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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
h5ef924860d0d8290
Venue
SIGMOD
Year
2022
Pagerank
6.0582762e-05
Overall Rank
5,630 | 62.15%
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 16 of 16 citing papers.

Rank Citing Paper Year Venue Pagerank
6,105 Breaking It Down: An In-depth Study of Index Advisors 2024 VLDB 5.8860941e-05
7,217 Refactoring Index Tuning Process with Benefit Estimation 2024 VLDB 5.5834823e-05
7,900 DISTILL: Low-Overhead Data-Driven Techniques for Filtering and Costing Indexes for Scalable Index Tuning 2022 VLDB 5.4303143e-05
7,977 The Holon Approach for Simultaneously Tuning Multiple Components in a Self-Driving Database Management System with Machine Learning via Synthesized Proto-Actions 2024 VLDB 5.4142519e-05
8,016 Generating Succinct Descriptions of Database Schemata for Cost-Efficient Prompting of Large Language Models 2024 VLDB 5.4065977e-05
9,133 Hit the Gym: Accelerating Query Execution to Efficiently Bootstrap Behavior Models for Self-Driving Database Management Systems 2024 VLDB 5.2229655e-05
9,671 Robustness of Updatable Learning-based Index Advisors against Poisoning Attack 2024 SIGMOD 5.1448657e-05
9,706 Database Gyms 2023 CIDR 5.1376763e-05
9,790 Wii: Dynamic Budget Reallocation In Index Tuning 2024 SIGMOD 5.1260323e-05
10,297 Wred: Workload Reduction for Scalable Index Tuning 2024 SIGMOD 5.0430432e-05
10,333 SCompression: Enhancing Database Knob Tuning Efficiency Through Slice-Based OLTP Workload Compression 2025 VLDB 5.0281656e-05
10,604 Understanding and Detecting Query Performance Regression in Practical Index Tuning: [Experiments & Analysis] 2026 SIGMOD 4.9793485e-05
10,681 RIB: Robust Learning-based Index Benefit Estimation 2026 SIGMOD 4.9793485e-05
10,887 MFTune: An Efficient Multi-fidelity Framework for Spark SQL Configuration Tuning 2026 VLDB 4.9793485e-05
10,918 Evaluating the Practical Effectiveness of LLM-Driven Index Tuning on Microsoft SQL Server 2026 VLDB 4.9793485e-05
11,224 Esc: An Early-Stopping Checker for Budget-aware Index Tuning 2025 VLDB 4.9793485e-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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