Abstract: A computing system that includes one or more server computing devices including one or more processors configured to execute instructions for a domain extensibility module that provides software development tools for building domain extensions for a database platform, and a data ingestion module that provides software development tools for defining a metadata schema for extracting metadata from data files. The one or more processors are configured to receive a set of data from a user computing device, define a target metadata schema that includes one or more metadata fields that will be populated during a data ingestion process, define a target domain extension that defines one or more data types for storing the received set of data after performing the data ingestion process, and ingest the received set of data using a metadata extraction pipeline to generate metadata files based on the target metadata schema.

Abstract: A computing system that includes one or more server computing devices including one or more processors configured to execute instructions for a domain extensibility module that provides software development tools for building domain extensions for a database platform, and a data ingestion module that provides software development tools for defining a metadata schema for extracting metadata from data files. The one or more processors are configured to receive a set of data from a user computing device, define a target metadata schema that includes one or more metadata fields that will be populated during a data ingestion process, define a target domain extension that defines one or more data types for storing the received set of data after performing the data ingestion process, and ingest the received set of data using a metadata extraction pipeline to generate metadata files based on the target metadata schema.

Abstract: A computing system that includes one or more server computing devices including one or more processors configured to execute instructions for a domain extensibility module that provides software development tools for building domain extensions for a database platform, and a data ingestion module that provides software development tools for defining a metadata schema for extracting metadata from data files. The one or more processors are configured to receive a set of data from a user computing device, define a target metadata schema that includes one or more metadata fields that will be populated during a data ingestion process, define a target domain extension that defines one or more data types for storing the received set of data after performing the data ingestion process, and ingest the received set of data using a metadata extraction pipeline to generate metadata files based on the target metadata schema.

Abstract: A remote server computing system is configured to deploy a cloud-service-managed control plane and a cloud service data plane spanning the remote server computing system, a local edge computing device, and a local on-premises computing device connected in a hybrid cloud environment. Energy-related training data is received including a plurality of energy-related training data pairs. A machine learning function is trained using the plurality of training data pairs to predict a classified label for restricted energy-related data that is not accessible to the remote server computing system. The trained machine learning function is deployed to the one or more of the local edge computing device and the local on-premises computing device via the cloud service data plane. The remote server computing system is further configured to receive, via the cloud service data plane, classified output of the trained machine learning function.

Abstract: A computing system that includes one or more server computing devices including one or more processors configured to execute instructions for a domain extensibility module that provides software development tools for building domain extensions for a database platform, and a data ingestion module that provides software development tools for defining a metadata schema for extracting metadata from data files. The one or more processors are configured to receive a set of data from a user computing device, define a target metadata schema that includes one or more metadata fields that will be populated during a data ingestion process, define a target domain extension that defines one or more data types for storing the received set of data after performing the data ingestion process, and ingest the received set of data using a metadata extraction pipeline to generate metadata files based on the target metadata schema.

Abstract: A computing system is provided for intelligent monitoring and management of an electrical system. The system receives electrical usage data and electrical production data via a computer network from a plurality of grid agents at a plurality of points across the electrical system; receives a privacy permission from an owner of one of the plurality of grid agents; based on the privacy permission, authorize sharing of a shareable portion of the electrical usage data and/or a shareable portion of the electrical production data and restrict sharing of a restricted portion of the electrical usage data and/or a restricted portion of the electrical production data; and executes a prediction model which processes patterns observed in the shareable portion of the electrical usage data and the shareable portion of the electrical production data, and predicts a forecasted aspect of the electrical system; and outputs the forecasted aspect of the electrical system.

Abstract: A remote server computing system is configured to deploy a cloud-service-managed control plane and a cloud service data plane spanning the remote server computing system, a local edge computing device, and a local on-premises computing device connected in a hybrid cloud environment. Energy-related training data is received including a plurality of energy-related training data pairs. A machine learning function is trained using the plurality of training data pairs to predict a classified label for restricted energy-related data that is not accessible to the remote server computing system. The trained machine learning function is deployed to the one or more of the local edge computing device and the local on-premises computing device via the cloud service data plane. The remote server computing system is further configured to receive, via the cloud service data plane, classified output of the trained machine learning function.

