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Updated: Sep 10, 2025

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过程GAN:使用条件生成对抗网络生成保护隐私的时间感知过程数据
Keyi Li1, Sen Yang2, Travis M Sullivan3
1Electrical and Computer Engineering Department, Rutgers University, New Brunswick, New Jersey, USA.
概括
为了研究,ProcessGAN生成现实的,保护隐私的合成过程数据. 这可以共享复杂的事件日志数据,克服过程挖掘和医学分析的局限性.
科学领域:
- 计算机科学
- 数据科学
- 人工智能
背景情况:
- 来自事件日志的过程数据提供了对程序动态的洞察力,但由于机密性和复杂性,通常无法共享.
- 过程数据的有限可用性限制了过程采矿领域的研究和分析.
研究的目的:
- 通过引入合成过程数据生成方法来解决可共享过程数据的局限性.
- 开发一个生成的对抗网络 (ProcessGAN),能够创建保护隐私的过程数据,具有现实的活动序列和时间.
主要方法:
- ProcessGAN使用基于变压器的发电机和时间感知自我注意力区分器.
- 该模型考虑了过程持续时间和互动时间间隔,以生成现实的数据.
- 通过使用统计指标,监督模型评分和领域专家评估发现的工作流程,对五个现实数据集 (公共和私人医疗) 进行评估.
主要成果:
- 在创建具有并行路径的复杂过程中, ProcessGAN 性能优于现有的生成模型.
- 生成的合成数据准确地代表了长距离的依赖关系和真实的时间分布.
- 与真实数据相比,相关的合成背景 (例如患者人口统计) 也显示出高准确性.
结论:
- 过程GAN有效地生成可共享的合成过程数据,无法与真实数据区分.
- 这种方法提高了过程采矿的研究和分析的可行性,特别是在医疗保健等敏感领域.
- 开发的模型和源代码已公开,以促进进一步的研究.
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