English(EN) Graph Grounded Cross Attention Transformer Neural Network for Structurally Constrained Full Event Sequence Generation in Predictive Process Monitoring

新的GGATN模型生成完整的事件序列用于预测性过程监控

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-18 04:00

研究人员开发了一种名为图基础交叉注意力Transformer神经网络（GGATN）的新型神经网络架构，用于预测性过程监控。该模型旨在生成完整的事件序列，同时确保结构可行性、时间顺序和属性一致性。与之前专注于单个预测任务的方法不同，GGATN一次性生成活动、时间戳和属性，然后进行约束解码以确保有效路径。在基准事件日志上的实验表明，与LLM基线相比，GGATN在序列相似性和控制流准确性方面表现更优，没有出现虚构的活动或属性不一致的情况。 AI

影响通过生成更真实的事件序列，这种新模型可以提高预测性过程监控系统的准确性和可靠性。

排序理由该集群包含一篇详细介绍用于特定AI任务的新神经网络架构的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.AI TIER_1 English(EN) · Fang Wang, Ernesto Damiani · 2026-06-18 04:00

用于预测性流程监控中结构约束的完整事件序列生成的图基交叉注意力Transformer神经网络

arXiv:2606.18726v1 Announce Type: cross Abstract: Structurally constrained event sequence generation remains challenging because generated paths must preserve transition feasibility, temporal order, termination, and attribute consistency. In predictive process monitoring (PPM), t…

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用于预测性流程监控中结构约束的完整事件序列生成的图基交叉注意力Transformer神经网络

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