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Declarative Systems for Large-Scale Machine Learning
2012
IEEE Data Engineering Bulletin
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.
dblp:journals/debu/BorkarBCRPCWR12
fatcat:rdg5wqjvbjg6vina3fu7lsjaqa