Process mining techniques including process discovery, conformance checking, and process enhancement provide extensive knowledge about processes. Discovering running processes and deviations as well as detecting performance problems and bottlenecks are well-supported by process mining tools. However, all the provided techniques represent the past/current state of the process. The improvement in a process requires insights into the future states of the process w.r.t. the possible actions/changes. In this paper, we present a new tool that enables process owners to extract all the process aspects from their historical event data automatically, change these aspects, and re-run the process automatically using an interface. The combination of process mining and simulation techniques provides new evidence-driven ways to explore "what-if" questions. Therefore, assessing the effects of changes in process improvement is also possible. Our Python-based web-application provides a complete interactive platform to improve the flow of activities, i.e., process tree, along with possible changes in all the derived activity, resource, and process parameters. These parameters are derived directly from an event log without user-background knowledge.

中文翻译：

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SIMPT：使用时间感知过程树的交互式模拟进行过程改进

流程挖掘技术包括流程发现、一致性检查和流程增强，提供了有关流程的广泛知识。流程挖掘工具很好地支持发现正在运行的流程和偏差以及检测性能问题和瓶颈。但是，所有提供的技术都代表了过程的过去/当前状态。流程的改进需要洞察流程的未来状态，以及可能的操作/更改。在本文中，我们提出了一种新工具，使流程所有者能够从其历史事件数据中自动提取所有流程方面，更改这些方面，并使用界面自动重新运行流程。流程挖掘和模拟技术的结合提供了新的证据驱动方式来探索“假设” 问题。因此，评估变更对过程改进的影响也是可能的。我们基于 Python 的网络应用程序提供了一个完整的交互平台来改进活动的流程，即流程树，以及所有派生活动、资源和流程参数的可能变化。这些参数直接来自事件日志，无需用户背景知识。