SeLeP: Learning Based Semantic Prefetching for Exploratory Database Workloads
Summary: SeLeP: semantic prefetching for exploratory SQL by encoding block values and framing prefetching as a time-series forecasting problem. An encoder–decoder LSTM learns semantic (not address) access patterns, improving hit ratio up to 40% and cutting I/O ~45% (96% hit, 84% I/O reduction avg). (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Farzaneh Zirak (University of Melbourne)
- 2. Farhana Choudhury (University of Melbourne)
- 3. Renata Borovica-Gajic (University of Melbourne)
BibTeX Citation
@article{zirak_vldb24,
title = {{SeLeP: Learning Based Semantic Prefetching for Exploratory Database Workloads}},
author = {Zirak, Farzaneh and Choudhury, Farhana and Borovica-Gajic, Renata},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {8},
pages = {2064--2076},
doi = {10.14778/3659437.3659458},
url = {https://doi.org/10.14778/3659437.3659458},
year = {2024}
}
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