Provided is a process of configuring a dashboard of a graphical user interface, the process including: obtaining identifiers of metrics; obtaining features that are properties of the metrics; accessing in a graph-effectiveness matrix effectiveness scores corresponding to the features; selecting a plurality of graphs to graphically represent the metrics in a dashboard; and instructing a computing device to display the dashboard.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of configuring a dashboard of a graphical user interface, the method comprising: obtaining, with one or more processors, a plurality of identifiers of a respective plurality of metrics of data, the metrics being different respective measurements based on the data, the data having values to be visualized in one or more graphs that graphically represent the metrics of the data in a dashboard; obtaining, with one or more processors, respective features of the metrics, the features being properties of the metrics, at least some features being properties of the respective metrics that are independent of values of the respective metrics; accessing, with one or more processors, in a graph-effectiveness matrix, for each of a plurality of candidate graphs, respective effectiveness scores corresponding to the obtained features of the metrics, the graph-effectiveness matrix comprising: a first dimension corresponding to a set of candidate graphs to graphically represent metrics of the data, a second dimension corresponding to a set of features of metrics, the set of features being a superset of at least some of the obtained features of the metrics, and effectiveness scores as values of the graph-effectiveness matrix corresponding to an inferred effectiveness of the respective candidate graph at representing metrics having the respective feature of metrics, wherein effectiveness scores are determined by training a machine-learning model with a training set correlating indicia of effectiveness to previous graphs of metrics with features among the feature dimension set of features; selecting, with one or more processors, at least one graph to graphically represent the metrics of the data in a dashboard from among the candidate graphs based on the accessed effectiveness scores; and instructing, with one or more processors, a computing device to display the dashboard having the at least one graph. 2. The method of claim 1, wherein: the data includes more than 10 megabytes of server log entries; the identifiers of metrics include identifiers of rankings of Internet Protocol (IP) addresses in the log entries, geolocations of IP addresses in the log entries, response times by IP address geolocation, and time-stamped counts of transport protocol response codes emitted within a trailing duration; the method comprises determining values of the identified metrics based on the server log entries; obtaining respective features of the metrics includes determining: that the ranking of IP addresses metric has a feature of being bivariate, that both the geolocations-of-IP-addressees metric and the response-times-by-IP-address-geolocation metric have a feature of being a geographic metric, and that the time-stamped-counts-of-transport-protocol-response-codes metric has both a feature of being a time-series and the feature of being bivariate; the training set comprises effectiveness scores obtained by presenting graphs having features among the set of features in the graph-effectiveness matrix to a user, receiving user-provided effectiveness scores, and storing the user-provided effectiveness scores in association with identifiers of features of the presented graphs; the method comprises, before obtaining the data: determining effectiveness scores based on the training set, receiving user feedback on previous dashboards, and adjusting the effectiveness scores based on the user feedback; selecting at least one graph comprises selecting a plurality of graphs by: ranking at least some of the candidate graphs based on respective effectiveness scores of features of the identified metrics, and selecting at least four graphs based on the ranking; and the method comprises determining positions of the at least four graphs in the dashboard based on the ranking. 3. The method of claim 1, comprising: selecting graphs for a plurality of customized dashboards for a plurality of users based on both features of metrics displayed in the customized dashboards and features of values of the metrics displayed in the customized dashboards, the customized dashboards each having a respective set of a plurality of graphs, the customized dashboards having different sets of graphs from one another. 4. The method of claim 1, wherein obtaining a plurality of respective identifiers of a plurality of metrics of the data comprises: selecting the metrics based on unsupervised machine-learning analysis of the data. 5. The method of claim 1, comprising: forming at least part of the training set by analyzing images of previously formed graphs with a computer vision algorithm to infer effectiveness scores based on visual attributes of the images. 6. The method of claim 1, comprising: training the machine learning model on a training set comprising human-provided scoring of the effectiveness of previously displayed graphs for metrics having features among the feature dimension of the graph-effectiveness matrix. 7. The method of claim 1, comprising: obtaining a role in an organization of a user for whom the dashboard is to be displayed; and training the machine learning model on a training set comprising feedback on previously displayed graphs from other users in the role. 8. The method of claim 1, comprising: selecting a plurality of graphs based on a role of a user in an organization and preferences previously provided by the user adjusting a default set of graphs for the role. 9. The method of claim 1, comprising: selecting the at least one graph based on: a list of permitted data, metrics, or graphs indicated in a policy record for a user for whom the dashboard is to be displayed; data indicative of preferences of the user provided before obtaining the data; and a task in which the user is engaged. 10. The method of claim 1, comprising: after instructing the computing device to display the dashboard, receiving a user comment associated with a graph among the selected at least one graph; storing the user comment in association with a template for the dashboard; receiving a request to share the dashboard with another user; sending the template among instructions to another computing device to display the dashboard for the other user, wherein displaying the dashboard for the other user includes displaying the user comment. 11. The method of claim 1, comprising: before selecting the at least one graph: displaying a plurality of previous dashboards to a user for whom the at least on graph is selected; measuring user interaction with the previous dashboards; and selecting the plurality of graphs based on the measurements. 