The problem of process discovery in process mining studies ways to construct process models that encode business processes that induced event data recorded by IT systems. Most existing discovery algorithms are concerned with constructing models that represent the control flow of the processes. Agent system mining argues that business processes often emerge from interactions of autonomous agents and uses event data to construct models of the agents and their interactions. This paradigm shift from the control flow to agent system discovery proves beneficial when interacting agents have produced the underlying data. This paper presents an algorithm, called Agent Miner, for discovering models of agents and their interactions that compose the system that has generated the business processes recorded in the input event data. The conducted evaluation using our open-source implementation of Agent Miner over publicly available industrial datasets confirms that the approach can unveil insights into the process participants and their interaction patterns and often discovers models that describe the data more accurately in terms of precision and recall and are smaller in size than the corresponding models discovered using conventional discovery algorithms.
翻译:采矿过程中的流程发现问题研究如何构建过程模型,将业务流程编码成由信息技术系统记录的事件数据。大多数现有的发现算法都涉及构建代表过程控制流的模型。代理系统采矿认为,业务流程通常产生于自主代理的相互作用,并利用事件数据构建代理人及其相互作用的模型。这种范式从控制流程向代理系统发现模式的转变证明在互动代理方生成基本数据时是有益的。本文介绍了一种算法,称为Miner代理,用于发现代理人及其相互作用的模型,这些模型组成了生成输入事件数据所记录的业务流程的系统。利用我们对采矿代理商的公开来源实施对公开的工业数据集进行的评估证实,该方法可以揭示对过程参与者及其互动模式的洞见,并经常发现在精确和回忆方面更准确地描述数据的模式,其规模小于使用常规发现算法发现的相应模型。