SystemML: Declarative Machine Learning on Spark
Summary: Declarative ML via SystemML's DSL for linear algebra lets data scientists express custom algorithms while Spark uses cost-based plans. End-to-end Spark integration yields in-memory and scalable plans; open-source with optimizer/runtime insights for research. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Matthias Boehm (IBM)
- 2. Michael W. Dusenberry (IBM Spark Technology Center)
- 3. Deron Eriksson (IBM Spark Technology Center)
- 4. Alexandre V. Evfimievski (IBM)
- 5. Faraz Makari Manshadi (IBM)
- 6. Niketan Pansare (IBM)
- 7. Berthold Reinwald (IBM)
- 8. Frederick R. Reiss (IBM; IBM Spark Technology Center)
- 9. Prithviraj Sen (IBM)
- 10. Arvind C. Surve (IBM Spark Technology Center)
- 11. Shirish Tatikonda (IBM; Target Corporation)
BibTeX Citation
@article{boehm_vldb16,
title = {{SystemML: Declarative Machine Learning on Spark}},
author = {Boehm, Matthias and Dusenberry, Michael W. and Eriksson, Deron and Evfimievski, Alexandre V. and Manshadi, Faraz Makari and Pansare, Niketan and Reinwald, Berthold and Reiss, Frederick R. and Sen, Prithviraj and Surve, Arvind C. and Tatikonda, Shirish},
journal = {PVLDB},
series = {{VLDB} '16},
volume = {9},
number = {13},
pages = {1425--1436},
doi = {10.14778/3007263.3007273},
url = {https://doi.org/10.14778/3007263.3007273},
year = {2016}
}
Incoming Citations (Sorted by Pagerank)
Showing 50 of 64 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 12 of 12 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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