ALEX: An Updatable Adaptive Learned Index
Summary: Presents ALEX, an updatable adaptive learned index for mixed read/write workloads. Blends learned indexing with conventional storage to support updates; delivers up to 4.1x read speed and dramatically smaller index footprints vs B+Trees. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Jialin Ding (Massachusetts Institute of Technology)
- 2. Umar Farooq Minhas (Microsoft)
- 3. Jia Yu (Arizona State University)
- 4. Chi Wang (Microsoft)
- 5. Jaeyoung Do (Microsoft)
- 6. Yinan Li (Microsoft)
- 7. Hantian Zhang (Georgia Institute of Technology)
- 8. Badrish Chandramouli (Microsoft)
- 9. Johannes Gehrke (Microsoft)
- 10. Donald Kossmann (Microsoft)
- 11. David Lomet (Microsoft)
- 12. Tim Kraska (Massachusetts Institute of Technology)
BibTeX Citation
@inproceedings{ding_sigmod20,
title = {{ALEX: An Updatable Adaptive Learned Index}},
author = {Ding, Jialin and Minhas, Umar Farooq and Yu, Jia and Wang, Chi and Do, Jaeyoung and Li, Yinan and Zhang, Hantian and Chandramouli, Badrish and Gehrke, Johannes and Kossmann, Donald and Lomet, David and Kraska, Tim},
series = {{SIGMOD} '20},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3318464.3389711},
url = {https://dl.acm.org/doi/10.1145/3318464.3389711},
year = {2020}
}Incoming Citations (Sorted by Pagerank)
Showing 50 of 105 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 17 of 17 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 10,656 | From Learning to Recycling: A Log-Structured Learned-Less Index | 2026 | SIGMOD |
| 2 | 8,382 | Tuning Hierarchical Learned Indexes on Disk and Beyond | 2022 | SIGMOD |
| 3 | 1,525 | Updatable Learned Index with Precise Positions | 2021 | VLDB |
| 4 | 6,613 | Making In-Memory Learned Indexes Efficient on Disk | 2024 | SIGMOD |
| 5 | 10,500 | On Self-Designing Learned Indexes | 2026 | SIGMOD |
| 6 | 8,208 | Algorithmic Complexity Attacks on Dynamic Learned Indexes | 2024 | VLDB |
| 7 | 2,582 | Are Updatable Learned Indexes Ready? | 2022 | VLDB |
| 8 | 3,621 | Learned Index: A Comprehensive Experimental Evaluation | 2023 | VLDB |
| 9 | 835 | Benchmarking Learned Indexes | 2021 | VLDB |
| 10 | 5,378 | Updatable Learned Indexes Meet Disk-Resident DBMS - From Evaluations to Design Choices | 2023 | SIGMOD |