Infrequent behaviors of business process refer to behaviors that occur in very exceptional cases, and their occurrence frequency is low as their required conditions are rarely fulfilled. Hence, a strong coupling relationship between infrequent behavior and data flow exists. Furthermore, some infrequent behaviors may reveal very important information about the process. Thus, not all infrequent behaviors should be disregarded as noise, and identifying infrequent but correct behaviors in the event log is vital to process mining from the perspective of data flow. Existing process mining approaches construct a process model from frequent behaviors in the event log, mostly concentrating on control flow only, without considering infrequent behavior and data flow information. In this paper, we focus on data flow to extract infrequent but correct behaviors from logs. For an infrequent trace, frequent patterns and interactive behavior profiles are combined to find out which part of the behavior in the trace occurs in low frequency. And, conditional dependency probability is used to analyze the influence strength of the data flow information on infrequent behavior. An approach for identifying effective infrequent behaviors based on the frequent pattern under data awareness is proposed correspondingly. Subsequently, an optimization approach for mining of process models with infrequent behaviors integrating data flow and control flow is also presented. The experiments on synthetic and real-life event logs show that the proposed approach can distinguish effective infrequent behaviors from noise compared with others. The proposed approaches greatly improve the fitness of the mined process model without significantly decreasing its precision.

中文翻译：

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集成数据流和控制流的不频繁行为的过程模型的优化挖掘方法

业务流程的罕见行为是指在非常特殊的情况下发生的行为，并且由于很少满足其所需条件，因此它们的发生频率较低。因此，不频繁的行为和数据流之间存在很强的耦合关系。此外，一些不常见的行为可能会揭示有关该过程的非常重要的信息。因此，并非所有不常见的行为都不应被视为噪音，并且从数据流的角度识别事件日志中的不常见但正确的行为对于处理挖掘至关重要。现有的流程挖掘方法是根据事件日志中的频繁行为构建流程模型的，大多数情况下仅关注控制流，而没有考虑不频繁行为和数据流信息。在本文中，我们专注于数据流以从日志中提取不常见但正确的行为。对于不频繁的跟踪，将频繁的模式和交互行为配置文件结合起来，以找出跟踪中行为的哪一部分以低频发生。并且，使用条件依赖性概率来分析数据流信息对不频繁行为的影响强度。相应地提出了一种在数据感知下基于频繁模式识别有效不频繁行为的方法。随后，还提出了一种用于优化流程模型的优化方法，该流程模型具有不频繁的行为，集成了数据流和控制流。对合成事件日志和现实事件日志的实验表明，与其他方法相比，该方法可以将有效的不频繁行为与噪声区分开。