Abstract: Various methods, apparatuses/systems, and media for data migration readiness of a target data source are disclosed. A processor receives a request having configurable filters and parameters from a consumer service to obtain data; invokes, in response to receiving the request, a service application programming interface (API) for the data; fetches requested data from a legacy data source in response to calling the service API; fetches data corresponding to the same request having the same configurable filters and parameters from a target data source; compare the fetched data from the legacy data source to the fetched data from the target data source; generates, in response to comparing, a data quality validation score; and when it is determined that the data quality validation score is equal to or more that predetermined threshold value, validates the target data source for migration readiness and terminating the legacy data source.

Abstract: In one example, a method for dynamically scaling computer resources in a cloud computing environment is disclosed. The method includes determining lag state information of a message broker. The message broker handles real-time data exchanged with application instances running in a cloud service. The method includes determining whether the lag state information indicates a change to the application instances running in the cloud service. If the lag state information indicates a change, the method includes providing instructions to the cloud service to alter the application instances.

Abstract: A method for providing ingestion checkpointing from an event topic is disclosed. The method includes polling, via an event consumer, a message broker to initiate consumption of events from the event topic, the event topic relating to event storage; checkpointing, based on a result of the polling, the consumption of the events by writing corresponding data in multiple stages to a checkpointing durable store; inserting an event pending status for each of the events in an events time bucket table; committing, via the event consumer, the consumption of the events; initiating event processing for the events; and updating the events time bucket table with an event complete status when consumption of the events are completed.

Abstract: Various methods, apparatuses/systems, and media for data migration readiness of a target data source are disclosed. A processor receives a request having configurable filters and parameters from a consumer service to obtain data; invokes, in response to receiving the request, a service application programming interface (API) for the data; fetches requested data from a legacy data source in response to calling the service API; fetches data corresponding to the same request having the same configurable filters and parameters from a target data source; compare the fetched data from the legacy data source to the fetched data from the target data source; generates, in response to comparing, a data quality validation score; and when it is determined that the data quality validation score is equal to or more that predetermined threshold value, validates the target data source for migration readiness and terminating the legacy data source.

Abstract: In one example, a method for updating an application that has multiple instances running in a distributed cloud environment is disclosed. The method may include the operation of generating a dynamic property bus interface. The method may write an application property value through the dynamic property bus interface to a database for storage and propagate the property value to the multiple instances of the application in the distributed cloud environment.

Abstract: Methods, computer systems, and apparatus, including computer programs encoded on computer storage media, for training a classification neural network. The system generates, from a set of object-specific data, one or more meta-learning datasets for one or more respective initial training tasks. The system determines values for a set of meta parameters by performing meta-learning with a classification neural network on the one or more meta-learning datasets. The system obtains a set of labeled training examples for a characteristic-detection task. The system determines based at least on one of the values for the set of meta parameters and using the set of labeled training examples, target values for the network parameters for the classification neural network to perform the characteristic-detection task.

Abstract: In one example, a method for updating an application that has multiple instances running in a distributed cloud environment is disclosed. The method may include the operation of generating a dynamic property bus interface. The method may write an application property value through the dynamic property bus interface to a database for storage and propagate the property value to the multiple instances of the application in the distributed cloud environment.

Abstract: A technique is described herein for using computing technology to intelligently manage the consumption of a resource in a physical environment and/or controlling the physical environment in other ways. The technique maintains environment information that describes entities within the physical environment, together with the relationships among the entities. The technique leverages the environment information and collected sensor data to generate forecast data using one or more machine-trained models. The technique then leverages the environment information, sensor data, and forecast data to generate a control plan. The control plan provides a strategy for controlling the physical environment that satisfies a specified optimization objective. In one use case, the technique contributes to the efficient consumption of power provided by a distribution system by avoiding consumption of power in periods in which the distribution system is expected to experience high loads.

Abstract: A method for processing events comprising time series data may include inferring different schema associated with the events. The method may also include storing property definitions corresponding to the events. Each property definition may include a name and a data type. The method may also include storing schema definitions corresponding to the different schema that are inferred. Each schema definition may include a set of one or more properties. The method may also include updating at least one data structure for storing information about the events based on the different schema that are inferred.

Abstract: A technique is described herein for using computing technology to intelligently manage the consumption of a resource in a physical environment and/or controlling the physical environment in other ways. The technique maintains environment information that describes entities within the physical environment, together with the relationships among the entities. The technique leverages the environment information and collected sensor data to generate forecast data using one or more machine-trained models. The technique then leverages the environment information, sensor data, and forecast data to generate a control plan. The control plan provides a strategy for controlling the physical environment that satisfies a specified optimization objective. In one use case, the technique contributes to the efficient consumption of power provided by a distribution system by avoiding consumption of power in periods in which the distribution system is expected to experience high loads.

Abstract: A method for generating scheduling recommendations for energy consumption tasks includes determining an estimated time-variant quantity of carbon emissions released from an energy supply plant over a future time interval; predicting a probability of user compliance with a recommendation to initiate an energy consumption task at one or more times within the future time interval; selecting a recommended start time for the energy consumption task based on both the predicted time-variant quantity of carbon emissions and the predicted probability of user compliance with the recommendation at the recommended start time; and outputting the recommended start time.

Abstract: A method for processing events comprising time series data may include inferring different schema associated with the events. The method may also include storing property definitions corresponding to the events. Each property definition may include a name and a data type. The method may also include storing schema definitions corresponding to the different schema that are inferred. Each schema definition may include a set of one or more properties. The method may also include updating at least one data structure for storing information about the events based on the different schema that are inferred.

Abstract: A method for facilitating access to information contained within stored events may include receiving a request to provide information about a set of events. The set of events may correspond to time series data from a plurality of devices. The method may also include identifying patterns within the set of events in response to the request. Identifying the patterns within the set of events may include performing basket analysis. The method may also include selecting a subset of the patterns based at least partially on percentage of occurrence within the set of events and pattern similarity.

Abstract: A method for improving readability of a heatmap representing time series data may include obtaining a set of time series. Each time series may be associated with a key property and may include a set of values. The set of values in a time series may include results of performing an aggregate function with respect to a measure property in events that are associated with the key property, and at time intervals having an interval size. For each time series, an average value may be determined for the set of values within the time series. A heatmap may be rendered based on the set of time series. The set of time series may be ordered vertically in the heatmap based on the set of average values that are determined for the set of time series.