5 papers
Bootstrapping Learned Cost Models with Synthetic SQL Queries
Michael Nidd, Christoph Miksovic, Thomas Gschwind +3
Having access to realistic workloads for a given database instance is extremely important to enable stress and vulnerability testing, as well as to optimize for cost and performanc…
AI Risk Atlas: Taxonomy and Tooling for Navigating AI Risks and Resources
Frank Bagehorn, Kristina Brimijoin, Elizabeth M. Daly +17
The rapid evolution of generative AI has expanded the breadth of risks associated with AI systems. While various taxonomies and frameworks exist to classify these risks, the lack o…
Are Bias Evaluation Methods Biased ?
Lina Berrayana, Sean Rooney, Luis Garcés-Erice +1
The creation of benchmarks to evaluate the safety of Large Language Models is one of the key activities within the trusted AI community. These benchmarks allow models to be compare…
A Learned Cost Model-based Cross-engine Optimizer for SQL Workloads
András Strausz, Niels Pardon, Ioana Giurgiu
Lakehouse systems enable the same data to be queried with multiple execution engines. However, selecting the engine best suited to run a SQL query still requires a priori knowledge…
Usage Governance Advisor: From Intent to AI Governance
Elizabeth M. Daly, Sean Rooney, Seshu Tirupathi +9
Evaluating the safety of AI Systems is a pressing concern for organizations deploying them. In addition to the societal damage done by the lack of fairness of those systems, deploy…