2020/02/23 by Laurel Orr, Orr, Laurel, Magda Balazinska +3
Computer Science · #Bayesian Modeling and Causal Inference #Data Management and Algorithms #Data Stream Mining Techniques #Databases (cs.DB) #FOS: Computer and information sciences #cs.DB
paper · pdf · doi:10.48550/arxiv.2002.09799
SIGMOD 2020
openalex publication_date 2020/02/23 · arxiv created 2020/02/29 · arxiv updated 2020/03/03 · openalex created_date 2020/03/06 · openalex updated_date 2026/07/28
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.