将事件日志转化为可问责的行动建议，推动流程智能向自主决策演进。
From Event Logs to Governed Action: A BlueSky Agenda for Agentic Process Mining
- 从日志中提取对象化表示与行动证据包，支撑智能决策
- 提出治理合约与四类输出（执行/推迟/询问/拒绝）的评估基准
- 适合关注企业代理系统、隐私合规与可解释决策的研究者
传统流程挖掘将事件日志转化为流程模型、合规性证据、瓶颈诊断和运行时预测。而智能体式AI改变了目标：流程感知智能体不仅要问‘发生了什么’，更要基于现有证据、隐私预算、组织权限和下游风险，判断‘是否应采取某项行动’。本文提出‘事件到行动的流程挖掘’这一远景蓝图，旨在将异构运营数据转化为受控的行动建议。目标不是新的仪表盘、通用企业模拟器或日志语言接口。我们主张社区需构建四大可挖掘资产：事件-对象表征、行动证据包、治理合约，以及包含‘执行、推迟、询问、拒绝’等多元输出的评估基准。该议程恰逢其时，因智能体式流程管理、大模型辅助流程挖掘、对象中心事件标准、因果流程监控及隐私保护学习正各自成熟。整合这些进展，定义了流程挖掘内部的新数据挖掘目标：从日志中挖掘可问责的行动，而非仅回溯性洞察。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。