Themis开放世界数据库系统（扩展版）中的去偏置示例
Sample Debiasing in the Themis Open World Database System (Extended Version)
摘要：
开放世界数据库管理系统假设数据库中没有的元组仍然存在，并且正在成为一个越来越重要的研究领域。我们提出了Themis，第一个开放世界的数据库，自动重新平衡任意偏置的样本，近似回答查询，如果他们在整个人口发出。我们利用先验的人口聚集信息，开发和结合两种不同的方法自动去偏：样本重新加权和贝叶斯网络概率建模。我们建立了一个原型的Themis和证明，Themis实现更高的查询精度比默认的AQP方法，一种替代的样本重新加权技术，和各种贝叶斯网络模型，同时保持交互式查询响应时间。我们还表明，\name是强大的样本和人口之间的支持，一个关键的用例时，使用社交媒体样本的差异。
英文摘要：
Open world database management systems assume tuples not in the database still exist and are becoming an increasingly important area of research. We present Themis, the first open world database that automatically rebalances arbitrarily biased samples to approximately answer queries as if they were issued over the entire population. We leverage apriori population aggregate information to develop and combine two different approaches for automatic debiasing: sample reweighting and Bayesian network probabilistic modeling. We build a prototype of Themis and demonstrate that Themis achieves higher query accuracy than the default AQP approach, an alternative sample reweighting technique, and a variety of Bayesian network models while maintaining interactive query response times. We also show that \name is robust to differences in the support between the sample and population, a key use case when using social media samples.
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