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REEF: Retainable Evaluator Execution Framework

Summary: REEF provides a control-plane over resource managers to orchestrate execution. Focus on resource re-use, caching, and state management to enable elastic data workflows; demonstrated by ML, a shell, CORFU port, and Azure Stream Analytics. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5080
Venue
SIGMOD
Year
2015
Pagerank
5.880521e-05
Overall Rank
6,430 | 55.89%
DOI
10.1145/2723372.2742793

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{weimer_sigmod15,
        title = {{REEF: Retainable Evaluator Execution Framework}},
        author = {Weimer, Markus and Chen, Yingda and Chun, Byung-Gon and Condie, Tyson and Curino, Carlo and Douglas, Chris and Lee, Yunseong and Majestro, Tony and Malkhi, Dahlia and Matusevych, Sergiy and Myers, Brandon and Narayanamurthy, Shravan and Ramakrishnan, Raghu and Rao, Sriram and Sears, Russell and Sezgin, Beysim and Wang, Julia},
        series = {{SIGMOD} '15},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/2723372.2742793},
        url = {https://dl.acm.org/doi/10.1145/2723372.2742793},
        year = {2015}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
415 SystemML: Declarative Machine Learning on Spark 2016 VLDB 0.0001888524
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Outgoing Citations (Sorted by Pagerank)

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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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