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Updated: Sep 10, 2025
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ProcessGAN: 条件付き生成アドバサリアルネットワークでプライバシー保護の時間認識プロセスデータを生成する
Keyi Li1, Sen Yang2, Travis M Sullivan3
1Electrical and Computer Engineering Department, Rutgers University, New Brunswick, New Jersey, USA.
ACM transactions on knowledge discovery from data
|August 25, 2025
まとめ
ProcessGANは,研究のために現実的でプライバシーを守る合成プロセスデータを生成します. これは複雑なイベントログデータを共有し,プロセスマイニングと医療分析の限界を克服します.
科学分野:
- コンピュータ科学
- データサイエンス
- 人工知能
背景:
- イベントログからのプロセスのデータは,手続きのダイナミクスを洞察できますが,機密性と複雑性のためにしばしば共有できません.
- プロセスデータの限られた可用性は,プロセスマイニングの領域での研究と分析を制限します.
研究 の 目的:
- 合成プロセスのデータ生成方法を導入することで,共有可能なプロセスのデータの制限に対処します.
- リアルなアクティビティシーケンスとタイムスタンプを備えた,プライバシーを守るプロセスのデータを作成できる,ジェネラティブ・アダサリアル・ネットワーク (ProcessGAN) を開発する.
主な方法:
- ProcessGANは,トランスフォーマーベースのジェネレーターと時間認識の自己注意区分器を使用しています.
- このモデルは,現実的なデータ生成のためのプロセス期間とインタラクティビティの時間間隔を考慮します.
- 統計的指標,監督モデルスコア,および発見されたワークフローのドメインの専門家評価を使用して,5つの現実世界のデータセット (公的および私的医療) で評価されました.
主要な成果:
- ProcessGANは,並列経路を持つ複雑なプロセスを作成する上で既存の生成モデルを上回ります.
- 生成された合成データは,長距離依存関係と本物のタイムスタンプ分布を正確に表しています.
- 関連した合成文脈 (例えば,患者の人口統計) も,本物データと比較して高精度を示した.
結論:
- ProcessGANは 共有可能な合成プロセスのデータを効果的に生成し 真のデータと区別できません
- このアプローチは,特にヘルスケアのような繊細な分野において,プロセスマイニングにおける研究と分析の実現可能性を高めます.
- 開発されたモデルとソースコードは,さらなる研究を促進するために公開されています.
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