Discovering Block-Structured Process Models from Event Logs Containing Infrequent Behaviour
Discovering Block-Structured Process Models from Event Logs Containing Infrequent Behaviour
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DOI：
10.1007/978-3-319-06257-0_6
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发表时间：
2013-08
期刊：
影响因子：
6
通讯作者：
S. Leemans;Dirk Fahland;Wil M.P. van der Aalst
中科院分区：
文献类型：
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作者：
S. Leemans;Dirk Fahland;Wil M.P. van der Aalst
给定一个描述观察到的行为的事件日志，流程发现的目标是找到一个“最好”描述这种行为的流程模型。已经提出了各种各样的进程发现算法。然而，没有一个现有的算法在所有情况下都能返回一个可靠的模型（没有死锁和其他异常），能够很好地处理不常见的行为并快速完成。我们提出了一种技术，能够科普罕见的行为和大型事件日志，同时确保健全。该技术已在ProM中实现，我们将该技术与现有的方法在质量和性能方面进行比较。
Given an event log describing observed behaviour, process discovery aims to find a process model that ‘best’ describes this behaviour. A large variety of process discovery algorithms has been proposed. However, no existing algorithm returns a sound model in all cases (free of deadlocks and other anomalies), handles infrequent behaviour well and finishes quickly. We present a technique able to cope with infrequent behaviour and large event logs, while ensuring soundness. The technique has been implemented in ProM and we compare the technique with existing approaches in terms of quality and performance.