Declarative Systems for Large-Scale Machine Learning. - Borkar, Vinayak R.; Bu, Yingyi; Carey, Michael J.; Rosen, Joshua; Polyzotis, Neoklis; Condie, Tyson; Weimer, Markus; Ramakrishnan, Raghu (IEEE Data(base) Engineering Bulletin, 2012)
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.