Business process simulation is a versatile technique to estimate the performance of a process under multiple scenarios. This, in turn, allows analysts to compare alternative options to improve a business process. A common roadblock for business process simulation is that constructing accurate simulation models is cumbersome and error-prone. Modern information systems store detailed execution logs of the business processes they support. Previous work has shown that these logs can be used to discover simulation models. However, existing methods for log-based discovery of simulation models do not seek to optimize the accuracy of the resulting models. Instead they leave it to the user to manually tune the simulation model to achieve the desired level of accuracy. This article presents an accuracy-optimized method to discover business process simulation models from execution logs. The method decomposes the problem into a series of steps with associated configuration parameters. A hyper-parameter optimization method is used to search through the space of possible configurations so as to maximize the similarity between the behavior of the simulation model and the behavior observed in the log. The method has been implemented as a tool and evaluated using logs from different domains.

业务流程模拟是一种通用的技术，可以在多种情况下估计流程的性能。反过来，这使分析师可以比较其他选择，以改善业务流程。业务流程仿真的一个常见障碍是构建准确的仿真模型既麻烦又容易出错。现代信息系统存储其支持的业务流程的详细执行日志。先前的工作表明，这些日志可用于发现仿真模型。但是，用于基于日志的仿真模型发现的现有方法并未寻求优化所得模型的准确性。相反，他们将其留给用户手动调整仿真模型以达到所需的精度水平。本文提出了一种精度优化的方法，可以从执行日志中发现业务流程仿真模型。该方法将问题分解为带有相关配置参数的一系列步骤。使用超参数优化方法搜索可能的配置空间，以使模拟模型的行为与日志中观察到的行为之间的相似性最大化。该方法已实现为工具，并使用来自不同域的日志进行了评估。使用超参数优化方法搜索可能的配置空间，以使模拟模型的行为与日志中观察到的行为之间的相似性最大化。该方法已实现为工具，并使用来自不同域的日志进行了评估。使用超参数优化方法搜索可能的配置空间，以使模拟模型的行为与日志中观察到的行为之间的相似性最大化。该方法已实现为工具，并使用来自不同域的日志进行了评估。