The MADlib Analytics Library or MAD Skills, the SQL
Summary: MADlib is an open-source, extensible library of parallel SQL-native machine-learning, data-mining, and statistical methods, bringing CRAN-like community analytics into database engines. It emphasizes in-database execution at scale, avoiding data movement and supporting research contributions across platforms. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Joseph M. Hellerstein (University of California Berkeley)
- 2. Christoper Ré (University of Wisconsin)
- 3. Florian Schoppmann (Greenplum)
- 4. Daisy Zhe Wang (University of Florida)
- 5. Eugene Fratkin (Greenplum)
- 6. Aleksander Gorajek (Greenplum)
- 7. Kee Siong Ng (Greenplum)
- 8. Caleb Welton (Greenplum)
- 9. Xixuan Feng (University of Wisconsin)
- 10. Kun Li (University of Florida)
- 11. Arun Kumar (University of Wisconsin)
BibTeX Citation
@article{hellerstein_vldb12,
title = {{The MADlib Analytics Library or MAD Skills, the SQL}},
author = {Hellerstein, Joseph M. and Ré, Christoper and Schoppmann, Florian and Wang, Daisy Zhe and Fratkin, Eugene and Gorajek, Aleksander and Ng, Kee Siong and Welton, Caleb and Feng, Xixuan and Li, Kun and Kumar, Arun},
journal = {PVLDB},
series = {{VLDB} '12},
volume = {5},
number = {12},
pages = {1700--1711},
doi = {10.14778/2367502.2367519},
url = {https://doi.org/10.14778/2367502.2367519},
year = {2012}
}Incoming Citations (Sorted by Pagerank)
Showing 50 of 106 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 7 of 7 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 | 0.0012250108 |
| 44 | A Comparison of Approaches to Large-Scale Data Analysis | 2009 | SIGMOD | 0.00046055057 |
| 155 | MAD Skills: New Analysis Practices for Big Data | 2009 | VLDB | 0.00028713176 |
| 518 | Towards a Unified Architecture for in-RDBMS Analytics | 2012 | SIGMOD | 0.00017167492 |
| 2,002 | SQLEM: Fast Clustering in SQL using the EM Algorithm | 2000 | SIGMOD | 9.3312236e-05 |
| 4,055 | Hybrid In-Database Inference for Declarative Information Extraction | 2011 | SIGMOD | 6.9349546e-05 |
| 4,935 | Querying Probabilistic Information Extraction | 2010 | VLDB | 6.4357418e-05 |
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