论文标题
代理商矿工：一种用于从事件数据发现代理系统的算法
Agent Miner: An Algorithm for Discovering Agent Systems from Event Data
论文作者
论文摘要
过程发现研究使用业务流程生成的事件数据并由IT系统记录的方法来构建描述流程的模型。现有的发现算法主要与构建代表流程控制流的过程模型有关。代理系统采矿认为，业务流程通常从自主代理的交互中出现，并使用事件数据来构建代理的模型及其交互。本文介绍并评估了代理商矿工，这是一种用于发现代理模型及其相互作用的算法，这些算法及其相互作用构成了已经执行生成输入数据的过程的系统的事件数据。通过我们对代理商矿工和公开可用工业数据集的开源实施进行的评估证实，我们的算法可以提供对流程参与者及其交互模式的见解，并且经常发现与使用常规过程发现算法发现的业务模型相比，与使用常规流程发现算法发现的业务模型相比，这些模型更忠实。
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.
