Agent Miner: An Algorithm for Discovering Agent Systems from Event Data

A. Tour, Artem Polyvyanyy, A. Kalenkova, Arik Senderovich
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引用次数: 17

Abstract

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.
Agent Miner:一种从事件数据中发现Agent系统的算法
流程发现研究使用由业务流程生成并由IT系统记录的事件数据来构建描述流程的模型的方法。现有的发现算法主要关注于构建表示过程控制流的过程模型。代理系统挖掘认为业务流程通常来自自治代理的交互,并使用事件数据构建代理及其交互的模型。Agent Miner是一种从事件数据中发现Agent模型及其交互的算法,这些事件数据构成了执行生成输入数据的过程的系统。使用我们对Agent Miner的开源实现和公开可用的工业数据集进行的评估证实,我们的算法可以提供对流程参与者及其交互模式的洞察,并且经常发现比使用传统流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。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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