Abstract: In an environment where a plurality of data platforms are communicatively coupled via a communication network, two of more of the platforms may be configured to share asset-related information. For example, a first platform may receive asset-related data and determine that a portion of the received data should be pushed to another platform that is communicatively coupled to the first platform via the communication network. Based on that determination, the first platform may prepare a portion of the received data to be transmitted to another platform and then push the portion of the data to another platform over a network connection.

Abstract: In an environment where a plurality of data platforms are communicatively coupled via a communication network, one platform in the environment may govern whether other platforms in the environment are permitted to participate in the sharing of asset-related information. For example, a first platform may receive, from a second platform (e.g., a master or seed platform) that is communicatively coupled to the first platform via the communication network, criteria that governs whether the first platform is permitted to share asset-related data with one or more other platforms in the network. In turn, the first platform may assess the reliability of the platform's stored asset-related data and then apply the criteria to the assessed reliability to determine whether the first platform is permitted to share asset-related data with the one or more other platforms and then operate in accordance with that determination.

Abstract: A deployment system includes a plurality of deployment environments, a change-control server, and a deployment orchestrator. Each deployment environment carries out a given phase of a deployment process for a set of artifacts. The change-control server maintains branches that correspond to respective deployment environments and that store artifacts that have been deployed to the respective deployment environments. A manifest contains a given set of artifacts stored by the change-control server, and each branch may contain multiple versions of a manifest associated with that branch. Upon creation of a new manifest version on the change-control server, the deployment orchestrator detects the presence of the new manifest version and responsively determine the differences between (i) artifacts contained in the new manifest version and (ii) artifacts deployed to a given deployment environment.

Abstract: Disclosed herein is a computer architecture and software that is configured to modify data intake operation at an asset-monitoring system based on a predictive model. In accordance with the present disclosure, the asset-monitoring system may execute a predictive model that outputs an indicator of whether at least one event from a group of events (e.g., a failure event) is likely to occur at a given asset within a given period of time in the future. Based on the output of this predictive model, the asset-monitoring system may modify one or more operating parameters for ingesting data from the given asset, such as a storage location for the ingested data, a set of data variables from the asset that are ingested, and/or a rate at which data from the asset is ingested.

Abstract: A deployment system includes a plurality of deployment environments, a change-control server, and a deployment orchestrator. Each deployment environment carries out a given phase of a deployment process for a set of artifacts. The change-control server maintains branches that correspond to respective deployment environments and that store artifacts that have been deployed to the respective deployment environments. A manifest contains a given set of artifacts stored by the change-control server, and each branch may contain multiple versions of a manifest associated with that branch. Upon creation of a new manifest version on the change-control server, the deployment orchestrator detects the presence of the new manifest version and responsively determine the differences between (i) artifacts contained in the new manifest version and (ii) artifacts deployed to a given deployment environment.

Abstract: Disclosed herein is a computer architecture and software that is configured to modify data intake operation at an asset-monitoring system based on a predictive model. In accordance with the present disclosure, the asset-monitoring system may execute a predictive model that outputs an indicator of whether at least one event from a group of events (e.g., a failure event) is likely to occur at a given asset within a given period of time in the future. Based on the output of this predictive model, the asset-monitoring system may modify one or more operating parameters for ingesting data from the given asset, such as a storage location for the ingested data, a set of data variables from the asset that are ingested, and/or a rate at which data from the asset is ingested.

Abstract: Disclosed herein is a computer architecture and software that is configured to modify data intake operation at an asset-monitoring system based on a predictive model. In accordance with the present disclosure, the asset-monitoring system may execute a predictive model that outputs an indicator of whether at least one event from a group of events (e.g., a failure event) is likely to occur at a given asset within a given period of time in the future. Based on the output of this predictive model, the asset-monitoring system may modify one or more operating parameters for ingesting data from the given asset, such as a storage location for the ingested data, a set of data variables from the asset that are ingested, and/or a rate at which data from the asset is ingested.

Abstract: Disclosed herein is a computer architecture and software that is configured to modify data intake operation at an asset-monitoring system based on a predictive model. In accordance with the present disclosure, the asset-monitoring system may execute a predictive model that outputs an indicator of whether at least one event from a group of events (e.g., a failure event) is likely to occur at a given asset within a given period of time in the future. Based on the output of this predictive model, the asset-monitoring system may modify one or more operating parameters for ingesting data from the given asset, such as a storage location for the ingested data, a set of data variables from the asset that are ingested, and/or a rate at which data from the asset is ingested.

Abstract: Disclosed herein are systems, devices, and methods for sharing asset-related information between data platforms that are communicatively coupled via a network. According to an example, a first platform may receive asset-related data and determine that a portion of the received data should be pushed to another platform. Based on that determination, the first platform may prepare a portion of the received data to be transmitted to another platform and then push the portion of the data to another platform over a network connection.

Abstract: Disclosed herein are systems, devices, and methods for governing data platforms that are communicatively coupled via a network. According to an example, a first platform may receive, from a second platform (e.g., a master or seed platform), criteria that governs whether the first platform is permitted to share asset-related data with one or more other platforms in the network and assess the reliability of the platform's stored asset-related data. In turn, the first platform may apply the criteria to the assessed reliability to determine whether the first platform is permitted to share asset-related data with the one or more other platforms and then operate in accordance with that determination.