Sibyl: Forecasting Time-Evolving Query Workloads

Summary: Sibyl forecasts time-evolving query workloads, with full statements, across multiple future windows. Template-based featurization plus a stacked-LSTM encoder-decoder handles drift and large windows, delivering 87.3% median F1 and 1.7x/1.3x gains for MV/index selection. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 6924
Venue: SIGMOD
Year: 2024
Pagerank: 5.5925285e-05
Overall Rank: 7,580 | 48.00%
DOI: 10.1145/3639308

Incoming Non-self Citations Over Time

Authors

1. Hanxian Huang (University of California San Diego)
2. Tarique Siddiqui (Microsoft)
3. Rana Alotaibi (Microsoft)
4. Carlo Curino (Microsoft)
5. Jyoti Leeka (Microsoft)
6. Alekh Jindal (SmartApps)
7. Jishen Zhao (University of California San Diego)
8. Jesús Camacho-Rodríguez (Microsoft)
9. Yuanyuan Tian (Microsoft)

BibTeX Citation

@inproceedings{huang_sigmod24,
        title = {{Sibyl: Forecasting Time-Evolving Query Workloads}},
        author = {Huang, Hanxian and Siddiqui, Tarique and Alotaibi, Rana and Curino, Carlo and Leeka, Jyoti and Jindal, Alekh and Zhao, Jishen and Camacho-Rodríguez, Jesús and Tian, Yuanyuan},
        series = {{SIGMOD} '24},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3639308},
        url = {https://dl.acm.org/doi/10.1145/3639308},
        year = {2024}
}

Incoming Citations (Sorted by Pagerank)

Showing 5 of 5 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
10,128	Survivorship Bias in Industrial Database Workloads	2026	CIDR	5.093636e-05
10,506	This is Going to Sound Crazy, But What If We Used Large Language Models to Boost Automatic Database Tuning Algorithms By Leveraging Prior History? We Will Find Better Configurations More Quickly Than Retraining From Scratch!	2026	SIGMOD	5.093636e-05
10,569	Toward Drift-Aware Database Benchmarking	2026	VLDB	5.093636e-05
10,969	Improving DBMS Scheduling Decisions with Accurate Performance Prediction on Concurrent Queries	2025	VLDB	5.093636e-05
11,290	Presto’s History-based Query Optimizer	2024	VLDB	5.093636e-05

Outgoing Citations (Sorted by Pagerank)

Showing 16 of 16 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
87	Automated Selection of Materialized Views and Indexes for SQL Databases	2000	VLDB	0.00035281619
154	Neo: A Learned Query Optimizer	2019	VLDB	0.00028726181
184	DB2 Design Advisor: Integrated Automatic Physical Database Design	2004	VLDB	0.00026256101
234	Self-Driving Database Management Systems	2017	CIDR	0.00023810722
378	Bao: Making Learned Query Optimization Practical	2021	SIGMOD	0.00019638121
445	Apache Calcite: A Foundational Framework for Optimized Query Processing Over Heterogeneous Data Sources	2018	SIGMOD	0.00018336751
461	Query-based Workload Forecasting for Self-Driving Database Management Systems	2018	SIGMOD	0.00018068441
846	SnipSuggest: Context-Aware Autocompletion for SQL	2011	VLDB	0.00013658824
1,218	P-Store: An Elastic Database System with Predictive Provisioning	2018	SIGMOD	0.00011621066
1,468	Towards a Learning Optimizer for Shared Clouds	2019	VLDB	0.00010686496
1,765	Selecting Subexpressions to Materialize at Datacenter Scale	2018	VLDB	9.8079546e-05
2,197	Multi-Dimensional Clustering: A New Data Layout Scheme in DB2	2003	SIGMOD	8.9684812e-05
2,822	Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings	2020	SIGMOD	8.0898536e-05
3,605	Computation Reuse in Analytics Job Service at Microsoft	2018	SIGMOD	7.2640711e-05
8,783	Tiresias: Enabling Predictive Autonomous Storage and Indexing	2022	VLDB	5.3740362e-05
9,175	CrocodileDB in Action: Resource-Efficient Query Execution by Exploiting Time Slackness	2020	VLDB	5.3087332e-05

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