Abstract: A remote server computing system is configured to present a user interface with a plurality of deployment configuration options including compute configuration options and data storage configuration options for energy-related data within a hybrid cloud environment. The hybrid cloud environment comprises a cloud-service-managed control plane and a data plane utilizing local compute resources and storage. A data control policy is generated that provides cloud-service-managed governance over at least a portion of the data plane. The control plane is configured to enforce the data control policy by subjecting at least a portion of the energy-related data to a data transmission restriction or a local storage restriction. The data plane is used to deploy one or more cloud service functions configured to process at least the portion of the energy-related data and output one or more extracted features from at least the portion of the energy-related data to the data plane.

Abstract: The disclosed technology is generally directed to an audit ledger for externality tracking. In one example of the technology, auditing of a virtual sensor is enabled. The virtual sensor is configured to output an externality value based on received telemetry data. The externality values are associated with the at least one particular type of quantifiable technical externality. Signing of the virtual sensor is enabled. Audit information associated with the signing of the virtual sensor is stored on a ledger. Periodic aggregated externality values based on the externality values output by the virtual sensor are calculated. The periodic aggregated externality values are stored on a distributed ledger. An audit of the periodic aggregated externality values is enabled. The auditing of the periodic aggregated externality values includes verifying the signing of the virtual sensor by the auditor based on the stored audit information.

Abstract: A computing system that includes one or more server computing devices including one or more processors configured to execute instructions for a domain extensibility module that provides software development tools for building domain extensions for a database platform, and a data ingestion module that provides software development tools for defining a metadata schema for extracting metadata from data files. The one or more processors are configured to receive a set of data from a user computing device, define a target metadata schema that includes one or more metadata fields that will be populated during a data ingestion process, define a target domain extension that defines one or more data types for storing the received set of data after performing the data ingestion process, and ingest the received set of data using a metadata extraction pipeline to generate metadata files based on the target metadata schema.

Abstract: Examples are disclosed that relate to an energy data platform. One example provides a method comprising receiving a first energy data set having a first data format, and a second energy data set having a second data format, and ingesting the first energy data set and the second energy data set by automatically converting one or more of the first energy data set and the second energy data set into a standard data format. The method further comprises receiving a request from a first application to provide the first energy data set in the first data format, and in response, providing the first energy data set in the first data format, and receiving a request from a second application to provide the first energy data set in the standard data format, and in response, providing the first energy data set in the standard data format.

Abstract: A computing device including a processor is provided. For one or more emissions factors, the processor may be configured to receive respective emissions factor data including sensor data received from one or more sensors and user-specified data received at a user interface. The processor may be further configured to receive a plurality of virtual sensor models. Each virtual sensor model may include one or more respective virtual sensor model parameters. At least in part by applying the virtual sensor models to the emissions factor data, the processor may be further configured to compute a plurality of virtual sensor readouts. The processor may be further configured to generate a greenhouse gas emissions estimate from the virtual sensor readouts. The processor may be further configured to convey an indication of the greenhouse gas emissions estimate to the user interface for output on one or more output devices.

Abstract: One example provides a computing system configured to implement a greenhouse gas (GHG) tracking and auditing platform for tracking GHG emissions associated with a plurality of GHG entities, each GHG entity representing one or more of a GHG source and a GHG sink. The computing system comprises a logic subsystem and a data-holding subsystem comprising computer-readable instructions. The instructions are executable to receive sensor data over time from each sensor of one or more sensors, each sensor configured to sense data related to GHG emissions. The instructions are further executable to, for each time interval of a plurality of time intervals, determine a GHG emission amount based at least in part on the sensor data and store GHG emission data. The instructions are further executable to receive a request for a set of GHG emission data and in response to the request, output the set of GHG emission data.

