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Comparative Evaluation of Big-Data Systems on Scientific Image Analytics Workloads

Summary: First cross-system, large-scale image analytics evaluation on real scientific workloads across SciDB, Myria, Spark, Dask, TensorFlow. Reveals shortcomings affecting implementation and performance, outlining directions for efficiency and usability. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
11596
Venue
VLDB
Year
2017
Pagerank
6.8214304e-05
Overall Rank
4,224 | 71.03%
DOI
10.14778/3137628.3137634

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{mehta_vldb17,
        title = {{Comparative Evaluation of Big-Data Systems on Scientific Image Analytics Workloads}},
        author = {Mehta, Parmita and Dorkenwald, Sven and Zhao, Dongfang and Kaftan, Tomer and Cheung, Alvin and Balazinska, Magdalena and Rokem, Ariel and Connolly, Andrew and Vanderplas, Jacob and AlSayyad, Yusra},
        journal = {PVLDB},
        series = {{VLDB} '17},
        volume = {10},
        number = {11},
        pages = {1226--1237},
        doi = {10.14778/3137628.3137634},
        url = {https://doi.org/10.14778/3137628.3137634},
        year = {2017}
}

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