让生成的表格数据更贴近真实查询负载，提升电商压力测试效果。
Synthesizing Tabular Data Using Selectivity Enhanced Generative Adversarial Networks
- 引入查询选择性约束，使生成数据更符合真实数据库查询模式。
- 在5个真实数据集上，选择性估计准确率提升20%，机器学习性能提高6%。
- 适合需要高保真数据模拟的电商平台、数据库系统研发者使用。
电商在黑五等大型促销活动期间交易量激增，使用合成数据进行压力测试对资源规划至关重要。现有基于生成对抗网络（GAN）的表格数据生成方法虽能保障隐私与机器学习可用性，却忽视了生成数据处理的计算开销，难以用于真实场景的压力测试。本文提出一种新型GAN方法，引入查询选择性约束这一数据库事务处理的关键因素。通过集成预训练深度神经网络，确保真实数据与合成数据间的选择性一致性。在五个真实数据集上的实验表明，该方法优于三种最先进的GAN模型和一个变分自编码器（VAE）模型，选择性估计准确率最高提升20个百分点，机器学习任务性能提升最高达6个百分点。
原文摘要 · Abstract (English)
As E-commerce platforms face surging transactions during major shopping events like Black Friday, stress testing with synthesized data is crucial for resource planning. Most recent studies use Generative Adversarial Networks (GANs) to generate tabular data while ensuring privacy and machine learning utility. However, these methods overlook the computational demands of processing GAN-generated data, making them unsuitable for E-commerce stress testing. This thesis introduces a novel GAN-based approach incorporating query selectivity constraints, a key factor in database transaction processing. We integrate a pre-trained deep neural network to maintain selectivity consistency between real and synthetic data. Our method, tested on five real-world datasets, outperforms three state-of-the-art GANs and a VAE model, improving selectivity estimation accuracy by up to 20pct and machine learning utility by up to 6 pct.
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