84 papers
This paper introduces RDB-PFN, the first relational foundation model trained exclusively on over 2 million synthetic databases generated by a Relational Prior Generator, which enables strong few-shot in-context learning on real-world relational tasks despite the scarcity of private high-quality data.
This paper addresses the challenges of integrating AI directly into database engines (AIxDB) to overcome the overhead and security risks of data export, proposing a framework for optimizing joint query processing, execution scheduling, and security across heterogeneous hardware to enable effective AI-driven data management.
This paper introduces Local Shapley, a framework that leverages model-induced locality to transform Shapley value computation from an exponential coalition enumeration problem into an efficient, optimal subset-centric process via the LSMR and LSMR-A algorithms, achieving significant speedups without sacrificing valuation accuracy.
The paper introduces SpotIt, a novel evaluation pipeline that employs formal bounded equivalence verification to actively search for database instances distinguishing generated SQL queries from ground-truths, thereby addressing the limitations of optimistic test-based methods and revealing hidden discrepancies in current Text-to-SQL performance assessments.