A characteristic of existing predictive process monitoring techniques is to first construct a predictive model based on past process executions, and then use it to predict the future of new ongoing cases, without the possibility of updating it with new cases when they complete their execution. This can make predictive process monitoring too rigid to deal with the variability of processes working in real environments that continuously evolve and/or exhibit new variant behaviors over time. As a solution to this problem, we propose the use of algorithms that allow the incremental construction of the predictive model. These incremental learning algorithms update the model whenever new cases become available so that the predictive model evolves over time to fit the current circumstances. The algorithms have been implemented using different case encoding strategies and evaluated on a number of real and synthetic datasets. The results provide a first evidence of the potential of incremental learning strategies for predicting process monitoring in real environments, and of the impact of different case encoding strategies in this setting.

翻译：现有预测流程监控技术的一个特点是，首先基于历史流程执行记录构建预测模型，然后利用该模型预测新进行为的未来走向，但无法在新案例完成执行后以新案例更新模型。这可能导致预测流程监控过于僵化，难以应对在持续演化或随时间展现新变体行为的真实环境中流程的可变性。为解决此问题，我们提出采用增量式构建预测模型的算法。这些增量式学习算法在新案例可用时即时更新模型，使预测模型随时间演化以适应当前环境。我们采用不同的案例编码策略实现了这些算法，并在多个真实与合成数据集上进行了评估。实验结果初步证明了增量式学习策略在真实环境中预测流程监控的潜力，以及不同案例编码策略在此背景下的影响。