Machine Learning for Big Data
Summary: Survey of using DB systems to scale ML, highlighting architectures, relational-algebra workflows, recursion, and streaming support. Outlines systems, applications, gaps, and questions at the DB-ML interface to guide cross-domain research. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Tyson Condie (Microsoft)
- 2. Paul Mineiro (Microsoft)
- 3. Neoklis Polyzotis (University of California Santa Cruz)
- 4. Markus Weimer (Microsoft)
BibTeX Citation
@inproceedings{condie_sigmod13,
title = {{Machine Learning for Big Data}},
author = {Condie, Tyson and Mineiro, Paul and Polyzotis, Neoklis and Weimer, Markus},
series = {{SIGMOD} '13},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/2463676.2465338},
url = {https://dl.acm.org/doi/10.1145/2463676.2465338},
year = {2013}
}
Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 521 | Learning Linear Regression Models over Factorized Joins | 2016 | SIGMOD | 0.00016929744 |
| 1,255 | Data Management in Machine Learning: Challenges, Techniques, and Systems | 2017 | SIGMOD | 0.00011325762 |
| 4,116 | Resource Elasticity for Large-Scale Machine Learning | 2015 | SIGMOD | 6.7961306e-05 |
| 9,286 | ParaX: Boosting Deep Learning for Big Data Analytics on Many-Core CPUs | 2021 | VLDB | 5.2025256e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 2 of 2 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3 | Pregel: A System for Large-Scale Graph Processing | 2010 | SIGMOD | 0.0012092602 |
| 22 | Distributed GraphLab: A Framework for Machine Learning and Data Mining in the Cloud | 2012 | VLDB | 0.00055962491 |
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Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 537 | MLbase: A Distributed Machine-learning System | 2013 | CIDR |
| 2 | 8,839 | Machine Learning for Data Management: Problems and Solutions | 2018 | SIGMOD |
| 3 | 14,438 | Clustering Methods for Large Databases: From the Past to the Future | 1999 | SIGMOD |
| 4 | 8,480 | Deep Learning: Systems and Responsibility | 2021 | SIGMOD |
| 5 | 2,400 | Declarative Recursive Computation on an RDBMS or, Why You Should Use a Database For Distributed Machine Learning | 2019 | VLDB |
| 6 | 3,716 | Machine Learning and Databases: The Sound of Things to Come or a Cacophony of Hype? | 2015 | SIGMOD |
| 7 | 4,741 | Machine Learning for Databases | 2021 | VLDB |
| 8 | 7,806 | Machine Learning for Cloud Data Systems: the Progress so far and the Path Forward | 2021 | VLDB |
| 9 | 9,073 | Machine Learning for Graph Data Management and Query Processing | 2025 | VLDB |
| 10 | 1,255 | Data Management in Machine Learning: Challenges, Techniques, and Systems | 2017 | SIGMOD |