Abstract: Systems and methods described herein provide an online constraint optimizing service that evaluates user requests and inquiries for telecommunications services in the context of a real-time constraint-based analysis. According to an implementation, a network device receives a function for an analytic event and a constraint. The function applies different user attributes for a telecommunications network. The network device generates a training data set using offline constrained optimization of the function. The network device develops a predictive model for utilization of network resources in the telecommunications network using the training data set. The network device receives a user request that corresponds to the analytic event addressed by the predictive model and conducts an online prescriptive analysis using the predictive model. The network device optimizes allocation of the network resources to the user based on the prescriptive analysis.

Abstract: The present teaching relates to method, system, medium, and implementations for job pricing. When receiving information about a new task of a job type, a mixture model representing historic job completion data and including multiple cluster-based models is used to predict job pricing. The multiple cluster-based models characterize corresponding multiple clusters identified from historic job completion data and are used to generate multiple predictions of duration to complete the new task. The predictions generated based on the cluster-based models are integrated based on the mixture model to generate an overall job pricing estimate for the new task.

Abstract: Systems and methods described herein provide an online constraint optimizing service that evaluates user requests and inquiries for telecommunications services in the context of a real-time constraint-based analysis. According to an implementation, a network device receives a function for an analytic event and a constraint. The function applies different user attributes for a telecommunications network. The network device generates a training data set using offline constrained optimization of the function. The network device develops a predictive model for utilization of network resources in the telecommunications network using the training data set. The network device receives a user request that corresponds to the analytic event addressed by the predictive model and conducts an online prescriptive analysis using the predictive model. The network device optimizes allocation of the network resources to the user based on the prescriptive analysis.

Abstract: An approach for providing feature configuration management is described. A service controller platform, monitors at least one environmental parameter of a mobile device; determines an event according to a threshold, the event being based on at least a change in the at least one environmental parameter; determines a feature configuration setting associated with at least one of the threshold or the event according to a device profile, wherein the device profile specifies a feature configuration setting that includes at least one of locking the mobile device, limiting access to at least one predetermined application, or disabling the mobile device; and causes the mobile device to change a feature configuration according to the feature configuration setting.

Abstract: An approach for providing feature configuration management is described. A service controller platform, monitors at least one environmental parameter of a mobile device; determines an event according to a threshold, the event being based on at least a change in the at least one environmental parameter; determines a feature configuration setting associated with at least one of the threshold or the event according to a device profile, wherein the device profile specifies a feature configuration setting that includes at least one of locking the mobile device, limiting access to at least one predetermined application, or disabling the mobile device; and causes the mobile device to change a feature configuration according to the feature configuration setting.