Adaptive Code Generation for Data-Intensive Analytics
Summary: Adaptive Code Generation for Data-Intensive Analytics extends an open-source compiler with database-style query optimization to synthesize plans. Plans run on data chunks and adapt via feedback to switch later chunks, enabling adaptive performance. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Wangda Zhang (Columbia University)
- 2. Junyoung Kim (Columbia University)
- 3. Kenneth A. Ross (Columbia University)
- 4. Eric Sedlar (Oracle)
- 5. Lukas Stadler (Oracle)
BibTeX Citation
@article{zhang_vldb21,
title = {{Adaptive Code Generation for Data-Intensive Analytics}},
author = {Zhang, Wangda and Kim, Junyoung and Ross, Kenneth A. and Sedlar, Eric and Stadler, Lukas},
journal = {PVLDB},
series = {{VLDB} '21},
volume = {14},
number = {6},
pages = {929--942},
doi = {10.14778/3447689.3447697},
url = {https://doi.org/10.14778/3447689.3447697},
year = {2021}
}
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,273 | User-Defined Operators: Efficiently Integrating Custom Algorithms into Modern Databases | 2022 | VLDB | 6.7909197e-05 |
| 5,301 | Babelfish: Efficient Execution of Polyglot Queries | 2022 | VLDB | 6.2750553e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 37 of 37 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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