Decision-based data compression by means of deep learning technologies
First Claim
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1. A method for creating a model for handling data based on compressibility of the data, the method comprising:
- identifying, by a computer system, a first training data stream input, the first data stream input comprising a sample of fully compressed data;
identifying, by the computer system, a second training data stream input, the second data stream input comprising a sample of compressible data;
evaluating, using an artificial neural network (ANN), the first training data stream input and the second training data stream input;
tagging, as fully compressed, the first training data stream output; and
tagging, as compressible, the second training data stream output.
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Abstract
Data may be handled based on compressibility (i.e., whether the data may be further compressed or is not further compressible). A supervised learning model may be trained using a set of known further compressible data and a set of known non-compressible data. Using these data sets, the model may generate weighting factors and bias for the particular data sets. The trained model may then be used to evaluate a set of unclassified data.
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Citations
15 Claims
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1. A method for creating a model for handling data based on compressibility of the data, the method comprising:
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identifying, by a computer system, a first training data stream input, the first data stream input comprising a sample of fully compressed data; identifying, by the computer system, a second training data stream input, the second data stream input comprising a sample of compressible data; evaluating, using an artificial neural network (ANN), the first training data stream input and the second training data stream input; tagging, as fully compressed, the first training data stream output; and tagging, as compressible, the second training data stream output. - View Dependent Claims (2, 3, 4, 5)
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6. A computer system for handling data based on compressibility, the system comprising a processor configured to perform a method comprising:
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identifying a first training data stream input, the first data stream input comprising a sample of fully compressed data; identifying a second training data stream input, the second data stream input comprising a sample of compressible data; evaluating, using an artificial neural network (ANN), the first training data stream input and the second training data stream input; tagging, as fully compressed, the first training data stream output; and tagging, as compressible, the second training data stream output. - View Dependent Claims (7, 8, 9, 10)
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11. A computer program product for handling data based on compressibility, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, wherein the computer readable storage medium is not a transitory signal per se, the program instructions executable by a processor to cause the processor to perform a method comprising:
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identifying a first training data stream input, the first data stream input comprising a sample of fully compressed data; identifying a second training data stream input, the second data stream input comprising a sample of compressible data; evaluating, using an artificial neural network (ANN), the first training data stream input and the second training data stream input; tagging, as fully compressed, the first training data stream output; and tagging, as compressible, the second training data stream output. - View Dependent Claims (12, 13, 14, 15)
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Specification