MetaJoin：面向异构表格的自适应连接选择框架

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Abstract:Joining heterogeneous tables requires different forms of correspondence, including exact agreement, syntactic transformation, semantic similarity, and entity-level matching. The appropriate join method is often unknown in advance, while running every candidate method incurs unnecessary cost. We introduce MetaJoin, a label-free framework for adaptive join selection. MetaJoin uses input evidence to choose an initial specialist, then uses specialist-specific output evidence to determine whether that result should be retained, compared with an alternative, escalated, or repaired. This allows join selection to adapt after execution rather than committing to a one-shot routing decision. We also introduce MNN Join, a transformation specialist that searches for string transformations by maximizing mutual-nearest-neighbor coverage between two unaligned columns and uses the same reciprocal structure to validate its output. We evaluate MetaJoin on 670 datasets from six benchmarks. It achieves 0.817 macro row-F1, slightly exceeding a benchmark-level oracle that is given the best standalone specialist for each benchmark (0.812), and outperforms label-free selection policies built from the same specialist portfolio by more than 0.13. Across the 609 datasets in the four transformation-dominant benchmarks, MNN Join outperforms every evaluated transformation baseline. Finally, MetaJoin's accuracy varies by only 0.015 as the controller backbone ranges from 3B to 14B parameters, showing that output validation and repair substantially reduce dependence on the initial routing decision.

Subjects:	Databases (cs.DB); Machine Learning (cs.LG)
Cite as:	arXiv:2512.02444 [cs.DB]
	(or arXiv:2512.02444v2 [cs.DB] for this version)
	https://doi.org/10.48550/arXiv.2512.02444 arXiv-issued DOI via DataCite

Submission history

From: Ning Wang [view email]
[v1] Tue, 2 Dec 2025 06:05:48 UTC (268 KB)
[v2] Fri, 9 Oct 2026 05:07:24 UTC (571 KB)