Intelligent visualization munging
First Claim
1. An intelligent visualization munging system comprising:
- a data loader, executed by at least one hardware processor, to ascertain data that is to be visualized;
a data iterator, executed by the at least one hardware processor, to transform and enrich the ascertained data;
a data feature and role discoverer, executed by the at least one hardware processor, todetermine features of the transformed and enriched data,determine a user role of a user associated with the transformed and enriched data, and a user interaction of the user associated with the transformed and enriched data, wherein the user is a person or entity that is currently using or is associated with the ascertained data, anddetermine a target role of a target associated with the transformed and enriched data, and a target interaction of the target associated with the transformed and enriched data;
a behavior learner, executed by the at least one hardware processor, tolearn a behavior of the user associated with the transformed and enriched data;
a visualization recommender, executed by the at least one hardware processor, toanalyze the features of the transformed and enriched data, the user role of the user associated with the transformed and enriched data, the user interaction of the user associated with the transformed and enriched data, the target role of the target associated with the transformed and enriched data, the target interaction of the target associated with the transformed and enriched data, and a learned behavior model to generate a recommendation that includes a predetermined number of visualizations from a plurality of available visualizations to graphically display the transformed and enriched data, wherein the predetermined number of visualizations is less than the plurality of available visualizations,receive selection of a visualization by the user from the predetermined number of recommended visualizations where the selected visualization is recommended for the target that includes another person or another entity that is to use or is associated with the selected visualization, andgenerate a new learned behavior model based on the learned behavior of the user associated with the transformed and enriched data bydetermining whether a number of selections of non-recommended visualizations exceeds a predetermined selection number threshold within a predetermined time threshold, andin response to a determination that the number of selections of non-recommended visualizations exceeds the predetermined selection number threshold within the predetermined time thresholdapplying multinomial logistic regression to the features of the transformed and enriched data, at least one of the user role of the user associated with the transformed and enriched data, or the user interaction of the user associated with the transformed and enriched data, at least one of the target role of the target associated with the transformed and enriched data, or the target interaction of the target associated with the transformed and enriched data, and the learned behavior of the user associated with the transformed and enriched data, wherein the learned behavior of the user associated with the transformed and enriched data includes a selection of a non-recommended visualization; and
a results generator, executed by the at least one hardware processor, to generate a graphical display of the transformed and enriched data using the selected visualization from the predetermined number of recommended visualizations.
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Abstract
According to examples, intelligent visualization munging may include transforming and enriching data that is to be visualized, determining features of the transformed and enriched data, determining a user role of a user associated with the transformed and enriched data, and a user interaction of the user. Intelligent visualization munging may further include learning a behavior of the user, and analyzing the features, the user role, the user interaction, and a learned behavior model to generate a recommendation that includes a predetermined number of visualizations from a plurality of available visualizations to display the transformed and enriched data. The predetermined number of visualizations is less than the plurality of available visualizations.
20 Citations
19 Claims
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1. An intelligent visualization munging system comprising:
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a data loader, executed by at least one hardware processor, to ascertain data that is to be visualized; a data iterator, executed by the at least one hardware processor, to transform and enrich the ascertained data; a data feature and role discoverer, executed by the at least one hardware processor, to determine features of the transformed and enriched data, determine a user role of a user associated with the transformed and enriched data, and a user interaction of the user associated with the transformed and enriched data, wherein the user is a person or entity that is currently using or is associated with the ascertained data, and determine a target role of a target associated with the transformed and enriched data, and a target interaction of the target associated with the transformed and enriched data; a behavior learner, executed by the at least one hardware processor, to learn a behavior of the user associated with the transformed and enriched data; a visualization recommender, executed by the at least one hardware processor, to analyze the features of the transformed and enriched data, the user role of the user associated with the transformed and enriched data, the user interaction of the user associated with the transformed and enriched data, the target role of the target associated with the transformed and enriched data, the target interaction of the target associated with the transformed and enriched data, and a learned behavior model to generate a recommendation that includes a predetermined number of visualizations from a plurality of available visualizations to graphically display the transformed and enriched data, wherein the predetermined number of visualizations is less than the plurality of available visualizations, receive selection of a visualization by the user from the predetermined number of recommended visualizations where the selected visualization is recommended for the target that includes another person or another entity that is to use or is associated with the selected visualization, and generate a new learned behavior model based on the learned behavior of the user associated with the transformed and enriched data by determining whether a number of selections of non-recommended visualizations exceeds a predetermined selection number threshold within a predetermined time threshold, and in response to a determination that the number of selections of non-recommended visualizations exceeds the predetermined selection number threshold within the predetermined time threshold applying multinomial logistic regression to the features of the transformed and enriched data, at least one of the user role of the user associated with the transformed and enriched data, or the user interaction of the user associated with the transformed and enriched data, at least one of the target role of the target associated with the transformed and enriched data, or the target interaction of the target associated with the transformed and enriched data, and the learned behavior of the user associated with the transformed and enriched data, wherein the learned behavior of the user associated with the transformed and enriched data includes a selection of a non-recommended visualization; and a results generator, executed by the at least one hardware processor, to generate a graphical display of the transformed and enriched data using the selected visualization from the predetermined number of recommended visualizations. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A method for intelligent visualization munging, the method comprising:
