A Framework for Streaming Event-Log Prediction in Business Processes
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912163728195584 |
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| author | Bollig, Benedikt Függer, Matthias Nowak, Thomas |
| author_facet | Bollig, Benedikt Függer, Matthias Nowak, Thomas |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_16032 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Framework for Streaming Event-Log Prediction in Business Processes Bollig, Benedikt Függer, Matthias Nowak, Thomas Artificial Intelligence Machine Learning 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. |
| title | A Framework for Streaming Event-Log Prediction in Business Processes |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2412.16032 |