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Data Management in Machine Learning: Challenges, Techniques, and Systems
Summary: Survey of data-management challenges and systems for ML workloads. Three lines of work: integrating ML with DBMS; adapting DB techniques to ML (queries, partitioning, compression); and combining data-management with ML lifecycles, plus open directions.
(summarized by gpt-5-nano on Feb 09 2026)
Paper ID
h3600472ff61e2e81
Venue
SIGMOD
Year
2017
Pagerank
0.00011325762
Overall Rank
1,255 | 91.57%
DOI
10.1145/3035918.3054775
Incoming Non-self Citations Over Time
BibTeX Citation
@inproceedings{kumar_sigmod17,
title = {{Data Management in Machine Learning: Challenges, Techniques, and Systems}},
author = {Kumar, Arun and Boehm, Matthias and Yang, Jun},
series = {{SIGMOD} '17},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3035918.3054775},
url = {https://dl.acm.org/doi/10.1145/3035918.3054775},
year = {2017}
}
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Query Processing on Tensor Computation Runtimes 2022
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HADAD: A Lightweight Approach for Optimizing Hybrid Complex Analytics Queries 2021
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