Vector Databases: What’s Really New and What’s Next? (VLDB 2024 Panel)
Summary: Characterizes the vector-DB surge: standalone systems (Chroma/Qdrant/Weaviate/Vespa) and broad RDBMS/cloud adoption to supply LLM context. Panel contrasts real innovations vs decades of similarity-search work and outlines next challenges: hybrid SQL+vector semantics, indexing, benchmarks. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
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Authors
- 1. Jianguo Wang
- 2. Eric Hanson
- 3. Guoliang Li
- 4. Yannis Papakonstantinou
- 5. Harsha Simhadri
- 6. Charles Xie
Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,680 | SingleStore-V: An Integrated Vector Database System in SingleStore | 2024 | VLDB | 6.8496415e-05 |
| 8,687 | TigerVector: Supporting Vector Search in Graph Databases for Advanced RAGs | 2025 | SIGMOD | 4.4675056e-05 |
| 9,978 | Fast Vector Search in PostgreSQL: A Decoupled Approach | 2026 | CIDR | 4.1945683e-05 |
| 10,160 | Efficient Vector Index Merging in Vector Databases | 2026 | SIGMOD | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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