DEEM'22: Data Management for End-to-End Machine Learning

Summary: DEEM’22 at SIGMOD/PODS bridges applied ML, data management, and systems to end-to-end ML data-management issues. Unique for two tracks (regular 10-page papers; apps/tools 4-page reports) and 13 global submissions, focusing on pipelines, datasets, benchmarks, and tools. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 6477
Venue: SIGMOD
Year: 2022
Pagerank: -
Overall Rank: 13,414 | 7.97%
DOI: 10.1145/3514221.3524075

Incoming Non-self Citations Over Time

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Authors

1. Matthias Boehm (Graz University of Technology)
2. Paroma Varma (Snorkel AI)
3. Doris Xin (Linea; University of California Berkeley)

BibTeX Citation

@inproceedings{boehm_sigmod22,
        title = {{DEEM'22: Data Management for End-to-End Machine Learning}},
        author = {Boehm, Matthias and Varma, Paroma and Xin, Doris},
        series = {{SIGMOD} '22},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3514221.3524075},
        url = {https://dl.acm.org/doi/10.1145/3514221.3524075},
        year = {2022}
}

Incoming Citations (Sorted by Pagerank)

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Rank	Citing Paper	Year	Venue	Pagerank

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.

Rank	Cited Paper	Year	Venue	Pagerank

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