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Chipmink: Efficient Delta Identification for Massive Object Graphs

Summary: Chipmink brings DBMS-style dirty-object tracking to distributed, heterogeneous object graphs, dynamically partitioning objects into cost-aware pods for partial persistence. It supports heaps, shared memory, GPUs, and remote objects, reducing storage up to 36.5× and persistence time 12.4×. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
14545
Venue
VLDB
Year
2026
Pagerank
5.093636e-05
Overall Rank
10,600 | 27.28%
DOI
10.14778/3785297.3785303

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BibTeX Citation

@article{chockchowwat_vldb26,
        title = {{Chipmink: Efficient Delta Identification for Massive Object Graphs}},
        author = {Chockchowwat, Supawit and Thakurdesai, Sumay and Li, Zhaoheng and Krafczyk, Matthew S. and Park, Yongjoo},
        journal = {PVLDB},
        series = {{VLDB} '26},
        volume = {19},
        number = {4},
        pages = {603--616},
        doi = {10.14778/3785297.3785303},
        url = {https://doi.org/10.14778/3785297.3785303},
        year = {2026}
}

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