Data processing systems and methods, according to various embodiments, are adapted for determining an applicable privacy policy based on various criteria associated with a user and the associated product or service. User and product criteria may be obtained automatically and/or based on user input and analyzed by a privacy policy rules engine to determine the applicable policy. Text from the applicable policy can then be presented to the user. A default policy can be used when no particular applicable policy can be identified using by the rules engine. Policies may be ranked or prioritized so that a policy can be selected in the event the rules engine identifies two, conflicting policies based on the criteria.

A method can be used to predict risk using machine learning models having efficient feature learning. A risk prediction model can be applied to time-series data associated with a target entity to generate a risk indicator. The risk prediction model can include a feature learning model for generating features from the time-series data. The risk prediction model can also include a risk classification model for generating the risk indicator. The feature learning model can include filters and can be trained. Parameters of the risk prediction model can be adjusted to minimize a loss function associated with risk indicators. An updated risk prediction model can be generated by removing a filter from an original set of filters based on influencing scores of the original filters. The risk indicator can be transmitted to a computing device for use in controlling access of the target entity to a computing environment.

Techniques and solutions are described for facilitating the use of machine learning techniques. In some cases, filters can be defined for multiple segments of a training data set. Model segments corresponding to respective segments can be trained using an appropriate subset of the training data set. When a request for a machine learning result is made, filter criteria for the request can be determined and an appropriate model segment can be selected and used for processing the request. One or more hyperparameter values can be defined for a machine learning scenario. When a machine learning scenario is selected for execution, the one or more hyperparameter values for the machine learning scenario can be used to configure a machine learning algorithm used by the machine learning scenario.

Embodiments of systems and methods for the aggregation, analysis, display and monetization of pricing data for commodities in general, and which may be particularly useful applied to vehicles are disclosed. Specifically, in certain embodiments, historical transaction data associated with a particular vehicle configuration may be obtained and processed to determine pricing data associated with the vehicle configuration. The historical transaction data or determined pricing data may then be presented in an intuitive manner.

Embodiments of systems and methods for the aggregation, analysis, display and monetization of pricing data for commodities in general, and which may be particularly useful applied to vehicles are disclosed. Specifically, in certain embodiments, historical transaction data associated with a particular vehicle configuration may be obtained and processed to determine pricing data associated with the vehicle configuration. The historical transaction data or determined pricing data may then be presented in an intuitive manner.

Exemplary embodiments provide a transportation staffing management system. An amount of transportations miles are forecasted for delivering inventory from a distribution enter to a store based on a sales forecast for a store. Non-driving time for drivers engaged in delivering inventory to a store is tracked based on data collected in real-time from mobile computing devices associated with delivery vehicles. An amount of time needed to deliver the inventory based on the forecasted amount of transportation miles and non-driving is calculated. An optimal transportation workload is generated for the distribution center based on the amount of time, data from a first database storing data from a central office, and data from a second database storing data from a distribution center.

Exemplary embodiments provide a transportation staffing management system. An amount of transportations miles are forecasted for delivering inventory from a distribution enter to a store based on a sales forecast for a store. Non-driving time for drivers engaged in delivering inventory to a store is tracked based on data collected in real-time from mobile computing devices associated with delivery vehicles. An amount of time needed to deliver the inventory based on the forecasted amount of transportation miles and non-driving is calculated. An optimal transportation workload is generated for the distribution center based on the amount of time, data from a first database storing data from a central office, and data from a second database storing data from a distribution center.

A computer-implemented method, computer program product and computing system for receiving a complex task; processing the complex task to define a plurality of discrete tasks each having a discrete goal; executing the plurality of discrete tasks on a plurality of machine-accessible public computing platforms; determining if any of the plurality of discrete tasks failed to achieve its discrete goal; and if a specific discrete task failed to achieve its discrete goal, defining a substitute discrete task having a substitute discrete goal.

A computer-implemented method, computer program product and computing system for receiving a complex task; processing the complex task to define a plurality of discrete tasks each having a discrete goal; executing the plurality of discrete tasks on a plurality of machine-accessible public computing platforms; determining if any of the plurality of discrete tasks failed to achieve its discrete goal; and if a specific discrete task failed to achieve its discrete goal, defining a substitute discrete task having a substitute discrete goal.

Well management includes receiving, from a user computing system, a simulation job request for simulating well management on the cloud computing system including compute nodes, and obtaining, for the simulation job request, search spaces for completion stage simulations, fracture stage simulations, and production stage simulations. Well management further includes orchestrating, using the search spaces, the completion stage simulations, the fracture stage simulations, and the production stage simulations on the cloud computing system to obtain at least one optional well plan, and sending the at least one optional well plan to the user computer system.