Querying Everything Everywhere All at Once: Supervaluationism for the Agentic Lakehouse
This addresses the need for multi-branch querying in OLAP systems, providing a baseline for the community, though it is incremental as it builds on existing lakehouse concepts.
The paper tackles the problem of querying across multiple data branches in agentic lakehouses, where competing pipelines exist, by introducing a system that uses supervaluationary semantics to answer queries without requiring knowledge of the underlying data lifecycle.
Agentic analytics is turning the lakehouse into a multi-version system: swarms of (human or AI) producers materialize competing pipelines in data branches, while (human or AI) consumers need answers without knowing the underlying data life-cycle. We demonstrate a new system that answers questions across branches rather than at a single snapshot. Our prototype focuses on a novel query path that evaluates queries under supervaluationary semantics. In the absence of comparable multi-branch querying capabilities in mainstream OLAP systems, we open source the demo code as a concrete baseline for the OLAP community.