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The Case for Learned Index Structures
Summary: Indexes treated as models; learned indexes proposed as replacements for B-Tree, Hash, and Bitmap indexes. The paper analyzes theoretical conditions under which learned indexes outperform traditional ones and reports initial results, signaling ML-driven data management as a design paradigm.
(summarized by gpt-5-nano on Feb 09 2026)
Paper ID
h951df9242ad9178d
Venue
SIGMOD
Year
2018
Pagerank
0.00046249672
Overall Rank
40 | 99.74%
DOI
10.1145/3183713.3196909
PDF
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(CC BY-NC-SA 4.0)
Incoming Non-self Citations Over Time
BibTeX Citation
@inproceedings{kraska_sigmod18,
title = {{The Case for Learned Index Structures}},
author = {Kraska, Tim and Beutel, Alex and Chi, Ed H. and Dean, Jeffrey and Polyzotis, Neoklis},
series = {{SIGMOD} '18},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3183713.3196909},
url = {https://dl.acm.org/doi/10.1145/3183713.3196909},
year = {2018}
}
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Small Materialized Aggregates: A Light Weight Index Structure for Data Warehousing 1998
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