发表机构
University of Oxford; Peng Cheng Laboratory; University of Glasgow; Fudan University; Squirrel Ai Learning（牛津大学; 鹏城实验室; 格拉斯哥大学; 复旦大学; 松鼠AI智适应）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该论文提出事件到行动流程挖掘议程，将事件日志转化为受治理行动，通过四种可挖掘工件支持智能体决策中的执行、推迟、询问和拒绝。
AI 中文摘要
流程挖掘长期以来将事件日志转化为流程知识：发现的模型、一致性证据、瓶颈诊断和运行时预测。智能体AI改变了目标。流程感知的智能体不仅会询问发生了什么，还会根据现有证据、隐私预算、组织权限和下游风险，询问是否应采取拟议的行动。这篇BlueSky论文提出了事件到行动流程挖掘：一个将异构运营事件数据转化为受治理行动的流程挖掘议程。目标不是另一个仪表板、通用企业模拟器或日志上的语言接口。我们认为社区需要四种可挖掘的工件：事件对象表示、行动证据包、治理契约，以及将执行、推迟、询问和拒绝作为有效输出的基准。这一议程是及时的，因为智能体业务流程管理（BPM）、LLM辅助流程挖掘、对象中心事件标准、因果流程监控和隐私保护学习正在分别成熟。将它们整合起来，定义了流程挖掘内部的一个数据挖掘目标：挖掘记录在案的组织行为以用于负责任的行动，而不仅仅是回顾性洞察。
英文摘要
Process mining has long turned event logs into process knowledge: discovered models, conformance evidence, bottleneck diagnoses, and runtime predictions. Agentic AI changes the target. Process-aware agents will not only ask what happened. They will ask whether a proposed action should be taken, given the available evidence, privacy budget, organizational authority, and downstream risk. This BlueSky paper proposes event-to-action process mining: a process-mining agenda for transforming heterogeneous operational event data into governed action. The goal is not another dashboard, a generic enterprise simulator, or a language interface over logs. We argue that the community needs four mineable artifacts: event-object representations, action evidence packages, governance contracts, and benchmarks where act, defer, ask, and refuse are all valid outputs. This agenda is timely because agentic business process management (BPM), LLM-assisted process mining, object-centric event standards, causal process monitoring, and privacy-preserving learning are maturing separately. Bringing them together defines a data-mining target inside process mining: mining logged organizational behavior for accountable action, not only retrospective insight.
CommentsAccepted by ICDM 2026
