Efficient data compression and analysis as a service
First Claim
1. A system, comprising:
- at least one hardware processor; and
a memory to store program instructions that, when executed, cause the at least one processor to implement a method comprising;
accessing historical compression data obtained for previously compressed data;
applying one or more machine learning techniques to the historical compression data to determine one or more compression selection rules;
receiving data from a client to be compressed;
analyzing the received data or metadata associated with the received data in order to select, based at least in part on the determined one or more compression selection rules, two or more compression techniques out of a plurality of different compression techniques to generate two or more compression data candidates to evaluate;
generating the two or more compression data candidates of the received data according to the selected two or more compression techniques;
selecting one of the data compression candidates to send as the compressed data; and
sending the selected data compression candidate as the compressed data to the client.
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Accused Products
Abstract
Data may be efficiently analyzed and compressed as part of a data compression service. A data compression request may be received from a client indicating data to be compressed. An analysis of the data or metadata associated with the data may be performed. In at least some embodiments, this analysis may be a rules-based analysis. Some embodiments may employ one or more machine learning techniques to historical compression data to update the rules-based analysis. One or more compression techniques may be selected out of a plurality of compression techniques to be applied to the data. Data compression candidates may then be generated according to the selected compression techniques. In some embodiments, a compression service restriction may be enforced. One of the data compression candidates may be selected and sent in a response.
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Citations
20 Claims
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1. A system, comprising:
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at least one hardware processor; and a memory to store program instructions that, when executed, cause the at least one processor to implement a method comprising; accessing historical compression data obtained for previously compressed data; applying one or more machine learning techniques to the historical compression data to determine one or more compression selection rules; receiving data from a client to be compressed; analyzing the received data or metadata associated with the received data in order to select, based at least in part on the determined one or more compression selection rules, two or more compression techniques out of a plurality of different compression techniques to generate two or more compression data candidates to evaluate; generating the two or more compression data candidates of the received data according to the selected two or more compression techniques; selecting one of the data compression candidates to send as the compressed data; and sending the selected data compression candidate as the compressed data to the client. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A method, comprising:
performing, by at least one computing device; accessing historical compression data obtained for previously compressed data; applying one or more machine learning techniques to the historical compression data to determine one or more compression selection rules; receiving data from a client to be compressed; analyzing the received data or metadata associated with the received data in order to select, based at least in part on the determined one or more compression selection rules, two or more compression techniques out of a plurality of different compression techniques to generate two or more compression data candidates to evaluate; generating the two or more compression data candidates of the received data according to the selected compression techniques; selecting one of the data compression candidates to send as compressed data; and sending the selected data compression candidate as the compressed data to the client. - View Dependent Claims (9, 10, 11, 12, 13, 14)
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15. A non-transitory, computer-readable storage medium, storing program instructions that when executed by one or more computing devices cause the one or more computing devices to implement:
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accessing historical compression data obtained for previously compressed data; applying one or more machine learning techniques to the historical compression data to determine one or more compression selection rules; receiving data from a client to be compressed; analyzing the received data or metadata associated with the received data in order to select, based at least in part on the determined one or more compression selection rules, two or more compression techniques out of a plurality of different compression techniques to generate two or more compression data candidates to evaluate; generating the two or more compression data candidates of the data according to the selected two or more compression techniques; selecting one of the data compression candidates to send as the compressed data; and sending the selected data compression candidate as the compressed data to the client. - View Dependent Claims (16, 17, 18, 19, 20)
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Specification