Abstract: A computer system comprises a logic system, and, operatively coupled to the logic system, a computer-memory system holding instructions that, when executed by the logic system, cause the computer system to: receive a token-behavior selection corresponding to a real-world asset to be tracked on a virtual ledger; receive a virtual-ledger architecture selection identifying a provider-defined architecture of the virtual ledger; construct a template for registration of a token class on the virtual ledger according to the provider-defined architecture of the virtual ledger, wherein each new token instantiated from the token class exhibits a set of behaviors determined by the token-behavior selection; and provide access to the template to a client computer device.

Abstract: Techniques for implementing a ledger-independent token service are provided. According to one set of embodiments, a computer system executing the service can receive, from a user, a request to create a token on a distributed ledger network. The computer system can further provide to the user one or more token templates, where each token template corresponds to a type of physical or digital asset and defines a set of one or more attributes and one or more control functions associated with the type. The computer system can then receive, from the user, a selection of a token template in the one or more token templates and create the token on the distributed ledger network, where the created token includes the set of one or more attributes and one or more control functions defined in the selected token template.

Abstract: A computer system comprises a logic system, and, operatively coupled to the logic system, a computer-memory system holding instructions that, when executed by the logic system, cause the computer system to: receive a token-behavior selection corresponding to a real-world asset to be tracked on a virtual ledger, the token behavior selection identifying a client-defined combination of behaviors; construct a template for registration of a token class on the virtual ledger according to the provider-defined architecture of the virtual ledger, wherein each new token instantiated from the token class exhibits the client-defined combination of behaviors as determined by the token-behavior selection; and provide access to the template to a client computer device.

Abstract: A computer system comprises a logic system, and, operatively coupled to the logic system, a computer-memory system holding instructions that, when executed by the logic system, cause the computer system to: receive a token-behavior selection corresponding to a real-world asset to be tracked on a virtual ledger; receive a virtual-ledgerarchitecture selection identifying a provider-defined architecture of the virtual ledger; construct a template for registration of a token class on the virtual ledger according to the provider-defined architecture of the virtual ledger, wherein each new token instantiated from the token class exhibits a set of behaviors determined by the token-behavior selection; and provide access to the template to a client computer device.

Abstract: A computer system comprises a logic system, and, operatively coupled to the logic system, a computer-memory system holding instructions that, when executed by the logic system, cause the computer system to: receive from a first client a token-behavior selection corresponding to a real-world asset to be tracked on a virtual ledger, the token-behavior selection identifying a client-defined combination of behaviors; construct a reusable template for registration of a token class on the virtual ledger according to a provider-defined architecture of the virtual ledger, wherein each new token instantiated from the token class exhibits the client-defined combination of behaviors as determined by the token-behavior selection; and provide marketplace access to the constructed template to a client computer device operated by a second client that differs from the first client.

Abstract: A computer system comprises a logic system, and, operatively coupled to the logic system, a computer-memory system holding instructions that, when executed by the logic system, cause the computer system to: receive a token-behavior selection corresponding to a real-world asset to be tracked on a virtual ledger; construct a template for registration of a token class on the virtual ledger according to a provider-defined architecture of the virtual ledger, wherein each new token instantiated from the token class exhibits a set of behaviors determined by the token-behavior selection; receive client metadata for assignment to a variable property of each new token of the token class; assign the client metadata to the variable property within the token class; and provide access to the template to a client computer device.

Abstract: Techniques for implementing a ledger-independent token service are provided. According to one set of embodiments, a computer system executing the service can receive, from a user, a request to create a token on a distributed ledger network. The computer system can further provide to the user one or more token templates, where each token template corresponds to a type of physical or digital asset and defines a set of one or more attributes and one or more control functions associated with the type. The computer system can then receive, from the user, a selection of a token template in the one or more token templates and create the token on the distributed ledger network, where the created token includes the set of one or more attributes and one or more control functions defined in the selected token template.