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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)

Paper ID
6526
Venue
SIGMOD
Year
2022
Pagerank
6.0610922e-05
Overall Rank
5,869 | 59.74%
DOI
10.1145/3514221.3526152

Incoming Non-self Citations Over Time

Authors

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}
}

Incoming Citations (Sorted by Pagerank)

Showing 14 of 14 citing papers.

Rank Citing Paper Year Venue Pagerank
6,327 Breaking It Down: An In-depth Study of Index Advisors 2024 VLDB 5.9124005e-05
7,076 Refactoring Index Tuning Process with Benefit Estimation 2024 VLDB 5.7098893e-05
7,750 DISTILL: Low-Overhead Data-Driven Techniques for Filtering and Costing Indexes for Scalable Index Tuning 2022 VLDB 5.5523652e-05
7,846 The Holon Approach for Simultaneously Tuning Multiple Components in a Self-Driving Database Management System with Machine Learning via Synthesized Proto-Actions 2024 VLDB 5.5331459e-05
7,865 Generating Succinct Descriptions of Database Schemata for Cost-Efficient Prompting of Large Language Models 2024 VLDB 5.5282752e-05
8,984 Hit the Gym: Accelerating Query Execution to Efficiently Bootstrap Behavior Models for Self-Driving Database Management Systems 2024 VLDB 5.3395569e-05
9,490 Robustness of Updatable Learning-based Index Advisors against Poisoning Attack 2024 SIGMOD 5.2629522e-05
9,536 Database Gyms 2023 CIDR 5.2529727e-05
9,615 Wii: Dynamic Budget Reallocation In Index Tuning 2024 SIGMOD 5.2436464e-05
10,082 Wred: Workload Reduction for Scalable Index Tuning 2024 SIGMOD 5.1587525e-05
10,105 SCompression: Enhancing Database Knob Tuning Efficiency Through Slice-Based OLTP Workload Compression 2025 VLDB 5.1435736e-05
10,413 Understanding and Detecting Query Performance Regression in Practical Index Tuning: [Experiments & Analysis] 2026 SIGMOD 5.093636e-05
10,494 RIB: Robust Learning-based Index Benefit Estimation 2026 SIGMOD 5.093636e-05
10,815 Esc: An Early-Stopping Checker for Budget-aware Index Tuning 2025 VLDB 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 14 of 14 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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