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Vineyard: Optimizing Data Sharing in Data-Intensive Analytics

Summary: Vineyard is a cloud-native, extensible object store for zero-copy sharing of intermediate data across heterogeneous analytics systems. Its VCDL registers data types, enabling memory mapping and method sharing; evaluations report up to 68.4× faster data exchange. (summarized by gpt-5.6-luna on Jul 21 2026)

Paper ID
hbb4a166ad5c4a03a
Venue
SIGMOD
Year
2023
Pagerank
5.0400722e-05
Overall Rank
10,307 | 30.71%
DOI
10.1145/3589780

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{yu_sigmod23,
        title = {{Vineyard: Optimizing Data Sharing in Data-Intensive Analytics}},
        author = {Yu, Wenyuan and He, Tao and Wang, Lei and Meng, Ke and Cao, Ye and Zhu, Diwen and Li, Sanhong and Zhou, Jingren},
        series = {{SIGMOD} '23},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3589780},
        url = {https://dl.acm.org/doi/10.1145/3589780},
        year = {2023}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
9,770 GraphScope Flex: LEGO-like Graph Computing Stack 2024 SIGMOD 5.133285e-05
10,932 IMLane: Composable Framework for Efficient AI Function Execution in Database Engine 2026 VLDB 4.9793485e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 4 of 4 cited papers.

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

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