ISUM: Efficiently Compressing Large and Complex Workloads for Scalable Index Tuning
Summary: ISUM compresses large, complex workloads to enable scalable index tuning. It introduces a low-overhead performance-gain estimator and a concise cross-query representation that avoids pairwise comparisons, yielding 1.4x median, 2x max improvement versus prior techniques on real workloads. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Tarique Siddiqui (Microsoft)
- 2. Saehan Jo (Cornell University)
- 3. Wentao Wu (Microsoft)
- 4. Chi Wang (Microsoft)
- 5. Vivek Narasayya (Microsoft)
- 6. Surajit Chaudhuri (Microsoft)
BibTeX Citation
@inproceedings{siddiqui_sigmod22,
title = {{ISUM: Efficiently Compressing Large and Complex Workloads for Scalable Index Tuning}},
author = {Siddiqui, Tarique and Jo, Saehan and Wu, Wentao and Wang, Chi and Narasayya, Vivek and Chaudhuri, Surajit},
series = {{SIGMOD} '22},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3514221.3526152},
url = {https://dl.acm.org/doi/10.1145/3514221.3526152},
year = {2022}
}
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