Systems and methods for processing machine learning algorithms in a MapReduce environment
First Claim
1. A computer implemented method for processing Machine Learning (ML) algorithms in a MapReduce environment, comprising:
- receiving a ML algorithm to be executed in the MapReduce environment;
parsing the ML algorithm into a plurality of statement blocks in a sequence, wherein each statement block comprises a plurality of basic operations (hops);
automatically determining an execution plan for each statement block, wherein at least one of the execution plans comprises one or more low-level operations (lops), and wherein determining an execution plan for each statement block comprises translating a hop into at least one lop; and
implementing the execution plans in the sequence of the plurality of the statement blocks.
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Accused Products
Abstract
Systems and methods for processing Machine Learning (ML) algorithms in a MapReduce environment are described. In one embodiment of a method, the method includes receiving a ML algorithm to be executed in the MapReduce environment. The method further includes parsing the ML algorithm into a plurality of statement blocks in a sequence, wherein each statement block comprises a plurality of basic operations (hops). The method also includes automatically determining an execution plan for each statement block, wherein at least one of the execution plans comprises one or more low-level operations (lops). The method further includes implementing the execution plans in the sequence of the plurality of the statement blocks.
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Citations
25 Claims
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1. A computer implemented method for processing Machine Learning (ML) algorithms in a MapReduce environment, comprising:
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receiving a ML algorithm to be executed in the MapReduce environment; parsing the ML algorithm into a plurality of statement blocks in a sequence, wherein each statement block comprises a plurality of basic operations (hops); automatically determining an execution plan for each statement block, wherein at least one of the execution plans comprises one or more low-level operations (lops), and wherein determining an execution plan for each statement block comprises translating a hop into at least one lop; and implementing the execution plans in the sequence of the plurality of the statement blocks. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)
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15. A computer program product comprising a non-transitory computer useable storage medium to store a computer readable program, wherein the computer readable program, when executed on a computer, causes the computer to perform operations for processing Machine Learning (ML) algorithms in a MapReduce environment, comprising:
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receiving a ML algorithm to be executed in the MapReduce environment; parsing the ML algorithm into a plurality of statement blocks in a sequence, wherein each statement block comprises a plurality of basic operations (hops); automatically determining an execution plan for each statement block, wherein at least one of the execution plans comprises one or more low-level operations (lops), and wherein determining an execution plan for each statement block comprises translating a hop into at least one lop; implementing the execution plans in the sequence of the plurality of the statement blocks; and determining if a plurality of lops may be represented by one MapReduce job, wherein such determination comprises piggybacking lops into one MapReduce job based on the characteristics of the lops. - View Dependent Claims (16, 17, 18, 19, 20, 21, 22, 23)
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24. A system for processing Machine Learning (ML) algorithms in a MapReduce environment, comprising:
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means for receiving a ML algorithm to be executed in the MapReduce environment; means for parsing the ML algorithm into a plurality of statement blocks in a sequence, wherein each statement block comprises a plurality of basic operations (hops); means for automatically determining an execution plan for each statement block, wherein at least one of the execution plans comprises one or more low-level operations (lops); means for implementing the execution plans in the sequence of the plurality of the statement blocks; means for optimizing the execution plan comprises means for optimizing size of a data block representation for implementing the ML algorithm. - View Dependent Claims (25)
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Specification