Process simulation is an analysis tool in process mining that allows users to measure the impact of changes, prevent losses, and update the process without risks or costs. In the literature, several process simulation techniques are available and they are usually built upon process models discovered from a given event log or learned via deep learning. Each group of approaches has its own strengths and limitations. The former is usually restricted to the control-flow but it is more interpretable, whereas the latter is not interpretable by nature but has a greater generalization capability on large event logs. Despite the great performance achieved by deep learning approaches, they are still not suitable to be applied to real scenarios and generate value for users. This issue is mainly due to fact their stochasticity is hard to control. To address this problem, we propose the CoSMo framework for implementing process simulation models fully based on deep learning. This framework enables simulating event logs that satisfy a constraint by conditioning the learning phase of a deep neural network. Throughout experiments, the simulation is validated from both control-flow and data-flow perspectives, demonstrating the proposed framework's capability of simulating cases while satisfying imposed conditions.

翻译：流程仿真是一种流程挖掘中的分析工具，使得用户能够评估变更的影响、预防损失并在无风险或成本的情况下更新流程。文献中已有多种流程仿真技术，它们通常基于从给定事件日志中发现或通过深度学习习得的流程模型构建。两类方法各有优劣：前者通常局限于控制流但更具可解释性，后者虽本质不可解释但在大规模事件日志上拥有更强的泛化能力。尽管深度学习方法性能优异，仍不适用于实际场景并难以为用户创造价值，其主要原因在于其随机性难以控制。为解决此问题，我们提出了CoSMo框架，用于实现完全基于深度学习的流程仿真模型。该框架通过条件化深度神经网络的训练阶段，实现满足约束条件的事件日志仿真。通过实验，我们从控制流与数据流两个维度验证了仿真效果，证明了所提框架在满足施加条件的同时生成案例的能力。