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A Comparison of Platforms for Implementing and Running Very Large Scale Machine Learning Algorithms

Summary: A comparative benchmark of four platforms for large-scale ML inference across five hierarchical-model tasks. Uses 70,000 EC2 hours to compare runtimes, tuning, and programming effort, highlighting data-management tradeoffs for DB researchers. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
4811
Venue
SIGMOD
Year
2014
Pagerank
7.0702034e-05
Overall Rank
3,460 | 75.96%
DOI
10.1145/2588555.2593680

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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 Pig Latin: A Not-So-Foreign Language for Data Processing 2008 SIGMOD 0.0024217964
4 Pregel: A System for Large-Scale Graph Processing 2010 SIGMOD 0.0019040811
139 The MADlib Analytics Library or MAD Skills, the SQL 2012 VLDB 0.00042320525
543 MLbase: A Distributed Machine-learning System 2013 CIDR 0.0002050918
1,165 Simulation of Database-Valued Markov Chains Using SimSQL 2013 SIGMOD 0.00013567206
1,375 SQLEM: Fast Clustering in SQL using the EM Algorithm 2000 SIGMOD 0.00012321024
1,494 Ricardo: Integrating R and Hadoop 2010 SIGMOD 0.00011677793
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