提出可实时预测业务流程日志的框架，支持多种模型集成。
A Framework for Streaming Event-Log Prediction in Business Processes
- 基于Python构建流式预测框架，支持n-gram、LSTM等算法接入。
- 集成模型在流式场景下表现超越LSTM，尤其早期阶段更稳定。
- 适合需要实时预测的业务流程监控与优化场景。
我们提出一个基于Python的流式事件日志预测框架，可在业务流程生成数据的同时进行实时预测。该框架支持轻松集成n-gram、LSTM等流式算法，并可通过集成方法组合多个预测器。我们在多个知名流程挖掘数据集上进行了实验，对比了经典批处理模式与流式模式的表现。尽管批量模式中LSTM通常表现最佳，但n-gram模型的准确率常接近其水平；而通过集成方法组合基础模型，甚至能超越LSTM。在流式模式下，这一优势更为明显：LSTM在预测初期准确率普遍偏低，而基础模型则能立即给出合理预测。
原文摘要 · Abstract (English)
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.
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