Rank-aware Query Optimization
Summary: Rank-aware query optimization framework integrating rank-join operators into relational engines, extending System R's dynamic programming with ranking-driven enumeration and pruning. Proposes a probabilistic model to estimate input cardinality and rank-join cost—the first to optimize input size for rank aggregation—validated on an open-source DBMS. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Ihab F. Ilyas (Purdue University)
- 2. Rahul Shah (Purdue University)
- 3. Walid G. Aref (Purdue University)
- 4. Jeffrey Scott Vitter (Purdue University)
- 5. Ahmed K. Elmagarmid (Purdue University)
BibTeX Citation
@inproceedings{ilyas_sigmod04,
title = {{Rank-aware Query Optimization}},
author = {Ilyas, Ihab F. and Shah, Rahul and Aref, Walid G. and Vitter, Jeffrey Scott and Elmagarmid, Ahmed K.},
series = {{SIGMOD} '04},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/1007568.1007593},
url = {https://dl.acm.org/doi/10.1145/1007568.1007593},
year = {2004}
}
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