FRAMEWORK FOR CALCULATING GROUPED OPTIMIZATION ALGORITHMS WITHIN A DISTRIBUTED DATA STORE

摘要

A framework for executing iterative grouped optimization algorithms such as machine learning and other analytic algorithms directly on unsorted data within a SQL data store without first redistributing the data comprises an architecture that provides C++ abstraction layers that include the algorithms over a SQL data store, and a higher Python abstraction layer that includes grouping and iteration controllers and call functionality to the C++ layer for invocation of the algorithms.