `IRkernel::installspec()` in case you are using conda environment.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffmannhardt%2Fcourse-applied-processmining","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffmannhardt%2Fcourse-applied-processmining","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffmannhardt%2Fcourse-applied-processmining/lists"}