ConnectorX: Accelerating Data Loading From Databases to Dataframes
Summary: ConnectorX speeds loading from DBMSs to dataframes by reducing client-side overhead—the dominant cost—rather than query execution or data transfer. Server-side result partitioning and a modular design enable easy extension to multiple databases and dataframes, yielding speedups over prior libraries. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Xiaoying Wang
- 2. Weiyuan Wu
- 3. Jinze Wu
- 4. Yizhou Chen
- 5. Nick Zrymiak
- 6. Changbo Qu
- 7. Lampros Flokas
- 8. George Chow
- 9. Jiannan Wang
- 10. Tianzheng Wang
- 11. Eugene Wu
- 12. Qingqing Zhou
Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,177 | InferF: Declarative Factorization of AI/ML Inferences over Joins | 2026 | SIGMOD | 4.1945683e-05 |
| 10,482 | Fast and Scalable Data Transfer Across Data Systems | 2025 | SIGMOD | 4.1945683e-05 |
| 10,591 | Accio: Bolt-on Query Federation | 2025 | VLDB | 4.1945683e-05 |
| 10,806 | Enter the Warp: Fast and Adaptive Data Transfer with XDBC | 2025 | VLDB | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 17 of 17 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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