Evaluating system generated historical transaction timeline images
In an approach for evaluating system generated historical transaction timeline images for a computer vision deep learning process, a processor provides one or more chart evaluation factors for evaluating historical timeline images for a deep learning of patterns based on the historical timeline images. A processor generates a quantitative metric based on the one or more chart evaluation factors using a quantitative technique. A processor determines a score for an input timeline image based on the quantitative metric. A processor filters input space based on the score. A processor, in response to receiving a feedback, adjusts a chart setting based on the one or more chart evaluation factors.
1 . A computer-implemented method, comprising:
providing, by one or more processors, one or more chart evaluation factors corresponding to a plurality of historical timeline images;
generating, by the one or more processors, a quantitative metric for the plurality of historical timeline images, based on the one or more chart evaluation factors, using one or more quantitative techniques, wherein the one or more quantitative techniques comprise at least one selected from the group consisting of analytical hierarchical process, fuzzy logic, and statistical distribution;
determining, by the one or more processors, a score for each of one or more input timeline images, based on the quantitative metric;
selecting, by the one or more processors, automatically an input timeline image from the one or more input timeline images for training a cognitive system in a deep event learning process, based on the score of the one or more input timeline images, wherein the cognitive system uses the selected input timeline image to detect patterns in the plurality of historical timeline images;
in response to receiving a feedback based on insights from a user, adjusting, by the one or more processors, a chart setting for evaluating the one or more input timeline images, wherein the chart setting comprises the one or more chart evaluation factors; and
applying, by the one or more processors, the adjusted chart setting for selecting further input timeline images, from the one or more input timeline images, for training the cognitive system in the deep event learning process,
wherein determining the score comprises determining a highest score among scores in the quantitative metric associated with the one or more chart evaluation factors.
2 . The computer-implemented method of claim 1 , wherein providing the one or more chart evaluation factors includes determining the one or more chart evaluation factors based on at least one of a chart type, scaling, color, marker shape, and data density.
3 . The computer-implemented method of claim 1 , wherein determining the score includes aggregating scores in the quantitative metric associated with the one or more chart evaluation factors.
4 . A computer program product, comprising:
one or more computer readable storage media; and
program instructions collectively stored on the one or more computer readable storage media, the program instructions comprising:
program instructions to provide one or more chart evaluation factors corresponding to a plurality of historical timeline images;
program instructions to generate a quantitative metric for the plurality of historical timeline images, based on the one or more chart evaluation factors, using one or more quantitative techniques, wherein the one or more quantitative techniques comprise at least one selected from the group consisting of analytical hierarchical process, fuzzy logic, and statistical distribution;
program instructions to determine a score for each of one or more input timeline images, based on the quantitative metric;
program instructions to select automatically an input timeline image from the one or more input timeline images for training a cognitive system in a deep event learning process, based on the score of the one or more input timeline images, wherein the cognitive system uses the selected input timeline image to detect patterns in the plurality of historical timeline images;
program instructions to, in response to receiving a feedback, adjust a chart setting to evaluate the one or more input timeline images, wherein the chart setting comprises the one or more chart evaluation factors; and
program instructions to apply the adjusted chart setting to select further input timeline images, from the one or more input timeline images, for training the cognitive system in the deep event learning process,
wherein the program instructions to determine the score include program instructions to determine a highest score among scores in the quantitative metric associated with the one or more chart evaluation factors.
5 . The computer program product of claim 4 , wherein the program instructions to provide the one or more chart evaluation factors include program instructions to determine the one or more chart evaluation factors based on at least one of chart type, scaling, color, marker shape and data density.
6 . The computer program product of claim 4 , wherein the program instructions to determine the score include program instructions to aggregate scores in the quantitative metric associated with the one or more chart evaluation factors.
7 . A computer system, comprising:
one or more computer processors;
one or more computer readable storage media; and
program instructions stored on the one or more computer readable storage media for execution by at least one of the one or more computer processors, the program instructions comprising:
program instructions to provide one or more chart evaluation factors corresponding to a plurality of historical timeline images;
program instructions to generate a quantitative metric for the plurality of historical timeline images, based on the one or more chart evaluation factors, using one or more quantitative techniques, wherein the one or more quantitative techniques comprise at least one selected from the group consisting of analytical hierarchical process, fuzzy logic, and statistical distribution;
program instructions to determine a score for each of one or more input timeline images, based on the quantitative metric;
program instructions to select automatically an input timeline image from the one or more input timeline images for training a cognitive system in a deep event learning process, based on the score of the one or more input timeline images, wherein the cognitive system uses the selected input timeline image to detect patterns in the plurality of historical timeline images;
program instructions to, in response to receiving a feedback, adjust a chart setting to evaluate the one or more input timeline images, wherein the chart setting comprises the one or more chart evaluation factors; and
program instructions to apply the adjusted chart setting to select further input timeline images, from the one or more input timeline images, for training the cognitive system in the deep event learning process,
wherein the program instructions to determine the score include program instructions to determine a highest score among scores in the quantitative metric associated with the one or more chart evaluation factors.
8 . The computer system of claim 7 , wherein the program instructions to provide the one or more chart evaluation factors include program instructions to determine the one or more chart evaluation factors based on at least one of a chart type, scaling, color, marker shape, and data density.
9 . The computer system of claim 7 , wherein the program instructions to determine the score include program instructions to aggregate scores in the quantitative metric associated with the one or more chart evaluation factors.