Accepted Papers

Accepted papers that use PM4Py

This page wants to collect all the papers in the scientific world that have been accepted/published using the PM4Py library (or custom forks of the library)

Demos:

Process Mining for Python (PM4Py): Bridging the Gap Between Process- and Data Science 

Alessandro Berti, Sebastiaan van Zelst, Wil van der Aalst

Accepted at ICPM 2019 Demo Track (ICPM 2019, 26 June 2019, CEUR Vol-2374 ISSN 1613-0073)

@inproceedings{berti2019process,
title={{Process Mining for Python (PM4Py): Bridging the Gap Between Process-and Data Science}},
author={Berti, Alessandro and van Zelst, Sebastiaan J and van der Aalst, Wil},
booktitle={ICPM Demo Track (CEUR 2374)},
pages={13–16},
year={2019}
}

 

PM4Py Web Services: Easy Development, Integration and Deployment of Process Mining Features in any Application Stack 

Alessandro Berti, Sebastiaan van Zelst, Wil van der Aalst

Accepted at BPM 2019 Demo Track (BPM 2019, 04 September 2019)

@inproceedings{berti2019webservices,
title={{PM4Py Web Services: Easy Development, Integration and Deployment of Process Mining Features in any Application Stack}},
author={Berti, Alessandro and van Zelst, Sebastiaan J and van der Aalst, Wil},
booktitle={BPM Demo Track},
year={2019}
}

 

CONFERENCE PAPERS:

Mining Uncertain Event Data in Process Mining 

Marco Pegoraro, Wil van der Aalst

Accepted at ICPM 2019 Main Track (ICPM 2019, 26 June 2019)

@inproceedings{marco2019mining,
title={{Mining Uncertain Event Data in Process Mining}},
author={Pegoraro, Marco and van der Aalst, Wil},
journal={ICPM 2019 Proceedings},
pages={89—96},
year={2019}
}

 

WORKSHOP PAPERS:

Discovering Process Models from Uncertain Event Data

Marco Pegoraro, Merih Seran Uysal, Wil van der Aalst

Accepted at BPI 2019 Workshop (BPM 2019, 02 September 2019)

@inproceedings{marco2019discovering,
title={{Discovering Process Models from Uncertain Event Data}},
author={Pegoraro, Marco and Seran, Merih Uysal and van der Aalst, Wil},
journal={Business Process Intelligence (BPI) workshop 2019},
year={2019}
}

 

A Generic Approach for Process Performance Analysis using Bipartite Graph Matching

Chiao-Yun Li, Sebastiaan van Zelst, Wil van der Aalst

Accepted at BPI 2019 Workshop (BPM 2019, 02 September 2019)

@inproceedings{chiao2019performance,
title={{A Generic Approach for Process Performance Analysis using Bipartite Graph Matching}},
author={Chiao-Yun Li and van Zelst, Sebastiaan J and van der Aalst, Wil},
journal={Business Process Intelligence (BPI) workshop 2019},
year={2019}
}

 

Reviving Token-based Replay: Increasing Speed While Improving Diagnostics

Alessandro Berti, Wil van der Aalst

Accepted at ATAED 2019 Workshop (ICPM 2019, 25 June 2019, CEUR Vol-2371 ISSN 1613-0073)

@inproceedings{berti2019reviving,
title={{Reviving Token-based Replay: Increasing Speed While Improving Diagnostics}},
author={Berti, Alessandro and van der Aalst, Wil},
journal={Algorithms \& Theories for the Analysis of Event Data (ATAED’2019) (CEUR 2371)},
pages={87—103},
year={2019}
}

 

DOCTORAL CONSORTIUM:

Process Mining on Event Graphs: a Framework to Extensively Support Projects

Alessandro Berti

Accepted at BPM 2019 Doctoral Consortium (BPM 2019, 01 September 2019)

@inproceedings{berti2019dc,
title={Process Mining on Event Graphs: a Framework to Extensively Support Projects},
author={Berti, Alessandro},
booktitle={BPM Demo Track},
year={2019}
}

 

 

Other contributions not using/regarding PM4Py, but citing the library:

Filtering Toolkit: Interactively Filter Event Logs to Improve the Quality of Discovered Models 

MohammadReza Fani Sani, Alessandro Berti, Sebastiaan van Zelst, Wil van der Aalst

Accepted at BPM 2019 Demo Track (BPM 2019, 04 September 2019)

@inproceedings{fani2019filtering,
title={{Filtering Toolkit: Interactively Filter Event Logs to Improve the Quality of Discovered Models}},
author={MohammadReza, Fani Sani and Berti, Alessandro and van Zelst, Sebastiaan J and van der Aalst, Wil},
booktitle={BPM Demo Track},
year={2019}
}
Pre-prints:
Alessandro Berti
@article{berti2019parquet,
title={Increasing Scalability of Process Mining using Event Dataframes: How Data Structure Matters},
author={Berti, Alessandro},
journal={arXiv preprint arXiv:1907.12817},
year={2019}
}