Process discovery studies ways to use event data generated by business processes and recorded by IT systems to construct models that describe the processes. Existing discovery algorithms are predominantly concerned with constructing process 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 paper presents and evaluates Agent Miner, an algorithm for discovering models of agents and their interactions from event data composing the system that has executed the processes which generated the input data. The conducted evaluation using our open-source implementation of Agent Miner and publicly available industrial datasets confirms that our algorithm can provide insights into the process participants and their interaction patterns and often discovers models that describe the business processes more faithfully than process models discovered using conventional process discovery algorithms.

翻译：过程发现研究利用业务流程生成并由IT系统记录的事件数据，构建描述流程的模型的方法。现有的发现算法主要侧重于构建表示流程控制流的流程模型。主体系统挖掘认为，业务流程通常源于自主主体的交互，并利用事件数据构建主体及其交互的模型。本文介绍并评估了主体挖掘器（Agent Miner），这是一种从事件数据中发现主体及其交互模型的算法，这些事件数据构成了执行生成输入数据的流程的系统。使用我们开源实现的主体挖掘器及公开可用的工业数据集进行的评估证实，我们的算法能够深入洞察流程参与者及其交互模式，并且通常能发现比传统过程发现算法发现的流程模型更忠实地描述业务流程的模型。