Event Log Sampling for Predictive Monitoring

Mohammadreza Fani Sani; Mozhgan Vazifehdoostirani; Gyunam  Park; Marco  Pegoraro; S.J. (Bas) van Zelst; Wil M.P. van der Aalst

doi:10.1007/978-3-030-98581-3_12

Event Log Sampling for Predictive Monitoring

Mohammadreza Fani Sani, Mozhgan Vazifehdoostirani, Gyunam Park, Marco Pegoraro, S.J. (Bas) van Zelst, Wil M.P. van der Aalst

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

4 Citaten (Scopus)

178 Downloads (Pure)

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Predictive process monitoring is a subfield of process mining that aims to estimate case or event features for running process instances. Such predictions are of significant interest to the process stakeholders. However, state-of-the-art methods for predictive monitoring require the training of complex machine learning models, which is often inefficient. This paper proposes an instance selection procedure that allows sampling training process instances for prediction models. We show that our sampling method allows for a significant increase of training speed for next activity prediction methods while maintaining reliable levels of prediction accuracy.

Originele taal-2	Engels
Titel	Process Mining Workshops - ICPM 2021 International Workshops, Revised Selected Papers
Subtitel	ICPM 2021 International Workshops, Eindhoven, The Netherlands, October 31 – November 4, 2021, Revised Selected Papers
Redacteuren	Jorge Munoz-Gama, Xixi Lu
Plaats van productie	Cham
Uitgeverij	Springer
Pagina's	154-166
Aantal pagina's	13
ISBN van elektronische versie	978-3-030-98581-3
ISBN van geprinte versie	978-3-030-98580-6
DOI's	https://doi.org/10.1007/978-3-030-98581-3_12
Status	Gepubliceerd - 2022
Evenement	2nd International Workshop on Leveraging Machine Learning in Process Mining, ML4PM 2021 - Eindhoven, Nederland Duur: 31 okt. 2021 → 4 nov. 2021 Congresnummer: 2 http://ml4pm2021.di.unimi.it/

Publicatie series

Naam	Lecture Notes in Business Information Processing
Volume	433
ISSN van geprinte versie	1865-1348
ISSN van elektronische versie	1865-1356

Workshop

Workshop	2nd International Workshop on Leveraging Machine Learning in Process Mining, ML4PM 2021
Verkorte titel	ML4PM 2021
Land/Regio	Nederland
Stad	Eindhoven
Periode	31/10/21 → 4/11/21
Internet adres	http://ml4pm2021.di.unimi.it/

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10.1007/978-3-030-98581-3_12

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Fani Sani, M., Vazifehdoostirani, M., Park, G., Pegoraro, M., van Zelst, S. J., & van der Aalst, W. M. P. (2022). Event Log Sampling for Predictive Monitoring. In J. Munoz-Gama, & X. Lu (editors), Process Mining Workshops - ICPM 2021 International Workshops, Revised Selected Papers: ICPM 2021 International Workshops, Eindhoven, The Netherlands, October 31 – November 4, 2021, Revised Selected Papers (blz. 154-166). (Lecture Notes in Business Information Processing; Vol. 433). Springer. https://doi.org/10.1007/978-3-030-98581-3_12

Fani Sani, Mohammadreza ; Vazifehdoostirani, Mozhgan ; Park, Gyunam et al. / Event Log Sampling for Predictive Monitoring. Process Mining Workshops - ICPM 2021 International Workshops, Revised Selected Papers: ICPM 2021 International Workshops, Eindhoven, The Netherlands, October 31 – November 4, 2021, Revised Selected Papers. redacteur / Jorge Munoz-Gama ; Xixi Lu. Cham : Springer, 2022. blz. 154-166 (Lecture Notes in Business Information Processing).

