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ArrayMorph: Optimizing Hyperslab Queries on the Cloud for Machine Learning Pipelines

Summary: ArrayMorph cost-basedly optimizes hyperslab (region-of-interest) reads from cloud-resident arrays by choosing among chunking, byte-range, and server-side filtering based on layout and platform costs. PyTorch integration cuts transferred data up to 9.8×, improving runtime 1.7× and cost 9×. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
14140
Venue
VLDB
Year
2025
Pagerank
5.093636e-05
Overall Rank
10,912 | 25.14%
DOI
10.14778/3746405.3746437

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BibTeX Citation

@article{jiang_vldb25,
        title = {{ArrayMorph: Optimizing Hyperslab Queries on the Cloud for Machine Learning Pipelines}},
        author = {Jiang, Ruochen and Blanas, Spyros},
        journal = {PVLDB},
        series = {{VLDB} '25},
        volume = {18},
        number = {9},
        pages = {3189--3202},
        doi = {10.14778/3746405.3746437},
        url = {https://doi.org/10.14778/3746405.3746437},
        year = {2025}
}

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