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BPMN Miner: Automated discovery of BPMN process models with hierarchical structure

机译：BPMN Miner：自动化发现具有分层结构的BPMN流程模型

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摘要

Existing techniques for automated discovery of process models from event logs generally produce flat process models. Thus, they fail to exploit the notion of subprocess as well as error handling and repetition constructs provided by contemporary process modeling notations, such as the Business Process Model and Notation (BPMN). This paper presents a technique, namely BPMN Miner, for automated discovery of hierarchical BPMN models containing interrupting and non-interrupting boundary events and activity markers. The technique employs approximate functional and inclusion dependency discovery techniques in order to elicit a process-subprocess hierarchy from the event log. Given this hierarchy and the projected logs associated to each node in the hierarchy, parent process and subprocess models are discovered using existing techniques for flat process model discovery. Finally, the resulting models and logs are heuristically analyzed in order to identify boundary events and markers. By employing approximate dependency discovery techniques, BPMN Miner is able to detect and filter out noise in the event log arising for example from data entry errors, missing event records or infrequent behavior. Noise is detected during the construction of the subprocess hierarchy and filtered out via heuristics at the lowest possible level of granularity in the hierarchy. A validation with one synthetic and two real-life logs shows that process models derived by the proposed technique are more accurate and less complex than those derived with flat process discovery techniques. Meanwhile, a validation on a family of synthetically generated logs shows that the technique is resilient to varying levels of noise. (c) 2015 Elsevier Ltd. All rights reserved.
机译：用于从事件日志中自动发现过程模型的现有技术通常会产生平坦的过程模型。因此，他们无法利用子流程的概念以及现代流程建模符号（例如业务流程模型和符号（BPMN））提供的错误处理和重复构造。本文介绍了一种技术，即BPMN Miner，用于自动发现包含中断和不中断边界事件以及活动标记的分层BPMN模型。该技术采用近似的功能和包含依赖性发现技术，以便从事件日志中得出过程子过程的层次结构。给定此层次结构以及与层次结构中每个节点关联的计划日志，可以使用现有的用于平面过程模型发现的技术来发现父过程和子过程模型。最后，对所得模型和日志进行启发式分析，以识别边界事件和标记。通过采用近似依赖关系发现技术，BPMN Miner能够检测和过滤出事件日志中的噪声，这些噪声例如是由于数据输入错误，事件记录丢失或不经常发生而引起的。在子流程层次结构的构建过程中会检测到噪声，并通过启发式方法在层次结构中尽可能最低的粒度级别上将其过滤掉。通过一个合成日志和两个真实日志进行的验证表明，与使用平面过程发现技术得出的过程模型相比，通过提议的技术得出的过程模型更准确，更简单。同时，对一系列人工合成的测井数据的验证表明，该技术可适应各种噪声水平。 （c）2015 Elsevier Ltd.保留所有权利。

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