Provided is a data meta-scaling method. The data meta-scaling method optimizes an abbreviation criterion for abbreviating data through continuous knowledge augmentation in various dimensions which enable expression of data in a process of performing machine learning.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A data meta-scaling method for continuous learning, the data meta-scaling method comprising: setting, by a processor, abbreviation criterion information which defines a rule for abbreviating input data to be expressed in another attribute, learning criterion information which defines a rule for limiting learning on the abbreviation data and a rule for evaluating learning performance, and knowledge augmentation criterion information which defines a rule for optimizing the abbreviation criterion information; abbreviating, by the processor, the input data to abbreviation data, based on the abbreviation criterion information; performing, by the processor, learning on the abbreviation data to generate a learning model, based on the learning criterion information; evaluating, by the processor, performance of the learning model to determine suitability of the abbreviation data, based on the learning criterion information; and performing, by the processor, knowledge augmentation for updating the abbreviation criterion information according to a result of the suitability determination, based on the knowledge augmentation criterion information. 2. The data meta-scaling method of claim 1, wherein the setting comprises setting the abbreviation criterion information which defines a rule for abbreviating the input data expressed as a plurality of attributes to be expressed as at least one of the plurality of attributes. 3. The data meta-scaling method of claim 1, wherein the setting comprises, when the input data is expressed as a plurality of attributes, setting the abbreviation criterion information which includes information representing a data dimension defining one of the plurality of attributes, information representing a window defining a unit of sampling of the input data, information representing a kind of the window, information representing a size of the window, and information representing a criterion for selecting a representative value in the window. 4. The data meta-scaling method of claim 1, wherein the setting comprises setting the learning criterion information which includes information representing a kind of the input data, information representing a condition of learning reliability for evaluating performance of the learning model, information representing a method of calculating the learning reliability, and information representing an early stop condition of learning which limits number of repetitions of the learning on the abbreviation data. 5. The data meta-scaling method of claim 1, wherein the setting comprises setting the knowledge augmentation criterion information which includes information representing number of changes of the abbreviation criterion information, information representing a change factor of the abbreviation criterion information, information representing a change range of the change factor, and information representing number of accumulations of a learning history generated in a process of performing learning on the abbreviation data. 6. The data meta-scaling method of claim 5, wherein the change factor is information associated with a window defining a unit of sampling of the input data. 7. The data meta-scaling method of claim 6, wherein the information associated with the window comprises pieces of information representing a size of the window and an interval between windows. 8. The data meta-scaling method of claim 1, wherein the abbreviating comprises, when the input data is expressed as a plurality of attributes and the plurality of attributes are defined as a plurality of data dimensions, abbreviating the input data to abbreviation data through one of a first process of sampling the input data as a representative value of the input data in each of the plurality of data dimensions, a second process of changing the input data to at least one data dimension selected from among the plurality of data dimensions, and a third process including a combination of the first process and the second process. 9. The data meta-scaling method of claim 8, wherein the first process comprises: a process of periodically sampling the input data as the representative value of the input data; a process of aperiodically sampling the input data as the representative value of the input data; a fixed window-based sampling process of, in a state where a plurality of windows defining a unit of sampling of the input data do not overlap each other, selecting the representative value in each of the plurality of windows; and a moving window-based sampling process of, in a state where the plurality of windows overlap each other, selecting the representative value in each of the plurality of windows. 10. The data meta-scaling method of claim 1, wherein the performing of the knowledge augmentation comprises: when learning reliability calculated for evaluating the performance of the learning model does not satisfy a condition prescribed in the rule, defined in the learning criterion information, for evaluating the learning performance, changing the abbreviation criterion information according to information representing a change factor, defined in the knowledge augmentation criterion information, of the abbreviation criterion information and a change range of the change factor; and when performance of a learning model generated by performing learning on the abbreviation data abbreviated based on the changed abbreviation criterion information satisfies a condition prescribed in the learning criterion information, updating the changed abbreviation criterion information to optimal abbreviation criterion information. 11. A data meta-scaling apparatus for continuous learning, the data meta-scaling apparatus comprising: a meta-optimizer setting abbreviation criterion information which defines a rule for abbreviating input data to be expressed in another attribute, learning criterion information which defines a rule for limiting learning on the abbreviation data and a rule for evaluating learning performance, and knowledge augmentation criterion information which defines a rule for optimizing the abbreviation criterion information; an abbreviator abbreviating the input data to abbreviation data, based on the abbreviation criterion information; a learning machine performing learning on the abbreviation data to generate a learning model, based on the learning criterion information; and an evaluator evaluating performance of the learning model to determine suitability of the abbreviation data, based on the learning criterion information, wherein the meta-optimizer performs knowledge augmentation for updating the abbreviation criterion information according to a result of the suitability determination, based on the knowledge augmentation criterion information. 12. The data meta-scaling apparatus of claim 11, wherein the meta-optimizer sets the abbreviation criterion information which defines a rule for abbreviating the input data expressed as a plurality of attributes to be expressed as at least one of the plurality of attributes. 13. The data meta-scaling apparatus of claim 11, wherein when the input data is expressed as a plurality of attributes, the meta-optimizer sets the abbreviation criterion information which includes information representing a data dimension defining one of the plurality of attributes, information representing a window defining a unit of sampling of the input data, information representing a kind of the window, information representing a size of the window, and information representing a criterion for selecting a representative value in the window. 14. The data meta-scaling apparatus of claim 11, wherein the meta-optimizer sets the learning criterion information which includes information representing a kind of the input data, information representing a condition of learning reliability for evaluating performance of the learning model, information representing a method of calculating the learning reliability, and information representing an early stop condition of learning which limits number of repetitions of the learning on the abbreviation data. 15. The data meta-scaling apparatus of claim 11, wherein the meta-optimizer sets the knowledge augmentation criterion information which includes information representing number of changes of the abbreviation criterion information, information representing a change factor of the abbreviation criterion information, information representing a change range of the change factor, and information representing number of accumulations of a learning history generated in a process of performing learning on the abbreviation data. 16. The data meta-scaling apparatus of claim 15, wherein the change factor is information associated with a window defining a unit of sampling of the input data. 17. The data meta-scaling apparatus of claim 11, wherein when the performance of the learning model does not satisfy a condition prescribed in the rule for evaluating the learning performance, the meta-optimizer changes the abbreviation criterion information according to information representing a change factor, defined in the knowledge augmentation criterion information, of the abbreviation criterion information and a change range of the change factor, and when performance of a learning model generated by performing learning on the abbreviation data abbreviated based on the changed abbreviation criterion information satisfies a condition prescribed in the learning criterion information, the meta-optimizer stores the changed abbreviation criterion information as the updated abbreviation criterion information in a storage unit to perform knowledge augmentation.