In the pursuit of ecological validity, current business process simulation methods are calibrated to data from existing processes. This is important for realistic what-if analysis in the context of these processes. However, this is not always the “right tool for the job.” To test hypotheses in the area of predictive process monitoring, it can be more helpful to simulate event-log data from a theoretical process, where all aspects can be manipulated. One example is when assessing the influence of process complexity or variability on the performance of a new prediction method. In this case, the ability to include control variables and systematically change process characteristics is a key to fully understanding their influence. Calibrating a simulation model from observed data alone can in these cases be limiting. This paper proposes a simulation framework, Synthetic Business Process Simulation (SynBPS), a Python library for the generation of event-log data from synthetic processes. Aspects such as process complexity, stability, trace distribution, duration distribution, and case arrivals can be fully controlled by the user. The overall architecture is described in detail, and a demonstration of the framework is presented.

中文翻译：

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SynBPS：用于生成事件日志数据的参数化模拟框架

为了追求生态有效性，当前的业务流程模拟方法根据现有流程的数据进行校准。这对于这些流程背景下的现实假设分析非常重要。然而，这并不总是“适合工作的工具”。为了测试预测过程监控领域的假设，从理论过程模拟事件日志数据可能会更有帮助，其中所有方面都可以操纵。一个例子是评估过程复杂性或可变性对新预测方法性能的影响。在这种情况下，纳入控制变量并系统地改变过程特征的能力是充分理解其影响的关键。在这些情况下，仅根据观测数据校准模拟模型可能会受到限制。本文提出了一个模拟框架，即综合业务流程模拟 (SynBPS)，这是一个用于从综合流程生成事件日志数据的 Python 库。流程复杂性、稳定性、轨迹分布、持续时间分布和案例到达等方面可以完全由用户控制。详细描述了总体架构，并给出了框架的演示。