Predicting Remaining Cycle Time from Ongoing Cases: A Survival Analysis-Based Approach
Predicting the remaining cycle time of running cases is one important use case of predictive process monitoring. Different approaches that learn from event logs, e.g., relying on an existing representation of the process or leveraging machine learning approaches, have been pro posed in literature to...
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| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , |
| منشور في: |
2020
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://bspace.buid.ac.ae/handle/1234/2928 https://link.springer.com/chapter/10.1007/978-3-030-72693-5_8 https://doi.org/10.1007/978-3-030-72693-5_8 |
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| _version_ | 1862980611659005952 |
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| author | Baskharon, Fadi |
| author2 | Awad, Ahmed Di Francescomarino, Chiara |
| author2_role | author author |
| author_facet | Baskharon, Fadi Awad, Ahmed Di Francescomarino, Chiara |
| author_role | author |
| dc.creator.none.fl_str_mv | Baskharon, Fadi Awad, Ahmed Di Francescomarino, Chiara |
| dc.date.none.fl_str_mv | 2020 2025-05-06T08:54:40Z 2025-05-06T08:54:40Z |
| dc.identifier.none.fl_str_mv | Baskharon, F., Awad, A., Di Francescomarino, C. (2021). Predicting Remaining Cycle Time from Ongoing Cases: A Survival Analysis-Based Approach. In: Leemans, S., Leopold, H. (eds) Process Mining Workshops. ICPM 2020. Lecture Notes in Business Information Processing, vol 406. Springer, Cham. https://bspace.buid.ac.ae/handle/1234/2928 https://link.springer.com/chapter/10.1007/978-3-030-72693-5_8 https://doi.org/10.1007/978-3-030-72693-5_8 |
| dc.language.none.fl_str_mv | en |
| dc.publisher.none.fl_str_mv | Springer, Cham |
| dc.relation.none.fl_str_mv | Process Mining Workshops, 2021, Volume 406 |
| dc.subject.none.fl_str_mv | Predictive process monitoring · Remaining time prediction · Survival analysis · Incomplete traces |
| dc.title.none.fl_str_mv | Predicting Remaining Cycle Time from Ongoing Cases: A Survival Analysis-Based Approach |
| dc.type.none.fl_str_mv | Article |
| description | Predicting the remaining cycle time of running cases is one important use case of predictive process monitoring. Different approaches that learn from event logs, e.g., relying on an existing representation of the process or leveraging machine learning approaches, have been pro posed in literature to tackle this problem. Machine learning-based tech niques have shown superiority over other techniques with respect to the accuracy of the prediction as well as freedom from knowledge about the underlying process models generating the logs. However, all proposed approaches learn from complete traces. This might cause delays in start ing new training cycles as usually process instances might last over long time periods of hours, days, weeks or even months. In this paper, we propose a machine learning approach that can learn from incomplete ongoing traces. Using a time-aware survival analysis technique, we can train a neural network to predict the remaining cycle time of a running case. Our approach accepts as input both complete and incomplete traces. We have evaluated our approach on different real-life datasets and compared it with a state of the art baseline. Results show that our approach, in many cases, is able to outperform the baseline approach both in accuracy and training time |
| id | budr_21e745063ae1ade2fa3eeeb32582e9a2 |
| identifier_str_mv | Baskharon, F., Awad, A., Di Francescomarino, C. (2021). Predicting Remaining Cycle Time from Ongoing Cases: A Survival Analysis-Based Approach. In: Leemans, S., Leopold, H. (eds) Process Mining Workshops. ICPM 2020. Lecture Notes in Business Information Processing, vol 406. Springer, Cham. |
| language_invalid_str_mv | en |
| network_acronym_str | budr |
| network_name_str | The British University in Dubai repository |
| oai_identifier_str | oai:bspace.buid.ac.ae:1234/2928 |
| publishDate | 2020 |
| publisher.none.fl_str_mv | Springer, Cham |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Predicting Remaining Cycle Time from Ongoing Cases: A Survival Analysis-Based ApproachBaskharon, FadiAwad, AhmedDi Francescomarino, ChiaraPredictive process monitoring · Remaining time prediction · Survival analysis · Incomplete tracesPredicting the remaining cycle time of running cases is one important use case of predictive process monitoring. Different approaches that learn from event logs, e.g., relying on an existing representation of the process or leveraging machine learning approaches, have been pro posed in literature to tackle this problem. Machine learning-based tech niques have shown superiority over other techniques with respect to the accuracy of the prediction as well as freedom from knowledge about the underlying process models generating the logs. However, all proposed approaches learn from complete traces. This might cause delays in start ing new training cycles as usually process instances might last over long time periods of hours, days, weeks or even months. In this paper, we propose a machine learning approach that can learn from incomplete ongoing traces. Using a time-aware survival analysis technique, we can train a neural network to predict the remaining cycle time of a running case. Our approach accepts as input both complete and incomplete traces. We have evaluated our approach on different real-life datasets and compared it with a state of the art baseline. Results show that our approach, in many cases, is able to outperform the baseline approach both in accuracy and training timeSpringer, Cham2025-05-06T08:54:40Z2025-05-06T08:54:40Z2020ArticleBaskharon, F., Awad, A., Di Francescomarino, C. (2021). Predicting Remaining Cycle Time from Ongoing Cases: A Survival Analysis-Based Approach. In: Leemans, S., Leopold, H. (eds) Process Mining Workshops. ICPM 2020. Lecture Notes in Business Information Processing, vol 406. Springer, Cham.https://bspace.buid.ac.ae/handle/1234/2928https://link.springer.com/chapter/10.1007/978-3-030-72693-5_8https://doi.org/10.1007/978-3-030-72693-5_8enProcess Mining Workshops, 2021, Volume 406oai:bspace.buid.ac.ae:1234/29282025-08-13T09:26:45Z |
| spellingShingle | Predicting Remaining Cycle Time from Ongoing Cases: A Survival Analysis-Based Approach Baskharon, Fadi Predictive process monitoring · Remaining time prediction · Survival analysis · Incomplete traces |
| title | Predicting Remaining Cycle Time from Ongoing Cases: A Survival Analysis-Based Approach |
| title_full | Predicting Remaining Cycle Time from Ongoing Cases: A Survival Analysis-Based Approach |
| title_fullStr | Predicting Remaining Cycle Time from Ongoing Cases: A Survival Analysis-Based Approach |
| title_full_unstemmed | Predicting Remaining Cycle Time from Ongoing Cases: A Survival Analysis-Based Approach |
| title_short | Predicting Remaining Cycle Time from Ongoing Cases: A Survival Analysis-Based Approach |
| title_sort | Predicting Remaining Cycle Time from Ongoing Cases: A Survival Analysis-Based Approach |
| topic | Predictive process monitoring · Remaining time prediction · Survival analysis · Incomplete traces |
| url | https://bspace.buid.ac.ae/handle/1234/2928 https://link.springer.com/chapter/10.1007/978-3-030-72693-5_8 https://doi.org/10.1007/978-3-030-72693-5_8 |