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Discovering high-level BPMN process models from event data

机译：从事件数据中发现高级BPMN流程模型

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Purpose The purpose of this paper is to demonstrate that process mining techniques can help to discover process models from event logs, using conventional high-level process modeling languages, such as Business Process Model and Notation (BPMN), leveraging their representational bias. Design/methodology/approach The integrated discovery approach presented in this work is aimed to mine: control, data and resource perspectives within one process diagram, and, if possible, construct a hierarchy of subprocesses improving the model readability. The proposed approach is defined as a sequence of steps, performed to discover a model, containing various perspectives and presenting a holistic view of a process. This approach was implemented within an open-source process mining framework called ProM and proved its applicability for the analysis of real-life event logs. Findings This paper shows that the proposed integrated approach can be applied to real-life event logs of information systems from different domains. The multi-perspective process diagrams obtained within the approach are of good quality and better than models discovered using a technique that does not consider hierarchy. Moreover, due to the decomposition methods applied, the proposed approach can deal with large event logs, which cannot be handled by methods that do not use decomposition. Originality/value The paper consolidates various process mining techniques, which were never integrated before and presents a novel approach for the discovery of multi-perspective hierarchical BPMN models. This approach bridges the gap between well-known process mining techniques and a wide range of BPMN-complaint tools.
机译：目的本文的目的是说明流程挖掘技术可以帮助使用常规的高级过程建模语言从事件日志发现流程模型，例如业务流程模型和符号（BPMN），利用其代表性偏见。设计/方法/方法本作品中呈现的集成发现方法旨在挖掘：控制，数据和资源透视图在一个过程图中，如果可能的话，构造子进程的层次结构，提高了模型可读性。所提出的方法被定义为执行以发现模型，包含各种观点并呈现过程的整体视图的步骤序列。这种方法是在一个名为PROM的开源过程挖掘框架内实施，并证明了其对现实生活事件日志分析的适用性。结果本文展示，所提出的综合方法可以应用于来自不同域的信息系统的现实事件日志。在该方法中获得的多透视过程图具有良好的质量，并且优于使用不考虑层次结构的技术的模型更好。此外，由于应用了分解方法，所提出的方法可以处理大型事件日志，该日志不能通过不使用分解的方法处理。原创性/值本文整合了各种过程挖掘技术，这些技术从未集成在一起，并提出了一种用于发现多视角分层BPMN模型的新方法。这种方法桥接着名的过程挖掘技术与广泛的BPMN抱怨工具之间的差距。

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