Integrating Vector Databases across Embedding Models

Summary: Integrates vector DBs built with different embedding models without access to raw data or model internals, enabling cross‑database top‑k similarity search. Relies on an empirical "local isometry" hypothesis with theoretical quality bounds and achieves high top‑k recall across NV‑embed‑V2, OpenAI Ada, GloVe, Mistral, and FastText. (summarized by gpt-5-mini on Feb 11 2026)

Paper ID: 7399
Venue: SIGMOD
Year: 2026
Pagerank: 4.1945683e-05
Overall Rank: 10,090 | 29.81%
DOI: 10.1145/3769803

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Rank	Citing Paper	Year	Venue	Pagerank

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Showing 13 of 13 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
398	Big Data Integration	2013	VLDB	0.00024372588
495	Milvus: A Purpose-Built Vector Data Management System	2021	SIGMOD	0.00021767688
736	AnalyticDB-V: A Hybrid Analytical Engine Towards Query Fusion for Structured and Unstructured Data	2020	VLDB	0.00017447617
893	Data Integration: The Teenage Years	2006	VLDB	0.00015558352
2,209	Data Integration: After the Teenage Years	2017	PODS	9.2868035e-05
2,262	Manu: A Cloud Native Vector Database Management System	2022	VLDB	9.1624446e-05
2,730	Open Data Integration	2018	VLDB	8.2126735e-05
3,680	SingleStore-V: An Integrated Vector Database System in SingleStore	2024	VLDB	6.8496415e-05
3,772	FEXIPRO: Fast and Exact Inner Product Retrieval in Recommender Systems	2017	SIGMOD	6.7761705e-05
4,401	LEMP: Fast Retrieval of Large Entries in a Matrix Product	2015	SIGMOD	6.2211271e-05
4,731	Graph-Based Vector Search: An Experimental Evaluation of the State-of-the-Art	2025	SIGMOD	5.966659e-05
8,509	The Journey to A Knowledgeable Assistant with Retrieval-Augmented Generation (RAG)	2024	SIGMOD	4.4952414e-05
9,335	Context-sensitive Ranking for Document Retrieval	2011	SIGMOD	4.3556432e-05

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