24 citations · 62 across the 21 of their papers we have counts for
22 papers
Test-Time Optimization of Physical Query Plans with LLMs
Mehmet Hamza Erol, Xiangpeng Hao, Federico Bianchi +3
Traditional query optimization relies on cost-based optimizers that estimate execution cost (e.g., runtime, memory, and I/O) using predefined heuristics and statistical models. Imp…
Trustworthy AI in the Agentic Lakehouse: from Concurrency to Governance
Jacopo Tagliabue, Federico Bianchi, Ciro Greco
Even as AI capabilities improve, most enterprises do not consider agents trustworthy enough to work on production data. In this paper, we argue that the path to trustworthy agentic…
Safe, Untrusted, "Proof-Carrying" AI Agents: toward the agentic lakehouse
Jacopo Tagliabue, Ciro Greco
Data lakehouses run sensitive workloads, where AI-driven automation raises concerns about trust, correctness, and governance. We argue that API-first, programmable lakehouses provi…
Eudoxia: a FaaS scheduling simulator for the composable lakehouse
Tapan Srivastava, Jacopo Tagliabue, Ciro Greco
Due to the variety of its target use cases and the large API surface area to cover, a data lakehouse (DLH) is a natural candidate for a composable data system. Bauplan is a composa…
FaaS and Furious: abstractions and differential caching for efficient data pre-processing
Jacopo Tagliabue, Ryan Curtin, Ciro Greco
Data pre-processing pipelines are the bread and butter of any successful AI project. We introduce a novel programming model for pipelines in a data lakehouse, allowing users to int…
Bauplan: zero-copy, scale-up FaaS for data pipelines
Jacopo Tagliabue, Tyler Caraza-Harter, Ciro Greco
Chaining functions for longer workloads is a key use case for FaaS platforms in data applications. However, modern data pipelines differ significantly from typical serverless use c…