让数据分析系统同时回答多版本数据中的问题，无需知道数据来源。
Querying Everything Everywhere All at Once: Supervaluationism for the Agentic Lakehouse
- 用超估值语义统一处理多分支数据查询
- 支持跨版本数据的联合分析，结果更全面
- 适合需要融合多个数据版本的智能分析场景
代理式分析正将湖仓一体系统转变为多版本体系：人类或AI生产者在数据分支中创建相互竞争的数据管道，而人类或AI消费者则需在不了解数据生命周期的情况下获取答案。我们提出一个新系统，能够跨分支而非单一快照回答问题。原型聚焦于一种新颖的查询路径，采用超估值语义进行查询评估。由于主流OLAP系统尚无类似多分支查询能力，我们开源演示代码，为OLAP社区提供可复现的基准参考。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。