{"id":37083651,"url":"https://github.com/mikeriess/synbps","last_synced_at":"2026-01-14T10:12:30.113Z","repository":{"id":214794590,"uuid":"734844049","full_name":"Mikeriess/SynBPS","owner":"Mikeriess","description":"A simulation framework for synthetic event-log data, based on theoretical processes.","archived":false,"fork":false,"pushed_at":"2024-07-28T21:22:47.000Z","size":5368,"stargazers_count":2,"open_issues_count":2,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-11-28T02:05:15.660Z","etag":null,"topics":["event-log","python","simulation-framework"],"latest_commit_sha":null,"homepage":"https://pypi.org/project/synbps","language":"Python","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/Mikeriess.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,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2023-12-22T19:35:20.000Z","updated_at":"2024-12-30T05:22:25.000Z","dependencies_parsed_at":"2024-07-23T00:52:58.135Z","dependency_job_id":null,"html_url":"https://github.com/Mikeriess/SynBPS","commit_stats":{"total_commits":44,"total_committers":1,"mean_commits":44.0,"dds":0.0,"last_synced_commit":"c007af11a5c5ccd64a683eaa81c923a2fa668c6b"},"previous_names":["mikeriess/synbps"],"tags_count":5,"template":false,"template_full_name":null,"purl":"pkg:github/Mikeriess/SynBPS","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Mikeriess%2FSynBPS","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Mikeriess%2FSynBPS/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Mikeriess%2FSynBPS/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Mikeriess%2FSynBPS/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Mikeriess","download_url":"https://codeload.github.com/Mikeriess/SynBPS/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Mikeriess%2FSynBPS/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28416679,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-01-14T08:38:59.149Z","status":"ssl_error","status_checked_at":"2026-01-14T08:38:43.588Z","response_time":107,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.6:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"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":["event-log","python","simulation-framework"],"created_at":"2026-01-14T10:12:29.511Z","updated_at":"2026-01-14T10:12:30.098Z","avatar_url":"https://github.com/Mikeriess.png","language":"Python","readme":"# SynBPS\n[![Downloads](https://static.pepy.tech/badge/synbps)](https://pepy.tech/project/synbps) [![Documentation Status](https://readthedocs.org/projects/synbps/badge/?version=latest)](https://synbps.readthedocs.io/en/latest/?badge=latest)\n\n\nSynBPS is short for Synthetic Business Process Simulation. This framework is designed to simulate **synthetic** business processes. In a nutshell, this framework lets you run predictive process monitoring experiments across **multiple business processes**, specified by well-known parametric distributions. See more in the publication: [Riess (2024)](https://journals.sagepub.com/doi/abs/10.1177/00375497241233326) [[pdf](https://journals.sagepub.com/doi/pdf/10.1177/00375497241233326?casa_token=h9BOK2WWdQQAAAAA:t46xt6_qhz651cLzDVktuPnr3ku-eRaWNk9vECyHEAZsl3OtUHCffCZncn48XI0BprdrZM8VcBT3)]\n\n\n![image](https://github.com/Mikeriess/SynBPS/blob/main/docs/illustration.png)\n\n## Whats new: Version 1.1.3\n- Added support for process memory with HOMC of order \u003e 4\n- Added Example notebooks in ```examples/``` folder\n- Added ability to specify distribution parameters (memoryless process)\n- Added ability to specify the dataprep function manually (see [e2e example notebook](https://github.com/Mikeriess/SynBPS/blob/main/examples/simulation_e2e_example.ipynb))\n- Fixed issues with seed value in processes with memory\n- Restructuring and separation of functions, based on their purpose: \n\t- ```Design``` for generating a DoE\n\t- ```Simulation``` for functions related to event-log generation\n\t- ```Dataprep``` for functions related to data-preparation for ML models (prefix-log, temporal splitting etc.,)\n- Updated readthedocs documentation with version *1.1.0+* syntax changes.\n- Other minor fixes\n\n**Please note:** Version 1.1.0** introduces new parameters and different function locations. Users are therefore advised to refer to the slightly changed code examples in ```examples/``` folder.\n\n# Getting Started\nYou can install SynBPS using pip:\n\n    pip install SynBPS\n\nOnce installed, you can:\n\n- Run a simulation experiment with your own models using the [End-to-end example notebook](https://github.com/Mikeriess/SynBPS/blob/main/examples/simulation_e2e_example.ipynb) for a short demo of SynBPS. \n- Or simply generate a single event-log using the example code in the [Event-log example notebook](https://github.com/Mikeriess/SynBPS/blob/main/examples/event_log_example.ipynb). This code example also lets you integrate the power of SynBPS into your own custom code pipeline (for advanced users).\n- For the memoryless process, you can also specify the parameters of the distributions manually as shown in the [Custom distribution Event-log example notebook](https://github.com/Mikeriess/SynBPS/blob/main/examples/event_log_example_custom_dist.ipynb).\n\n\n## Documentation\nSee the [official documentation here](https://synbps.readthedocs.io/en/latest/).\n\n\n## Citation\nIf you use SynBPS, please cite the corresponding paper. The paper can be cited as:\n\n```\n@article{riess2024synbps,\n\ttitle={SynBPS: a parametric simulation framework for the generation of event-log data},\n\tauthor={Riess, Mike},\n\tjournal={SIMULATION},\n\tpages={00375497241233326},\n\tyear={2024},\n\tpublisher={SAGE Publications Sage UK: London, England}\n}\n```\n\n## Contributing\nIf you would like to contribute to SynBPS, you are welcome to submit your suggestions, bug reports, or pull requests. Follow [the guidelines](https://github.com/Mikeriess/SynBPS/blob/main/src/contributing.md) to ensure smooth collaboration.\n\n\n## Thanks\nJacob Schreiber and Pomegranate team. Joachim Scholderer and Kristoffer Lien.","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmikeriess%2Fsynbps","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmikeriess%2Fsynbps","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmikeriess%2Fsynbps/lists"}