12. The method of claim 11, wherein measuring user interaction comprises: tracking the user's eye position to measure gaze time on different graphs in the previous dashboards. 13. The method of claim 11, wherein measuring user interaction comprises: logging user interactions with the previous dashboards; and detecting, in the logged user interactions, a correlation between a given feature of values of a metric displayed in the previous dashboards and a user interaction to request a different graph, wherein selecting the at least one graph based on the measurement comprises selecting the different graph in response to detecting the given feature in values of the obtained identified metrics. 14. The method of claim 11, comprising: selecting a plurality of graphs from among the candidate graphs based on the accessed effectiveness scores; determining positions of the selected graphs in the dashboard based on the measurements. 15. The method of claim 1, wherein: instructing a computing device to display the dashboard comprises: sending a template to the computing device from a first server at a first network domain, the template indicating the selected at least one graph; and causing the computing device to retrieve at least some values of the metrics displayed in the selected at least one graph from a second server at a second network domain that is different from the first network domain, wherein at least some of the values of the metrics displayed in the selected graphs are not provided to the first server at the first network domain. 16. The method of claim 1, comprising: training the machine-learning model by: obtaining initial effectiveness score values of the graph-effectiveness matrix, and iteratively, until a termination condition is determined to have occurred: determining an aggregate measure of fitness or error of a current iteration of effectiveness score values of the graph-effectiveness matrix based a differences between graph effectiveness indicated by at least part of the training set and based on graph effectiveness indicated by the current iteration of effectiveness score values, and adjusting the effectiveness score values to reduce the aggregate measure of error or increase the aggregate measure of fitness, wherein determining the terminal condition has occurred comprises a determination that an amount of change in the effectiveness score values is less than a threshold, a determination that a threshold amount of iterations have been performed, or both. 17. The method of claim 1, comprising: steps for training the machine-learning model with the training set, wherein selecting the at least one graph comprises: steps for selecting a plurality of graphs to graphically represent metrics in a dashboard. 18. The method of claim 1, wherein at least some of the data and at least some values of the metrics depicted in the dashboard are withheld from a computer system selecting the at least one graph. 19. A system, comprising: one or more processors; and memory storing instructions that when executed by at least some of the processors effectuate operations comprising: obtaining a plurality of identifiers of a respective plurality of metrics of data, the metrics being different respective measurements based on the data, the data having values to be visualized in one or more graphs that graphically represent the metrics of the data in a dashboard; obtaining respective features of the metrics, the features being properties of the metrics, at least some features being properties of the respective metrics that are independent of values of the respective metrics; accessing, in a graph-effectiveness matrix, for each of a plurality of candidate graphs, respective effectiveness scores corresponding to the obtained features of the metrics, the graph-effectiveness matrix comprising: a first dimension corresponding to a set of candidate graphs to graphically represent metrics of the data, a second dimension corresponding to a set of features of metrics, the set of features being a superset of the obtained features of the metrics, and effectiveness scores as values of the graph-effectiveness matrix corresponding to an inferred effectiveness of the respective candidate graph at representing metrics having the respective feature of metrics, wherein effectiveness scores are determined by training a machine-learning model with a training set correlating indicia of effectiveness to previous graphs of metrics with features among the feature dimension set of features; selecting at least one graph to graphically represent the metrics of the data in a dashboard from among the candidate graphs based on the accessed effectiveness scores; and instructing a computing device to display the dashboard having the at least one graph. 20. The system of claim 19, the operations comprising: selecting graphs for a plurality of customized dashboards for a plurality of users based on both features of metrics displayed in the customized dashboards and features of values of the metrics displayed in the customized dashboards, the customized dashboards each having a respective set of a plurality of graphs, the customized dashboards having different sets of graphs from one another. 21. The system of claim 19, the operations comprising: obtaining a role in an organization of a user for whom the dashboard is to be displayed; and training the machine learning model on a training set comprising feedback on previously displayed graphs from other users in the role. 22. The system of claim 19, the operations comprising: before selecting the at least one graph: displaying a plurality of previous dashboards to a user for whom plurality of graphs is selected; measuring user interaction with the previous dashboards; and selecting the at least one graph based on the measurements. 23. The system of claim 22, wherein measuring user interaction comprises: logging user interactions with the previous dashboards; and detecting, in the logged user interactions, a correlation between a given feature of values of a metric displayed in the previous dashboards and a user interaction to request a different graph, wherein selecting the at least one graph based on the measurement comprises selecting the different graph in response to detecting the given feature in values of the obtained identified metrics. 24. The system of claim 19, wherein: instructing a computing device to display the dashboard comprises: sending a template to the computing device from a first server at a first network domain, the template indicating the selected at least one graph; causing the computing device to retrieve at least some values of the metrics displayed in the selected at least one graph from a second server at a second network domain that is different from the first network domain, wherein at least some of the values of the metrics displayed in the selected at least one graph are not provided to the first server at the first network domain.