By Yannis Papakonstantinou
Communications of the ACM,
Vol. 63 No. 8, Page 92
Linear algebra operations are at the core of machine learning. Multiple specialized systems have emerged for the scalable, distributed execution of matrix and vector operations. The relationship of such computations to data management and databases however brings frictions. It is well known that a great deal of human time and machine time is being spent nowadays on fetching data out of the database and performing a computation on a specialized system. One answer to the issue is that we truly need a new kind of non-SQL database that is tuned to these computations.
The creators of SimSQL opted for the decidedly incremental approach. Can we make a very small set of changes to the relational model and RDBMS software to render them suitable for executing linear algebra in the database?
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