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WISK: A Workload-aware Learned Index for Spatial Keyword Queries

Summary: WISK is a workload-aware learned index for spatial keyword queries, adapting to the workload distribution. It partitions data into cost-minimizing blocks (NP-hard) and builds an RL-guided hierarchy to prune, achieving up to 8x speedups with similar storage. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
hcd0755cfea77364a
Venue
SIGMOD
Year
2023
Pagerank
5.3794981e-05
Overall Rank
8,196 | 44.90%
DOI
10.1145/3589332

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{sheng_sigmod23,
        title = {{WISK: A Workload-aware Learned Index for Spatial Keyword Queries}},
        author = {Sheng, Yufan and Cao, Xin and Fang, Yixiang and Zhao, Kaiqi and Qi, Jianzhong and Cong, Gao and Zhang, Wenjie},
        series = {{SIGMOD} '23},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3589332},
        url = {https://dl.acm.org/doi/10.1145/3589332},
        year = {2023}
}

Incoming Citations (Sorted by Pagerank)

Showing 6 of 6 citing papers.

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Outgoing Citations (Sorted by Pagerank)

Showing 28 of 28 cited papers.

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

Rank Cited Paper Year Venue Pagerank
4 The R*-tree: An Efficient and Robust Access Method for Points and Rectangles 1990 SIGMOD 0.0011405675
13 Mining Association Rules between Sets of Items in Large Databases 1993 SIGMOD 0.00064420972
40 The Case for Learned Index Structures 2018 SIGMOD 0.00046284649
164 Mining Frequent Patterns without Candidate Generation 2000 SIGMOD 0.00027412227
371 STHoles: A Multidimensional Workload-Aware Histogram 2001 SIGMOD 0.00019829769
430 ALEX: An Updatable Adaptive Learned Index 2020 SIGMOD 0.00018409112
460 Query-based Workload Forecasting for Self-Driving Database Management Systems 2018 SIGMOD 0.00017842695
463 The PGM-index: a fully-dynamic compressed learned index with provable worst-case bounds 2020 VLDB 0.00017804544
848 Benchmarking Learned Indexes 2021 VLDB 0.00013506188
868 Learning Multi-dimensional Indexes 2020 SIGMOD 0.00013354403
1,036 Fine-grained Partitioning for Aggressive Data Skipping 2014 SIGMOD 0.00012377471
1,064 Are We Ready For Learned Cardinality Estimation? 2021 VLDB 0.00012202282
1,132 Qd-tree: Learning Data Layouts for Big Data Analytics 2020 SIGMOD 0.00011898257
1,191 Tsunami: A Learned Multi-dimensional Index for Correlated Data and Skewed Workloads 2021 VLDB 0.00011590153
1,230 Efficient Query Processing in Geographic Web Search Engines 2006 SIGMOD 0.00011412036
1,446 LISA: A Learned Index Structure for Spatial Data 2020 SIGMOD 0.00010629222
1,477 Efficient Processing of Top-k Spatial Preference Queries 2011 VLDB 0.00010551239
1,550 Updatable Learned Index with Precise Positions 2021 VLDB 0.00010282449
1,856 Efficient Retrieval of the Top-k Most Relevant Spatial Web Objects 2009 VLDB 9.4938262e-05
1,878 Effectively Learning Spatial Indices 2020 VLDB 9.4451309e-05
2,908 AI Meets Database: AI4DB and DB4AI 2021 SIGMOD 7.8742664e-05
3,045 Spatial Keyword Query Processing: An Experimental Evaluation 2013 VLDB 7.72015e-05
4,482 LearnedSQLGen: Constraint-aware SQL Generation using Reinforcement Learning 2022 SIGMOD 6.5802486e-05
4,574 Collective Spatial Keyword Querying 2011 SIGMOD 6.5257688e-05
4,850 The RLR-Tree: A Reinforcement Learning Based R-Tree for Spatial Data 2023 SIGMOD 6.3808017e-05
4,968 Budget-aware Index Tuning with Reinforcement Learning 2022 SIGMOD 6.3348803e-05
7,332 Selectivity Functions of Range Queries are Learnable* 2022 SIGMOD 5.5499953e-05
7,561 Selectivity Estimation on Streaming Spatio-Textual Data Using Local Correlations 2015 VLDB 5.4958573e-05
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