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title = "Event Log Sampling for Predictive Monitoring",

abstract = "Predictive process monitoring is a subfield of process mining that aims to estimate case or event features for running process instances. Such predictions are of significant interest to the process stakeholders. However, state-of-the-art methods for predictive monitoring require the training of complex machine learning models, which is often inefficient. This paper proposes an instance selection procedure that allows sampling training process instances for prediction models. We show that our sampling method allows for a significant increase of training speed for next activity prediction methods while maintaining reliable levels of prediction accuracy.",

keywords = "Process Mining, Perdicitive Monitoring, Sampling, Machine Learning, Deep Learning, Instance Selection, Predictive Monitoring",

author = "{Fani Sani}, Mohammadreza and Mozhgan Vazifehdoostirani and Gyunam Park and Marco Pegoraro and {van Zelst}, {S.J. (Bas)} and {van der Aalst}, {Wil M.P.}",

year = "2022",

doi = "10.1007/978-3-030-98581-3_12",

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booktitle = "Process Mining Workshops - ICPM 2021 International Workshops, Revised Selected Papers",

note = "2nd International Workshop on Leveraging Machine Learning in Process Mining, ML4PM 2021, ML4PM 2021 ; Conference date: 31-10-2021 Through 04-11-2021",

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Fani Sani, M, Vazifehdoostirani, M, Park, G, Pegoraro, M, van Zelst, SJ & van der Aalst, WMP 2022, Event Log Sampling for Predictive Monitoring. in J Munoz-Gama & X Lu (uitgave), Process Mining Workshops - ICPM 2021 International Workshops, Revised Selected Papers: ICPM 2021 International Workshops, Eindhoven, The Netherlands, October 31 – November 4, 2021, Revised Selected Papers. Lecture Notes in Business Information Processing, vol. 433, Springer, Cham, blz. 154-166, 2nd International Workshop on Leveraging Machine Learning in Process Mining, ML4PM 2021, Eindhoven, Nederland, 31/10/21. https://doi.org/10.1007/978-3-030-98581-3_12

Event Log Sampling for Predictive Monitoring. / Fani Sani, Mohammadreza; Vazifehdoostirani, Mozhgan; Park, Gyunam et al.
Process Mining Workshops - ICPM 2021 International Workshops, Revised Selected Papers: ICPM 2021 International Workshops, Eindhoven, The Netherlands, October 31 – November 4, 2021, Revised Selected Papers. uitgave / Jorge Munoz-Gama; Xixi Lu. Cham: Springer, 2022. blz. 154-166 (Lecture Notes in Business Information Processing; Vol. 433).

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

TY - GEN

T1 - Event Log Sampling for Predictive Monitoring

AU - Fani Sani, Mohammadreza

AU - Vazifehdoostirani, Mozhgan

AU - Park, Gyunam

AU - Pegoraro, Marco

AU - van Zelst, S.J. (Bas)

AU - van der Aalst, Wil M.P.

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AB - Predictive process monitoring is a subfield of process mining that aims to estimate case or event features for running process instances. Such predictions are of significant interest to the process stakeholders. However, state-of-the-art methods for predictive monitoring require the training of complex machine learning models, which is often inefficient. This paper proposes an instance selection procedure that allows sampling training process instances for prediction models. We show that our sampling method allows for a significant increase of training speed for next activity prediction methods while maintaining reliable levels of prediction accuracy.

KW - Process Mining

KW - Perdicitive Monitoring

KW - Sampling

KW - Machine Learning

KW - Deep Learning

KW - Instance Selection

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T3 - Lecture Notes in Business Information Processing

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BT - Process Mining Workshops - ICPM 2021 International Workshops, Revised Selected Papers

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Fani Sani M, Vazifehdoostirani M, Park G, Pegoraro M, van Zelst SJ, van der Aalst WMP. Event Log Sampling for Predictive Monitoring. In Munoz-Gama J, Lu X, redacteurs, Process Mining Workshops - ICPM 2021 International Workshops, Revised Selected Papers: ICPM 2021 International Workshops, Eindhoven, The Netherlands, October 31 – November 4, 2021, Revised Selected Papers. Cham: Springer. 2022. blz. 154-166. (Lecture Notes in Business Information Processing). doi: 10.1007/978-3-030-98581-3_12

Event Log Sampling for Predictive Monitoring

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