Dynamically Optimizing Queries over Large Scale Data Platforms
Summary: Dynamic optimization of queries on large-scale data platforms, handling opaque UDFs and cross-relational correlations. Pilot runs estimate selectivities to seed a cost-based plan, after which plans evolve during execution to yield up to 2x (Jaql) and 4x (Hive) gains over hand-written left-deep baselines. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Konstantinos Karanasos (IBM)
- 2. Andrey Balmin (GraphSQL)
- 3. Marcel Kutsch (Apple)
- 4. Fatma Özcan (IBM)
- 5. Vuk Ercegovac (Google)
- 6. Chunyang Xia (IBM)
- 7. Jesse Jackson (IBM)
BibTeX Citation
@inproceedings{karanasos_sigmod14,
title = {{Dynamically Optimizing Queries over Large Scale Data Platforms}},
author = {Karanasos, Konstantinos and Balmin, Andrey and Kutsch, Marcel and Özcan, Fatma and Ercegovac, Vuk and Xia, Chunyang and Jackson, Jesse},
series = {{SIGMOD} '14},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/2588555.2610531},
url = {https://dl.acm.org/doi/10.1145/2588555.2610531},
year = {2014}
}
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