A method of, and system for, estimating a consumption value of a consumption device from a set of consumption devices (CDs). The method includes storing in a memory of a computer a model indicative of dependency between consumption relationships of CDs from the set of CDs, and transforming the operational measured value into estimated consumption values of one or more CDs, wherein transforming is provided by a processor operatively connected to the memory using the model stored in the memory.

A machine-learning ecosystem includes a correlation module for building at least one prediction model based on at least one data input including at least one input parameter and at least one output parameter, the prediction model relating the output parameter to the input parameter. The correlation module performs at least one threshold check on the prediction model to assess the robustness of the prediction model. The ecosystem further includes a decision module communicatively coupled to the correlation module and receiving the prediction model from the correlation module. Based on a verification check at the decision module, a confirmation, a deferral, or a rejection of the prediction model is sent from the decision module to the correlation module.

A machine-learning ecosystem includes a correlation module for building at least one prediction model based on at least one data input including at least one input parameter and at least one output parameter, the prediction model relating the output parameter to the input parameter. The correlation module performs at least one threshold check on the prediction model to assess the robustness of the prediction model. The ecosystem further includes a decision module communicatively coupled to the correlation module and receiving the prediction model from the correlation module. Based on a verification check at the decision module, a confirmation, a deferral, or a rejection of the prediction model is sent from the decision module to the correlation module.

The present disclosure describes transaction-enabling systems and methods. A system can include a controller and a fleet of machines, each having at least one of a compute task requirement, a networking task requirement, and an energy consumption task requirement. The controller may include a resource requirement circuit to determine an amount of a resource for each of the machines to service the task requirement for each machine, a forward resource market circuit to access a forward resource market, and a resource distribution circuit to execute an aggregated transaction of the resource on the forward resource market.

The present disclosure describes transaction-enabling systems and methods. A system can include a controller and a fleet of machines, each having at least one of a compute task requirement, a networking task requirement, and an energy consumption task requirement. The controller may include a resource requirement circuit to determine an amount of a resource for each of the machines to service the task requirement for each machine, a forward resource market circuit to access a forward resource market, and a resource distribution circuit to execute an aggregated transaction of the resource on the forward resource market.

Embodiments of the present invention describe predictively reconstructing a physical event using augmented reality. Embodiments describe, identifying relative states of objects located in a physical event area by using video analysis to analyze collected video feeds from the physical event area before and after a physical event involving at least one of the objects, creating a knowledge corpus including the video analysis and the collected video feeds associated with the physical event and historical information, and capturing data, by a computing device, of the physical event area. Additionally, embodiments describe identifying possible precursor events based on the captured data and the knowledge corpus, and generating a virtual reconstruction of the physical event using the possible precursor events, displaying, by the computing device, the generated virtual reconstruction of the predicted physical event, wherein the displayed virtual reconstruction of the predicted physical event overlays an image of the physical event area.

Aspects of the present disclosure relate to data visualization, and more specifically, to technology that automatically visualizes various analytics and predictions generated for mass participation endurance events, or other mass participation events of interest.

Aspects of the present disclosure relate to data visualization, and more specifically, to technology that automatically visualizes various analytics and predictions generated for mass participation endurance events, or other mass participation events of interest.

A system and method for constructing an improved computing model that preserves use rights for data utilized by the model. A first dataset is accessed to build a computing model. The first data set is subject to terminable usage rights provisions. A portion of the first dataset is sampled to generate a second dataset. Vectors present in the first dataset and the second dataset are discretized. In response to determine that the usage rights associated with the primary dataset have been terminated, a coverage depletion for the second dataset is computed based on the usage rights termination associated with the first dataset. An estimated mean time to coverage failure for the first model based on the depletion coverage is determined for the second dataset. One or more data points are removed from the first dataset due to the termination of usage rights.

A system and method for constructing an improved computing model that preserves use rights for data utilized by the model. A first dataset is accessed to build a computing model. The first data set is subject to terminable usage rights provisions. A portion of the first dataset is sampled to generate a second dataset. Vectors present in the first dataset and the second dataset are discretized. In response to determine that the usage rights associated with the primary dataset have been terminated, a coverage depletion for the second dataset is computed based on the usage rights termination associated with the first dataset. An estimated mean time to coverage failure for the first model based on the depletion coverage is determined for the second dataset. One or more data points are removed from the first dataset due to the termination of usage rights.