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transforming and enriching, by at least one hardware processor, data that is to be visualized; determining, by the at least one hardware processor, features of the transformed and enriched data; determining, by the at least one hardware processor, a user role of a user associated with the transformed and enriched data, and a user interaction of the user associated with the transformed and enriched data, wherein the user is a person or entity that is currently using or is associated with the data that is to be visualized; determining, by the at least one hardware processor, a target role of a target associated with the transformed and enriched data, and a target interaction of the target associated with the transformed and enriched data; learning, by the at least one hardware processor, a behavior of the user associated with the transformed and enriched data; analyzing, by the at least one hardware processor, the features of the transformed and enriched data, the user role of the user associated with the transformed and enriched data, the user interaction of the user associated with the transformed and enriched data, the target role of the target associated with the transformed and enriched data, the target interaction of the target associated with the transformed and enriched data, and a learned behavior model; generating, by the at least one hardware processor, based on the analysis of the features of the transformed and enriched data, the user role of the user associated with the transformed and enriched data, the user interaction of the user associated with the transformed and enriched data, the target role of the target associated with the transformed and enriched data, the target interaction of the target associated with the transformed and enriched data, and the learned behavior model, a recommendation that includes a predetermined number of visualizations from a plurality of available visualizations to graphically display the transformed and enriched data, wherein the predetermined number of visualizations is less than the plurality of available visualizations; identifying, by the at least one hardware processor, based on the analysis of the features of the transformed and enriched data, the user role of the user associated with the transformed and enriched data, the user interaction of the user associated with the transformed and enriched data, the target role of the target associated with the transformed and enriched data, the target interaction of the target associated with the transformed and enriched data, and the learned behavior model, an anomaly in displays of the recommended visualizations; receiving, by the at least one hardware processor, selection of a visualization by the user from the predetermined number of recommended visualizations where the selected visualization is recommended for the target that includes another person or another entity that is to use or is associated with the selected visualization; generating, by the at least one hardware processor, a new learned behavior model based on the learned behavior of the user associated with the transformed and enriched data by determining whether a number of selections of non-recommended visualizations exceeds a predetermined selection number threshold within a predetermined time threshold, and in response to a determination that the number of selections of non-recommended visualizations exceeds the predetermined selection number threshold within the predetermined time threshold applying multinomial logistic regression to the features of the transformed and enriched data, at least one of the user role of the user associated with the transformed and enriched data, or the user interaction of the user associated with the transformed and enriched data, at least one of the target role of the target associated with the transformed and enriched data, or the target interaction of the target associated with the transformed and enriched data, and the learned behavior of the user associated with the transformed and enriched data, wherein the learned behavior of the user associated with the transformed and enriched data includes a selection of a non-recommended visualization. - View Dependent Claims (10, 11, 12, 13, 14, 15)
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16. A non-transitory computer readable medium having stored thereon machine readable instructions to provide intelligent visualization munging, the machine readable instructions, when executed, cause a processor to:
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transform and enrich data that is to be visualized; determine features of the transformed and enriched data; determine at least one of a user role of a user associated with the transformed and enriched data, or a user interaction of the user associated with the transformed and enriched data, wherein the user is a person or entity that is currently using or is associated with the data that is to be visualized, and at least one of a target role of a target associated with the transformed and enriched data, or a target interaction of the target associated with the transformed and enriched data; learn a behavior of the user associated with the transformed and enriched data; analyze the features of the transformed and enriched data, the at least one of the user role of the user associated with the transformed and enriched data, or the user interaction of the user associated with the transformed and enriched data, the at least one of the target role of the target associated with the transformed and enriched data, or the target interaction of the target associated with the transformed and enriched data, and a learned behavior model; generate, based on the analysis of the features of the transformed and enriched data, the at least one of the user role of the user associated with the transformed and enriched data, or the user interaction of the user associated with the transformed and enriched data, the at least one of the target role of the target associated with the transformed and enriched data, or the target interaction of the target associated with the transformed and enriched data, and the learned behavior model, a recommendation that includes a predetermined number of visualizations from a plurality of available visualizations to graphically display the transformed and enriched data, wherein the predetermined number of visualizations is less than the plurality of available visualizations; receive selection of a visualization by the user from the predetermined number of recommended visualizations where the selected visualization is recommended for the target that includes another person or another entity that is to use or is associated with the selected visualization; and generate a new learned behavior model based on the learned behavior of the user associated with the transformed and enriched data by determining whether a number of selections of non-recommended visualizations exceeds a predetermined selection number threshold within a predetermined time threshold, and in response to a determination that the number of selections of non-recommended visualizations exceeds the predetermined selection number threshold within the predetermined time threshold applying multinomial logistic regression to the features of the transformed and enriched data, the at least one of the user role of the user associated with the transformed and enriched data, or the user interaction of the user associated with the transformed and enriched data, the at least one of the target role of the target associated with the transformed and enriched data, or the target interaction of the target associated with the transformed and enriched data, and the learned behavior of the user associated with the transformed and enriched data, wherein the learned behavior of the user associated with the transformed and enriched data includes a selection of a non-recommended visualization. - View Dependent Claims (17, 18, 19)
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Specification