机器学习在业务流程管理中的应用：系统文献综述
Machine learning in business process management: A systematic literature review
AI总结：
本文系统综述了机器学习在业务流程管理中的应用，提取任务、总结生命周期阶段，并提出了包含十个未来研究方向的研究议程。
摘要：
机器学习（ML）提供了基于数据创建计算机程序的算法，而无需显式编程。在业务流程管理（BPM）中，机器学习应用被用于高效地分析和改进流程。使用机器学习的三个常见示例包括：通过预测提供决策支持、发现准确的流程模型以及改进资源分配。本文组织了机器学习在业务流程管理中的知识体系。我们从不同的文献流中提取业务流程管理任务，将其归纳到流程生命周期的各个阶段下，解释机器学习如何帮助执行这些任务，并识别跨任务的机器学习实现中的技术共性。本研究是首次对机器学习在业务流程管理中如何被使用的详尽综述。我们希望它能通过帮助研究人员识别相关的初步工作，然后以聚焦的方式组合并进一步发展现有方法，为累积研究的新时代打开大门。我们的论文帮助管理者和顾问找到在业务流程管理倡议的当前项目阶段（如重新设计业务流程）中相关的机器学习应用。我们还作为综述的综合，提供了一个研究议程，该议程展开了未来研究的十个方向，包括应用新颖的机器学习概念（如联邦学习）、解决较少受关注的业务流程管理生命周期阶段（如流程识别），以及交付以最终用户为中心的机器学习应用。
英文摘要：
Machine learning (ML) provides algorithms to create computer programs based on data without explicitly programming them. In business process management (BPM), ML applications are used to analyse and improve processes efficiently. Three frequent examples of using ML are providing decision support through predictions, discovering accurate process models, and improving resource allocation. This paper organises the body of knowledge on ML in BPM. We extract BPM tasks from different literature streams, summarise them under the phases of a process`s lifecycle, explain how ML helps perform these tasks and identify technical commonalities in ML implementations across tasks. This study is the first exhaustive review of how ML has been used in BPM. We hope that it can open the door for a new era of cumulative research by helping researchers to identify relevant preliminary work and then combine and further develop existing approaches in a focused fashion. Our paper helps managers and consultants to find ML applications that are relevant in the current project phase of a BPM initiative, like redesigning a business process. We also offer - as a synthesis of our review - a research agenda that spreads ten avenues for future research, including applying novel ML concepts like federated learning, addressing less regarded BPM lifecycle phases like process identification, and delivering ML applications with a focus on end-users.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
