Querying Everything Everywhere All at Once: Supervaluationism for the Agentic Lakehouse
Fuente:
arXiv
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| Autore principale: | |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866918387737690112 |
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| author | Tagliabue, Jacopo |
| author_facet | Tagliabue, Jacopo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_13380 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Querying Everything Everywhere All at Once: Supervaluationism for the Agentic Lakehouse Tagliabue, Jacopo Databases Artificial Intelligence 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. |
| title | Querying Everything Everywhere All at Once: Supervaluationism for the Agentic Lakehouse |
| topic | Databases Artificial Intelligence |
| url | https://arxiv.org/abs/2603.13380 |