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)
Incoming Non-self Citations Over Time
Authors
- 1. Yufan Sheng (University of New South Wales)
- 2. Xin Cao (University of New South Wales)
- 3. Yixiang Fang (Chinese University of Hong Kong)
- 4. Kaiqi Zhao (University of Auckland)
- 5. Jianzhong Qi (University of Melbourne)
- 6. Gao Cong (Nanyang Technological University)
- 7. Wenjie Zhang (University of New South Wales)
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.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,368 | Accelerating String-key Learned Index Structures via Memoization-based Incremental Training | 2024 | VLDB | 5.6315363e-05 |
| 10,262 | LINE: A Learned Index with Group-Enhanced Leaves and Cache-Optimized Inner Tree | 2026 | SIGMOD | 5.093636e-05 |
| 10,378 | High Performance or Low Memory? An Updatable Learned Index Framework for Time-Space Tradeoff | 2026 | SIGMOD | 5.093636e-05 |
| 10,469 | LM-Tree: A Hybrid Learned Index for Similarity Search in Metric Spaces | 2026 | SIGMOD | 5.093636e-05 |
| 10,610 | A Workload-Aware Encrypted Index for Efficient Privacy-Preserving Range Queries | 2026 | VLDB | 5.093636e-05 |
| 10,851 | ACE: A Cardinality Estimator for Set-Valued Queries | 2025 | VLDB | 5.093636e-05 |
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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.
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