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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)

Paper ID
13627
Venue
VLDB
Year
2024
Pagerank
5.093636e-05
Overall Rank
11,232 | 22.94%
DOI
10.14778/3659437.3659458

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Authors

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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