Declarative Systems for Large-Scale Machine Learning
Vinayak Borkar, Yingyi Bu, Michael J. Carey, Joshua Rosen, Neoklis Polyzotis, Tyson Condie, Markus Weimer and Raghu Ramakrishnan
Abstract
In this article, we make the case for a declarative foundation for data-intensive machine learning systems. Instead of creating a new system for each specific flavor of machine learning task, or hardcoding new optimizations, we argue for the use of recursive queries to program a variety of machine learning algorithms. By taking this approach, database query optimization techniques can be utilized to identify effective execution plans, and the resulting runtime plans can be executed on a single unified data-parallel query processing engine.
BibTeX
@article{Vinayak-Borkar:2012fk,
Author = {Vinayak Borkar, Yingyi Bu, Michael J. Carey, Joshua Rosen, Neoklis Polyzotis, Tyson Condie, Markus Weimer, Raghu Ramakrishnan},
Journal = {Bulletin of the Technical Committee on Data Engineering},
Month = {June},
Number = {2},
Pages = {24},
Title = {Declarative Systems for Large-Scale Machine Learning},
Volume = {35},
Year = {2012}}