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Declarative Data Serving: The Future of Machine Learning Inference on the Edge

Summary: Proposes declarative data serving for edge ML inference, specifying data placement and movement across heterogeneous devices via high-level constraints. Positions this as a database agenda for managing task-specific communication without brittle hand-coded systems. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
12618
Venue
VLDB
Year
2021
Pagerank
5.2528121e-05
Overall Rank
9,566 | 34.37%
DOI
10.14778/3476249.3476302

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{shaowang_vldb21,
        title = {{Declarative Data Serving: The Future of Machine Learning Inference on the Edge}},
        author = {Shaowang, Ted and Jain, Nilesh and Matthews, Dennis D. and Krishnan, Sanjay},
        journal = {PVLDB},
        series = {{VLDB} '21},
        volume = {14},
        number = {11},
        pages = {2555--2562},
        doi = {10.14778/3476249.3476302},
        url = {https://doi.org/10.14778/3476249.3476302},
        year = {2021}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
10,623 KEN: An Execution Engine for Unstructured Database Systems 2026 VLDB 5.093636e-05
11,077 Algorithmic Data Minimization for Machine Learning over Internet-of-Things Data Streams 2025 VLDB 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 5 of 5 cited papers.

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

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