Abstract: In some examples, a computing device can include a processor resource and a non-transitory memory resource storing machine-readable instructions stored thereon that, when executed, cause the processor resource to: generate a model of activity for the computing device, determine a time period for performing a calculation based on the model, wherein the calculation utilizes performance metrics associated with the computing device, activate an agent at a start time of the time period to perform the calculation, send, by the agent, a result of the calculation to a remote computing device, and deactivate the agent in response to sending the result.

Abstract: The present subject matter relates to techniques of assigning a mobility factor to the user based on the spatial movement data. In one example, the technique may include four phases for assigning the mobility factors to the users. In a first phase, spatial movement data from a plurality of user devices assigned to a plurality of users may be received. In a second phase, spatial movement behavior for a set of user devices may be assessed using the received spatial movement data of the members of the set using machine learning techniques. In a third phase, user devices may be selected based on the assessed spatial movement behavior using machine learning techniques for reconfiguration. Finally, at a four phase, the selected user devices may be reconfigured.

Abstract: In some examples, a computing device can include a processor resource and a non-transitory memory resource storing machine-readable instructions stored thereon that, when executed, cause the processor resource to: generate a model of activity for the computing device, determine a time period for performing a calculation based on the model, wherein the calculation utilizes performance metrics associated with the computing device, activate an agent at a start time of the time period to perform the calculation, send, by the agent, a result of the calculation to a remote computing device, and deactivate the agent in response to sending the result.

Abstract: An example of an apparatus including a communication interface to receive raw data from a client device. The raw data is to be collected by the client device. The apparatus further includes a filtering engine to remove portions of the raw data to generate filtered data. The apparatus also includes an analysis engine to process the filtered data to identify an application that reduces an overall performance. The apparatus also includes a memory storage unit to store a database. The database includes the raw data, the filtered data, a client device identifier, and an application identifier associated with the application.

Abstract: The present subject matter relates to techniques of assigning a mobility factor to the user based on the spatial movement data. In one example, the technique may include four phases for assigning the mobility factors to the users. In a first phase, spatial movement data from a plurality of user devices assigned to a plurality of users may be received. In a second phase, spatial movement behavior for a set of user devices may be assessed using the received spatial movement data of the members of the set using machine learning techniques. In a third phase, user devices may be selected based on the assessed spatial movement behavior using machine learning techniques for reconfiguration. Finally, at a four phase, the selected user devices may be reconfigured.

Abstract: A system is provided including a memory in communication with a processor. The memory may store status data for a module of a device. The processor may generate a module score based on the status data, and generate a device score by applying a transformation to the module score. Moreover, the processor may assign the device to a status group based on the device score. The status group may include one of a healthy group and an unhealthy group. Furthermore, the processor may output the status group associated with an identifier of the device.