Abstract: A warehouse management system may receive a predictive analytics request associated with one or more warehouses and may, in response, input data associated with the one or more warehouses into a warehouse management model to determine one or more predictive analytics associated with the one or more warehouses, where the warehouse management model is trained via machine learning to determine the predictive analytics. The warehouse management system may perform simulations of operations of the one or more warehouses based on the one or more predictive analytics to determine one or more warehouse actions to meet one or more operational requirements. The warehouse management system may communicate the one or more warehouse actions to one or more devices associated with the one or more warehouses to enable the one or more devices to operate according to the one or more warehouse actions to meet the one or more operational requirements.

Abstract: In general, this disclosure describes media stream transmission techniques for a computing device. The computing device captures a first media item and identifies a primary portion of the first media item and a secondary portion of the first media item different than the primary portion. The computing device applies a first compression algorithm to the primary portion of the first media item to generate a compressed primary portion. The computing device applies a second compression algorithm to the secondary portion of the first media item to generate a compressed secondary portion, where a data compression ratio of the second compression algorithm is greater than a data compression ratio of the first compression algorithm. The computing device transmits, to a central computing device, the compressed primary portion of the first media item and the compressed secondary portion of the first media item.

Abstract: In general, this disclosure describes media stream transmission techniques for a computing device. The computing device may capture an image of a local background environment. The computing device may record a first media stream that includes at least a portion of the image of the background environment and at least one movement of at least one object through the background environment. The computing device may remove the image of the background environment from the first media stream to create a second media stream that includes the movement of the object without the image of the background environment. The computing device may determine a bandwidth of a network over which the second media stream will be transmitted and perform further alterations to the second media stream if the current bandwidth is less than a bandwidth threshold level in order to reduce the bandwidth needed to transmit the second media stream.

Abstract: An example method includes receiving one or more cross-platform system maintenance rules that are generated by and sent from a central server computing system, determining a type of software operating system currently being executed by a client computing system, determining, based on the one or more cross-platform system maintenance rules and further based on the type of software operating system, one or more platform-specific system maintenance rules that specify one or more platform-specific system maintenance operations and scheduling information, and determining, based on scheduling information identified by the one or more platform-specific system maintenance rules, a schedule indicating when to perform the one or more platform-specific system maintenance operations. The example method includes initiating performance of the one or more platform-specific system maintenance operations in accordance with the schedule, collecting one or more results, and generating and sending at least one summary report.

Abstract: In general, this disclosure describes media stream transmission techniques for a computing device. The computing device captures a first media item and identifies a primary portion of the first media item and a secondary portion of the first media item different than the primary portion. The computing device applies a first compression algorithm to the primary portion of the first media item to generate a compressed primary portion. The computing device applies a second compression algorithm to the secondary portion of the first media item to generate a compressed secondary portion, where a data compression ratio of the second compression algorithm is greater than a data compression ratio of the first compression algorithm. The computing device transmits, to a central computing device, the compressed primary portion of the first media item and the compressed secondary portion of the first media item.

Abstract: In general, this disclosure describes media stream transmission techniques for a computing device. The computing device may capture an image of a local background environment. The computing device may record a first media stream that includes at least a portion of the image of the background environment and at least one movement of at least one object through the background environment. The computing device may remove the image of the background environment from the first media stream to create a second media stream that includes the movement of the object without the image of the background environment. The computing device may determine a bandwidth of a network over which the second media stream will be transmitted and perform further alterations to the second media stream if the current bandwidth is less than a bandwidth threshold level in order to reduce the bandwidth needed to transmit the second media stream.

Abstract: An example method includes deploying, by a modular training system and on one or more virtual machines in a network, one or more training environments that are configured to execute one or more training exercises; deploying, by the modular training system and for execution by one or more remote host computing systems that are communicatively coupled to the network, one or more software agents that are executed during the one or more training exercises, wherein the one or more software agents are configured to collect parameter data from the one or more remote host computing systems while the one or more trainees perform actions during the training exercise; and receiving, by the modular training system and from the one or more remote host computing system via the network, the parameter data collected by the one or more software agents during execution of the one or more training exercises.

Abstract: An example method includes receiving one or more cross-platform system maintenance rules that are generated by and sent from a central server computing system, determining a type of software operating system currently being executed by a client computing system, determining, based on the one or more cross-platform system maintenance rules and further based on the type of software operating system, one or more platform-specific system maintenance rules that specify one or more platform-specific system maintenance operations and scheduling information, and determining, based on scheduling information identified by the one or more platform-specific system maintenance rules, a schedule indicating when to perform the one or more platform-specific system maintenance operations. The example method includes initiating performance of the one or more platform-specific system maintenance operations in accordance with the schedule, collecting one or more results, and generating and sending at least one summary report.