Sieve: A Learned Data-Skipping Index for Data Analytics

Summary: Sieve is a learned data-skipping index that models block-distribution trends over the key space with piecewise-linear functions. It groups keys with similar distributions to trade index size for false positives, reducing accessed blocks by up to 80% and query time by 42%. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID: h11a8ad872f0b4eb3
Venue: VLDB
Year: 2023
Pagerank: 5.3763264e-05
Overall Rank: 8,207 | 44.84%
DOI: 10.14778/3611479.3611520
PDF: Download (CC BY-NC-ND 4.0)

Incoming Non-self Citations Over Time

Authors

1. Yulai Tong (Huazhong University of Science and Technology)
2. Jiazhen Liu (Huazhong University of Science and Technology)
3. Hua Wang (Huazhong University of Science and Technology)
4. Ke Zhou (Huazhong University of Science and Technology)
5. Rongfeng He (Huawei)
6. Qin Zhang (Huawei)
7. Cheng Wang (Huawei)

BibTeX Citation

@article{tong_vldb23,
        title = {{Sieve: A Learned Data-Skipping Index for Data Analytics}},
        author = {Tong, Yulai and Liu, Jiazhen and Wang, Hua and Zhou, Ke and He, Rongfeng and Zhang, Qin and Wang, Cheng},
        journal = {PVLDB},
        series = {{VLDB} '23},
        volume = {16},
        number = {11},
        pages = {3214--3226},
        doi = {10.14778/3611479.3611520},
        url = {https://doi.org/10.14778/3611479.3611520},
        year = {2023}
}

Incoming Citations (Sorted by Pagerank)

Showing 4 of 4 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
6,867	Accelerating String-key Learned Index Structures via Memoization-based Incremental Training	2024	VLDB	5.6581046e-05
7,362	Optimizing Collections of Bloom Filters within a Space Budget	2024	VLDB	5.540567e-05
10,586	High Performance or Low Memory? An Updatable Learned Index Framework for Time-Space Tradeoff	2026	SIGMOD	4.9769913e-05
10,723	Robust Predicate Transfer with Dynamic Execution	2026	VLDB	4.9769913e-05

Outgoing Citations (Sorted by Pagerank)

Showing 20 of 20 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
40	The Case for Learned Index Structures	2018	SIGMOD	0.00046363107
163	DB2 with BLU Acceleration: So Much More than Just a Column Store	2013	VLDB	0.00027480091
216	Small Materialized Aggregates: A Light Weight Index Structure for Data Warehousing	1998	VLDB	0.00024485637
422	ALEX: An Updatable Adaptive Learned Index	2020	SIGMOD	0.00018488849
458	The PGM-index: a fully-dynamic compressed learned index with provable worst-case bounds	2020	VLDB	0.00017880664
768	FITing-Tree: A Data-aware Index Structure	2019	SIGMOD	0.00014107655
1,506	BF-Tree: Approximate Tree Indexing	2014	VLDB	0.00010448376
1,531	Column Imprints: A Secondary Index Structure	2013	SIGMOD	0.00010344951
1,714	Online Piece-wise Linear Approximation of Numerical Streams with Precision Guarantees	2009	VLDB	9.8119635e-05
2,483	AnalyticDB: Real-time OLAP Database System at Alibaba Cloud	2019	VLDB	8.3973995e-05
2,954	Column Sketches: A Scan Accelerator for Rapid and Robust Predicate Evaluation	2018	SIGMOD	7.8119682e-05
3,075	Pushing Data-Induced Predicates Through Joins in Big-Data Clusters	2020	VLDB	7.6742518e-05
3,083	Skipping-oriented Partitioning for Columnar Layouts	2017	VLDB	7.6620866e-05
3,285	Performance-Optimal Filtering: Bloom Overtakes Cuckoo at High Throughput	2019	VLDB	7.4525495e-05
3,445	Slalom: Coasting Through Raw Data via Adaptive Partitioning and Indexing	2017	VLDB	7.2911896e-05
3,592	Two Birds, One Stone: A Fast, yet Lightweight, Indexing Scheme for Modern Database Systems	2017	VLDB	7.1807308e-05
4,618	The Price of Tailoring the Index to Your Data: Poisoning Attacks on Learned Index Structures	2022	SIGMOD	6.4981355e-05
4,755	Cuckoo Index: A Lightweight Secondary Index Structure	2020	VLDB	6.4309508e-05
6,806	Petabyte Scale Databases and Storage Systems at Facebook	2013	SIGMOD	5.6768452e-05
9,984	Fingerprints for Compressed Columnar Data Search	2019	SIGMOD	5.0991805e-05

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1	4,627	Stacked Filters: Learning to Filter by Structure	2021	VLDB
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