LAPRAS uses predictions to answer likely queries with the offline Matrix Mechanism and paces residual budget for unpredicted queries via unbiased stopping-time estimation from the first few unexpected arrivals, achieving near-offline utility when overlap is high.
Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data , pages =
3 Pith papers cite this work. Polarity classification is still indexing.
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Incorporating Deep Learning Design in Database Queries
RelaNN associates tuples with learnable embeddings and lifts relational queries to jointly process data and embeddings, enabling declarative implementation of graph neural networks inside database systems.
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