Data compression by using cognitive created dictionaries
First Claim
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1. A computer-implemented compression method, the method comprising:
- creating compressed data via a first system from input data;
sending information to a second system detailing a compression strategy for the compressed data;
learning, via the second system, from the information how to recreate the input to the first system using the compressed data;
decompressing, via the second system, the compressed data to a recreated input;
comparing the input data to the first system with the recreated input in an iterative loop;
based on a result of the comparing and the iterative loop of the comparing, modifying the information at each iterative loop how to recreate the input such that the input data to the first system matches the recreated input; and
sending a feedback to the first system based on the recreated input by the second system,wherein the first system teaches the second system how to recreate the input simultaneously while the second system teaches the first system an effectiveness of the teaching that the first system provides to the second system,further comprising measuring a quality of the recreated input by the second system to send a feedback to the first system to adjust the information for the compression strategy,wherein the information is not sent to the second system with a next compressed data when the quality is greater than a predetermined threshold value,wherein the first system and the second system are trained by the learning and feedback without training data and/or pre-annotated data,wherein the input data comprises a set of unstructured data of special types having multiple categories to classify each of the special types into one of the multiple categories, andwherein the first system and the second system comprise a neuronal network or a cognitive expert.
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Abstract
A compression method, system, and computer program product include creating compressed data via a first system from input data, sending information to a second system detailing a compression strategy for the compressed data, and learning, via the second system, from the information how to recreate the input to the first system using the compressed data.
39 Citations
12 Claims
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1. A computer-implemented compression method, the method comprising:
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creating compressed data via a first system from input data; sending information to a second system detailing a compression strategy for the compressed data; learning, via the second system, from the information how to recreate the input to the first system using the compressed data; decompressing, via the second system, the compressed data to a recreated input; comparing the input data to the first system with the recreated input in an iterative loop; based on a result of the comparing and the iterative loop of the comparing, modifying the information at each iterative loop how to recreate the input such that the input data to the first system matches the recreated input; and sending a feedback to the first system based on the recreated input by the second system, wherein the first system teaches the second system how to recreate the input simultaneously while the second system teaches the first system an effectiveness of the teaching that the first system provides to the second system, further comprising measuring a quality of the recreated input by the second system to send a feedback to the first system to adjust the information for the compression strategy, wherein the information is not sent to the second system with a next compressed data when the quality is greater than a predetermined threshold value, wherein the first system and the second system are trained by the learning and feedback without training data and/or pre-annotated data, wherein the input data comprises a set of unstructured data of special types having multiple categories to classify each of the special types into one of the multiple categories, and wherein the first system and the second system comprise a neuronal network or a cognitive expert. - View Dependent Claims (2, 3, 4, 5, 6)
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7. A computer program product for compression, the computer program product comprising a computer-readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to perform:
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creating compressed data via a first system from input data; sending information to a second system detailing a compression strategy for the compressed data; learning, via the second system, from the information how to recreate the input to the first system using the compressed data; decompressing, via the second system, the compressed data to a recreated input; comparing the input data to the first system with the recreated input in an iterative loop; based on a result of the comparing and the iterative loop of the comparing, modifying the information at each iterative loop how to recreate the input such that the input data to the first system matches the recreated input; and sending a feedback to the first system based on the recreated input by the second system, wherein the first system teaches the second system how to recreate the input simultaneously while the second system teaches the first system an effectiveness of the teaching that the first system provides to the second system, further comprising measuring a quality of the recreated input by the second system to send a feedback to the first system to adjust the information for the compression strategy, wherein the information is not sent to the second system with a next compressed data when the quality is greater than a predetermined threshold value, wherein the first system and the second system are trained by the learning and feedback without training data and/or pre-annotated data, wherein the input data comprises a set of unstructured data of special types having multiple categories to classify each of the special types into one of the multiple categories, and wherein the first system and the second system comprise a neuronal network or a cognitive expert. - View Dependent Claims (8, 9, 10)
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11. A compression system, said system comprising:
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a processor; and a memory, the memory storing instructions to cause the processor to perform; creating compressed data via a first system from input data; sending information to a second system detailing a compression strategy for the compressed data; learning, via the second system, from the information how to recreate the input to the first system using the compressed data; decompressing, via the second system, the compressed data to a recreated input; comparing the input data to the first system with the recreated input in an iterative loop; based on a result of the comparing and the iterative loop of the comparing, modifying the information at each iterative loop how to recreate the input such that the input data to the first system matches the recreated input; and sending a feedback to the first system based on the recreated input by the second system, wherein the first system teaches the second system how to recreate the input simultaneously while the second system teaches the first system an effectiveness of the teaching that the first system provides to the second system, further comprising measuring a quality of the recreated input by the second system to send a feedback to the first system to adjust the information for the compression strategy, wherein the information is not sent to the second system with a next compressed data when the quality is greater than a predetermined threshold value, wherein the first system and the second system are trained by the learning and feedback without training data and/or pre-annotated data, wherein the input data comprises a set of unstructured data of special types having multiple categories to classify each of the special types into one of the multiple categories, and wherein the first system and the second system comprise a neuronal network or a cognitive expert. - View Dependent Claims (12)
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Specification