SemJoin: Semantic Join Optimization
Integrating unstructured data into relational database systems is increasingly important as demand grows for natural language querying and analysis. A semantic join, joining two tables under a natural-language predicate, can be evaluated with a large language model (LLM), but comparing every pair of tuples requires O(M x N) LLM invocations and is cost-prohibitive at scale. Existing systems reduce this cost but typically commit to a single fixed strategy (e.g., embedding similarity or one batched scheme) regardless of the data or the join predicate. We propose an LLM-agent-based decision pipeline that optimizes semantic joins by matching the execution strategy to the characteristics of the...
| Authors | Christopher Gou, Aditya Banerjee, Jiaxuan Wang et al. |
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| Primary category | cs.DB |
| Published | 28 June 2026 |
| Last updated at source | 28 June 2026 |
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