CoSMo：一个用于实例化条件化流程模拟模型的框架
CoSMo: a Framework to Instantiate Conditioned Process Simulation Models
AI总结：
本文提出CoSMo框架，一种基于循环神经架构的条件化流程模拟模型，通过整合声明式规则到学习阶段，支持在约束下模拟事件日志，并兼顾控制流与数据流，填补了深度学习在假设分析中的信息整合空白。
摘要：
流程模拟因其能够评估业务流程变更相关的潜在性能改进和风险而受到越来越多的关注。现有文献提出了多种技术，这些技术通常基于从事件日志数据中发现的流程模型，或基于深度学习算法构建。这些技术各有其特定的优势和局限性。传统的数据驱动方法提供了更高的可解释性，而基于深度学习的方法则擅长在大型事件日志上泛化变更。然而，深度学习的实际应用面临着与随机性管理和为假设分析整合信息相关的挑战。本文提出了一种新颖的循环神经架构，旨在基于用户约束或任何其他性质的先验知识发现条件化流程模拟模型（CoSMo）。该架构通过将基于声明式规则的信息整合到学习阶段，以尝试填补将信息纳入深度学习模型以执行假设分析的空白，从而促进模拟符合特定约束的事件日志。实验验证展示了CoSMo在遵循预定义声明条件的同时模拟事件日志的有效性，并强调了控制流和数据流两个视角。
英文摘要：
Process simulation is gaining attention for its ability to assess potential performance improvements and risks associated with business process changes. The existing literature presents various techniques, generally grounded in process models discovered from event log data or built upon deep learning algorithms. These techniques have specific strengths and limitations. Traditional data-driven approaches offer increased interpretability, while deep learning-based excel at generalizing changes across large event logs. However, the practical application of deep learning faces challenges related to managing stochasticity and integrating information for what-if analysis. This paper introduces a novel recurrent neural architecture tailored to discover COnditioned process Simulation MOdels (CoSMo) based on user-based constraints or any other nature of a-priori knowledge. This architecture facilitates the simulation of event logs that adhere to specific constraints by incorporating declarative-based rules into the learning phase as an attempt to fill the gap of incorporating information into deep learning models to perform what-if analysis. Experimental validation illustrates CoSMo's efficacy in simulating event logs while adhering to predefined declarative conditions, emphasizing both control-flow and data-flow perspectives.
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