FactorBase : Multi-relational model learning with SQL all the way

Qian, Zhensong

doi:10.1109/dsaa.2015.7344828

Cited by 2 publications

(1 citation statement)

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“…Similar to how Themis treats the biased samples as first class citizens, FactorBase [62] and BayesStore [70,72] do the same with graphical models and probabilistic inference. While they both utilize Bayesian networks to model data, their goal is not data debiasing.…”

Section: Related Workmentioning

confidence: 99%

Sample Debiasing in the Themis Open World Database System (Extended Version)

Orr,

Balazinska,

Suciu

2020

Preprint

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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 1 , 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 Themis 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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Section: Related Workmentioning

confidence: 99%