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Tuning Hierarchical Learned Indexes on Disk and Beyond

Summary: Explores learned hierarchical indexes under external memory (disk/remote storage), where fast random access assumptions fail and I/O dominates cost. Shows that loading the entire index is impractical and per-key lookups trigger extra round-trips; proposes external-memory aware tuning that exploits on-disk key-position patterns to reduce costly accesses. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6399
Venue
SIGMOD
Year
2022
Pagerank
4.4398976e-05
Overall Rank
8,811 | 38.77%
DOI
10.1145/3514221.3520255

Incoming Non-self Citations Over Time

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Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
5,327 DILI: A Distribution-Driven Learned Index 2023 VLDB 5.5660777e-05
10,172 HIRE: A Hybrid Learned Index for Robust and Efficient Performance under Mixed Workloads 2026 SIGMOD 4.1905499e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 5 of 5 cited papers.

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

Rank Cited Paper Year Venue Pagerank
101 The Case for Learned Index Structures 2018 SIGMOD 0.00049778866
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
1,365 FITing-Tree: A Data-aware Index Structure 2019 SIGMOD 0.00012379754
1,438 Benchmarking Learned Indexes 2021 VLDB 0.00011965956
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