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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
- 6691
- Venue
- SIGMOD
- Year
- 2023
- Pagerank
- 4.4758336e-05
- Overall Rank
- 8,636 | 39.98%
- DOI
-
10.1145/3589332
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 5 of 5 citing papers.
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 |
| 6 |
The R*-tree: An Efficient and Robust Access Method for Points and Rectangles |
1990 |
SIGMOD |
0.0016113151 |
| 13 |
Mining Association Rules between Sets of Items in Large Databases |
1993 |
SIGMOD |
0.0010863639 |
| 101 |
The Case for Learned Index Structures |
2018 |
SIGMOD |
0.00049778866 |
| 182 |
Mining Frequent Patterns without Candidate Generation |
2000 |
SIGMOD |
0.00036955562 |
| 512 |
STHoles: A Multidimensional Workload-Aware Histogram |
2001 |
SIGMOD |
0.00021385343 |
| 704 |
Query-based Workload Forecasting for Self-Driving Database Management Systems |
2018 |
SIGMOD |
0.00017785557 |
| 819 |
ALEX: An Updatable Adaptive Learned Index |
2020 |
SIGMOD |
0.00016237497 |
| 844 |
The PGM-index: a fully-dynamic compressed learned index with provable worst-case bounds |
2020 |
VLDB |
0.00015964123 |
| 1,262 |
Efficient Query Processing in Geographic Web Search Engines |
2006 |
SIGMOD |
0.00012982825 |
| 1,317 |
Efficient Processing of Top-k Spatial Preference Queries |
2011 |
VLDB |
0.00012615781 |
| 1,438 |
Benchmarking Learned Indexes |
2021 |
VLDB |
0.00011965956 |
| 1,464 |
Learning Multi-dimensional Indexes |
2020 |
SIGMOD |
0.0001184772 |
| 1,473 |
Fine-grained Partitioning for Aggressive Data Skipping |
2014 |
SIGMOD |
0.00011786148 |
| 1,608 |
Qd-tree: Learning Data Layouts for Big Data Analytics |
2020 |
SIGMOD |
0.00011169837 |
| 1,699 |
Are We Ready For Learned Cardinality Estimation? |
2021 |
VLDB |
0.00010848882 |
| 1,887 |
Tsunami: A Learned Multi-dimensional Index for Correlated Data and Skewed Workloads |
2021 |
VLDB |
0.00010201938 |
| 2,020 |
Efficient Retrieval of the Top-k Most Relevant Spatial Web Objects |
2009 |
VLDB |
9.7766925e-05 |
| 2,108 |
LISA: A Learned Index Structure for Spatial Data |
2020 |
SIGMOD |
9.5283642e-05 |
| 2,148 |
Spatial Keyword Query Processing: An Experimental Evaluation |
2013 |
VLDB |
9.4278918e-05 |
| 2,550 |
Updatable Learned Index with Precise Positions |
2021 |
VLDB |
8.5569576e-05 |
| 2,676 |
Effectively Learning Spatial Indices |
2020 |
VLDB |
8.326321e-05 |
| 3,466 |
AI Meets Database: AI4DB and DB4AI |
2021 |
SIGMOD |
7.0645718e-05 |
| 4,693 |
Collective Spatial Keyword Querying |
2011 |
SIGMOD |
5.9867895e-05 |
| 5,289 |
LearnedSQLGen: Constraint-aware SQL Generation using Reinforcement Learning |
2022 |
SIGMOD |
5.5790771e-05 |
| 5,463 |
The RLR-Tree: A Reinforcement Learning Based R-Tree for Spatial Data |
2023 |
SIGMOD |
5.4920768e-05 |
| 5,673 |
Budget-aware Index Tuning with Reinforcement Learning |
2022 |
SIGMOD |
5.3789277e-05 |
| 7,442 |
Selectivity Functions of Range Queries are Learnable* |
2022 |
SIGMOD |
4.7248554e-05 |
| 7,644 |
Selectivity Estimation on Streaming Spatio-Textual Data Using Local Correlations |
2015 |
VLDB |
4.6853638e-05 |
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| Overall Rank |
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| 7,263 |
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4.351469e-05 |
| 3,722 |
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VLDB |
9.4278918e-05 |
| 2,020 |
Efficient Retrieval of the Top-k Most Relevant Spatial Web Objects |
2009 |
VLDB |
9.7766925e-05 |