Abstract: Disclosed herein are systems, devices, and methods related to assets and predictive models and corresponding workflows that are related to the operation of assets. In particular, examples involve defining and deploying aggregate, predictive models and corresponding workflows, defining and deploying individualized, predictive models and/or corresponding workflows, and dynamically adjusting the execution of model-workflow pairs.

Abstract: Disclosed herein is an improved local analytics device that includes a single-board computer with a high-capacity processing unit, a remote network interface configured to wirelessly communicate with a remote computing system, a local network interface configured to wirelessly communicate with a remote computing system, a secondary power source, and an asset interface that may include (i) a communication connector that enables the local analytics device to be coupled to the asset's on-board systems via a single cable that carries both data and power, (ii) an asset communication subsystem configured to manage data communication with an asset's on-board systems, and (iii) a power management subsystem configured to receive and manage power from the asset's on-board systems.

Abstract: Disclosed herein are systems, devices, and methods related to assets and predictive models and corresponding workflows that are related to the operation of assets. In particular, examples involve assets configured to receive and locally execute predictive models, locally individualize predictive models, and/or locally execute workflows or portions thereof.

Abstract: Disclosed herein are systems, devices, and methods related to assets and predictive models and corresponding workflows that are related to updating a routing table. In particular, examples involve based on a predictive model, determining that a given asset of a plurality of assets in a mesh network is likely to be unavailable within a given period of time in the future and in response to the determining, causing a routing configuration for at least one other asset in the mesh network to be updated.

Abstract: To distribute execution of a predictive model between multiple data analytics platforms, a first platform may be provisioned with a set of precursor detection models and a second platform may be provisioned with a set of precursor analysis models. Based on a given precursor detection model, the first platform may detect an occurrence of a given type of precursor event at a given asset and send data associated with the occurrence to the second platform. In response, the second platform may (a) identify at least one precursor analysis model that is associated with the given type of precursor event and predicts whether a given type of problem is present at an asset and (b) execute the at least one precursor analysis model to perform a deeper analysis of the occurrence and thereby output a prediction of whether the given type of problem is present at the given asset.

Abstract: Disclosed herein are systems, devices, and methods for provisioning a local analytics device to interact with a remote computing system on behalf of an asset that is coupled to the local analytics device and that is associated with a particular customer account hosted by the remote computing system.

Abstract: Disclosed herein are systems, devices, and methods related to assets and predictive models and corresponding workflows that are related to the operation of assets. In particular, examples involve defining and deploying aggregate, predictive models and corresponding workflows, defining and deploying individualized, predictive models and/or corresponding workflows, and dynamically adjusting the execution of model-workflow pairs.

Abstract: Disclosed herein are systems, devices, and methods related to assets and predictive models and corresponding workflows that are related to the operation of assets. In particular, examples involve assets configured to receive and locally execute predictive models, locally individualize predictive models, and/or locally execute workflows or portions thereof.

Abstract: Disclosed herein are systems, devices, and methods related to assets and predictive models and corresponding workflows that are related to the operation of assets. In particular, examples involve assets configured to receive and locally execute predictive models, locally individualize predictive models, and/or locally execute workflows or portions thereof.

Abstract: Disclosed herein are systems, devices, and methods related to assets and predictive models and corresponding workflows that are related to the operation of assets. In particular, examples involve defining and deploying aggregate, predictive models and corresponding workflows, defining and deploying individualized, predictive models and/or corresponding workflows, and dynamically adjusting the execution of model-workflow pairs.

Abstract: Disclosed herein are systems, devices, and methods related to assets and predictive models and corresponding workflows that are related to the operation of assets. In particular, examples involve defining and deploying aggregate, predictive models and corresponding workflows, defining and deploying individualized, predictive models and/or corresponding workflows, and dynamically adjusting the execution of model-workflow pairs.

Abstract: Disclosed herein are systems, devices, and methods related to assets and predictive models and corresponding workflows that are related to updating a routing table. In particular, examples involve based on a predictive model, determining that a given asset of a plurality of assets in a mesh network is likely to be unavailable within a given period of time in the future and in response to the determining, causing a routing configuration for at least one other asset in the mesh network to be updated.

Abstract: Disclosed herein are systems, devices, and methods for provisioning a local analytics device to interact with a remote computing system on behalf of an asset that is coupled to the local analytics device and that is associated with a particular customer account hosted by the remote computing system.

Abstract: Disclosed herein are systems, devices, and methods for provisioning a local analytics device to interact with a remote computing system on behalf of an asset that is coupled to the local analytics device and that is associated with a particular customer account hosted by the remote computing system.

Abstract: Disclosed herein is an improved local analytics device that includes a single-board computer with a high-capacity processing unit, a remote network interface configured to wirelessly communicate with a remote computing system, a local network interface configured to wirelessly communicate with a remote computing system, a secondary power source, and an asset interface that may include (i) a communication connector that enables the local analytics device to be coupled to the asset's on-board systems via a single cable that carries both data and power, (ii) an asset communication subsystem configured to manage data communication with an asset's on-board systems, and (iii) a power management subsystem configured to receive and manage power from the asset's on-board systems.

Abstract: Disclosed herein are systems, devices, and methods related to assets and predictive models and corresponding workflows that are related to updating a routing table. In particular, examples involve based on a predictive model, determining that a given asset of a plurality of assets in a mesh network is likely to be unavailable within a given period of time in the future and in response to the determining, causing a routing configuration for at least one other asset in the mesh network to be updated.

Abstract: Disclosed herein are systems, devices, and methods related to assets and predictive models and corresponding workflows that are related to the operation of assets. In particular, examples involve defining and deploying aggregate, predictive models and corresponding workflows, defining and deploying individualized, predictive models and/or corresponding workflows, and dynamically adjusting the execution of model-workflow pairs.

Abstract: Disclosed herein are systems, devices, and methods related to assets and predictive models and corresponding workflows that are related to the operation of assets. In particular, examples involve defining and deploying aggregate, predictive models and corresponding workflows, defining and deploying individualized, predictive models and/or corresponding workflows, and dynamically adjusting the execution of model-workflow pairs.

Abstract: Disclosed herein are systems, devices, and methods related to assets and predictive models and corresponding workflows that are related to the operation of assets. In particular, examples involve defining and deploying aggregate, predictive models and corresponding workflows, defining and deploying individualized, predictive models and/or corresponding workflows, and dynamically adjusting the execution of model-workflow pairs.

Abstract: Disclosed herein are systems, devices, and methods related to assets and predictive models and corresponding workflows that are related to the operation of assets. In particular, examples involve assets configured to receive and locally execute predictive models, locally individualize predictive models, and/or locally execute workflows or portions thereof.