论文标题
分类器和簇网络的预测过程挖掘：PEDF模型
Predictive process mining by network of classifiers and clusterers: the PEDF model
论文作者
论文摘要
在这项研究中，提出了一个模型来从事件日志中学习并预测系统的未来事件。拟议的PEDF模型根据事件的序列，持续时间和额外功能学习。 PEDF模型是由由标准簇和分类器组成的网络构建的，它具有很高的灵活性来迭代更新模型。该模型需要从日志文件中提取两组数据，即过渡差异和累积特征。该模型具有一层内存，这意味着每个过渡都取决于当前事件和上一个事件。为了评估所提出模型的性能，它可以与复发性的神经网络和顺序预测模型进行比较，并且表现优于它们。由于事件日志预测模型缺少性能度量，因此提出了三个措施。
In this research, a model is proposed to learn from event log and predict future events of a system. The proposed PEDF model learns based on events' sequences, durations, and extra features. The PEDF model is built by a network made of standard clusterers and classifiers, and it has high flexibility to update the model iteratively. The model requires to extract two sets of data from log files i.e., transition differences, and cumulative features. The model has one layer of memory which means that each transition is dependent on both the current event and the previous event. To evaluate the performance of the proposed model, it is compared to the Recurrent Neural Network and Sequential Prediction models, and it outperforms them. Since there is missing performance measure for event log prediction models, three measures are proposed.
