A system comprising: a global monitor configured to provide a data analytics service, the global monitor comprising: a global interface component configured to communicate with a plurality of sensors over a wide area network; and a global learner configured to perform an iterative process to build a global model for the data analytics service that maps elements in the global model to labels based on data reported by the sensors; for at least one iteration of the iterative process, the global learner is configured to generate a query for the data from the sensors, to select at least one candidate element from the elements in the global model, wherein the at least one candidate element is an element of the global model for which the global learner wants to obtain data from the sensors, wherein the selection of the at least one candidate element is based on: a contribution of the at least one candidate element to improve the global model, wherein the contribution is assessed based on an error risk estimate of the global model when the at least one candidate element is augmented to the global model; and an impact on the wide area network resource utilization caused by the sensors reporting the data targeted to the at least one candidate element in the global model, to generate a global policy specifying that the data requested from the sensors is limited to the data targeted to the at least one candidate element in the global model, to send the query indicating the global policy to the sensors through the global interface component, to receive the data targeted to the at least one candidate element in the global model from the sensors through the global interface component according to the global policy, and to adjust the global model based on the data targeted to the at least one candidate element in the global model. a global interface component configured to communicate with a plurality of sensors over a wide area network; and a global learner configured to perform an iterative process to build a global model for the data analytics service that maps elements in the global model to labels based on data reported by the sensors; for at least one iteration of the iterative process, the global learner is configured to generate a query for the data from the sensors, to select at least one candidate element from the elements in the global model, wherein the at least one candidate element is an element of the global model for which the global learner wants to obtain data from the sensors, wherein the selection of the at least one candidate element is based on: a contribution of the at least one candidate element to improve the global model, wherein the contribution is assessed based on an error risk estimate of the global model when the at least one candidate element is augmented to the global model; and an impact on the wide area network resource utilization caused by the sensors reporting the data targeted to the at least one candidate element in the global model, a contribution of the at least one candidate element to improve the global model, wherein the contribution is assessed based on an error risk estimate of the global model when the at least one candidate element is augmented to the global model; and an impact on the wide area network resource utilization caused by the sensors reporting the data targeted to the at least one candidate element in the global model, to generate a global policy specifying that the data requested from the sensors is limited to the data targeted to the at least one candidate element in the global model, to send the query indicating the global policy to the sensors through the global interface component, to receive the data targeted to the at least one candidate element in the global model from the sensors through the global interface component according to the global policy, and to adjust the global model based on the data targeted to the at least one candidate element in the global model.