Report
A Framework for Streaming Event-Log Prediction in Business Processes
| العنوان: | A Framework for Streaming Event-Log Prediction in Business Processes |
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| المؤلفون: | Bollig, Benedikt, Függer, Matthias, Nowak, Thomas |
| المساهمون: | Laboratoire Méthodes Formelles (LMF), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Ecole Normale Supérieure Paris-Saclay (ENS Paris Saclay), Institut universitaire de France (IUF), Ministère de l'Education nationale, de l’Enseignement supérieur et de la Recherche (M.E.N.E.S.R.), ANR-21-CE48-0003,DREAMY,Algorithmes distribués pour les systèmes microbiologiques(2021), ANR-23-CE45-0013,COSTXPRESS,Modèles quantitatifs des coûts d'expression des circuits génétiques synthétiques(2023), ANR-23-PEIA-0006,SAIF,Safe AI through Formal Methods(2023) |
| المصدر: | https://hal.science/hal-04866045 ; 2025. |
| بيانات النشر: | CCSD |
| سنة النشر: | 2025 |
| مصطلحات موضوعية: | Artificial Intelligence (cs.AI), Machine Learning (cs.LG), event-log prediction n-gram LSTM ensemble methods streaming data batch processing, event-log prediction, n-gram, LSTM, ensemble methods, streaming data, batch processing, [INFO]Computer Science [cs] |
| الوصف: | We present a Python-based framework for event-log prediction in streaming mode, enabling predictions while data is being generated by a business process. The framework allows for easy integration of streaming algorithms, including language models like n-grams and LSTMs, and for combining these predictors using ensemble methods. Using our framework, we conducted experiments on various well-known process-mining data sets and compared classical batch with streaming mode. Though, in batch mode, LSTMs generally achieve the best performance, there is often an n-gram whose accuracy comes very close. Combining basic models in ensemble methods can even outperform LSTMs. The value of basic models with respect to LSTMs becomes even more apparent in streaming mode, where LSTMs generally lack accuracy in the early stages of a prediction run, while basic methods make sensible predictions immediately. |
| نوع الوثيقة: | report |
| اللغة: | English |
| DOI: | 10.48550/arXiv.2412.16032 |
| الاتاحة: | https://hal.science/hal-04866045 https://hal.science/hal-04866045v1/document https://hal.science/hal-04866045v1/file/Streaming_Event_Log_Prediction.pdf https://doi.org/10.48550/arXiv.2412.16032 |
| Rights: | info:eu-repo/semantics/OpenAccess |
| رقم الانضمام: | edsbas.37DCD36D |
| قاعدة البيانات: | BASE |
| DOI: | 10.48550/arXiv.2412.16032 |
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