Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings
Summary: Workload-driven cost models for big data queries, integrated into a Cascade-style optimizer to optimize plans and containers. In production, Cleo/SCOPE sees 2–3 orders higher accuracy and 20x correlation; ~70% plan changes cut latency and save resources. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Tarique Siddiqui (Microsoft; University of Illinois Urbana-Champaign)
- 2. Alekh Jindal (Microsoft)
- 3. Shi Qiao (Microsoft)
- 4. Hiren Patel (Microsoft)
- 5. Wangchao Le (Microsoft)
BibTeX Citation
@inproceedings{siddiqui_sigmod20,
title = {{Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings}},
author = {Siddiqui, Tarique and Jindal, Alekh and Qiao, Shi and Patel, Hiren and Le, Wangchao},
series = {{SIGMOD} '20},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3318464.3380584},
url = {https://dl.acm.org/doi/10.1145/3318464.3380584},
year = {2020}
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