Opportunities for Data Management Research in the Era of Horizontal AI/ML
Summary: Horizontal AI/ML expands data-management touchpoints across the stack, elevating data-centric challenges in ML pipelines. Panel outlines opportunities, cross-domain collaboration, and evaluation strategies to shape a practical data-management research agenda. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Theodoros Rekatsinas (University of Wisconsin)
- 2. Sudeepa Roy (Duke University)
- 3. Manasi Vartak (Verta)
- 4. Ce Zhang (ETH Zurich)
- 5. Neoklis Polyzotis (Google)
BibTeX Citation
@article{rekatsinas_vldb19,
title = {{Opportunities for Data Management Research in the Era of Horizontal AI/ML}},
author = {Rekatsinas, Theodoros and Roy, Sudeepa and Vartak, Manasi and Zhang, Ce and Polyzotis, Neoklis},
journal = {PVLDB},
series = {{VLDB} '19},
volume = {12},
number = {12},
pages = {2323--2324},
doi = {10.14778/3352063.3352149},
url = {https://doi.org/10.14778/3352063.3352149},
year = {2019}
}
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,105 | Optimizing In-memory Database Engine for AI-powered On-line Decision Augmentation Using Persistent Memory | 2021 | VLDB | 5.9743349e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 0 of 0 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|
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