Summary of A Framework For Streaming Event-log Prediction in Business Processes, by Benedikt Bollig et al.
A Framework for Streaming Event-Log Prediction in Business Processes
by Benedikt Bollig, Matthias Függer, Thomas Nowak
First submitted to arxiv on: 20 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel Python-based framework for event-log prediction in streaming mode has been developed, allowing predictions to be made as data is being generated by a business process. The framework facilitates the integration of various streaming algorithms, including language models such as n-grams and LSTMs, and enables the combination of these predictors using ensemble methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The framework makes it easy to predict event logs in real-time while the business process is still running. It allows for combining different prediction methods like N-gram and LSTM models, which are common language models used in natural language processing tasks. |
Keywords
» Artificial intelligence » Lstm » N gram » Natural language processing
