arXivDaily arXiv每日学术速递 周一至周五更新
arXiv周末暂无论文更新,休息一下吧,周末愉快~~
arXiv 2401.16263cs.FL
收藏

Collaboration Miner:发现协作Petri网(扩展版)

Collaboration Miner: Discovering Collaboration Petri Nets (Extended Version)

Janik-Vasily Benzin, Stefanie Rinderle-Ma

AI总结:

针对协作流程缺乏标准挖掘模型的问题,本研究结合协作Petri网提出Collaboration Miner方法,可发现多类协作概念,在26个事件日志上验证了其低表示偏差、高拟合精度的优势。

摘要:

现有大多数流程发现技术旨在挖掘流程编排的模型,这类模型表示单个业务流程内案例的行为。协作流程发现技术则挖掘协作流程的模型,这类模型表示多个通过组织、智能体、服务等协作概念交互的流程编排中协作案例的行为。虽然工作流网主要用于挖掘流程编排,但协作流程尚缺乏标准模型。因此,本研究依托新提出的协作Petri网,并证明结合新提出的Collaboration Miner(CM,协作挖掘器)后,其产生的表示偏差低于现有模型。此外,CM能够发现异构的协作概念和类型,例如资源共享与消息交换,从而生成拟合度高且精确的协作Petri网。基于文献中的26个人工及真实事件日志的评估表明,CM实现了其设计目标:无需对概念和类型做假设,且能生成拟合且精确的模型。

英文摘要:

Most existing process discovery techniques aim to mine models of process orchestrations that represent behavior of cases within one business process. Collaboration process discovery techniques mine models of collaboration processes that represent behavior of collaborating cases within multiple process orchestrations that interact via collaboration concepts such as organizations, agents, and services. While workflow nets are mostly mined for process orchestrations, a standard model for collaboration processes is missing. Hence, in this work, we rely on the newly proposed collaboration Petri nets and show that in combination with the newly proposed Collaboration Miner (CM), the resulting representational bias is lower than for existing models. Moreover, CM can discover heterogeneous collaboration concepts and types such as resource sharing and message exchange, resulting in fitting and precise collaboration Petri nets. The evaluation shows that CM achieves its design goals: no assumptions on concepts and types as well as fitting and precise models, based on 26 artificial and real-world event logs from literature.

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。

↑