A fast-copyable database apparatus includes a processor and a memory in electronic communication with the processor. The memory stores a database table with multiple records each having a compound key including a version identifier. The memory also stores instructions executable to cause the processor to generate a first virtual clone of the database table and a second virtual clone of the database table, receive a query at the first virtual clone, and modify the query so that it includes a version identifier of the first virtual clone. The processor can query the database table using the modified query to generate a response based in part on each record from the set of records having a version identifier associated with an ancestral path of the version identifier of the first virtual clone, and send the response. Each compound key includes a version identifier and a data portion.

A computer-implemented method for power grid anomaly detection using a convolutional neural network (CNN) trained to detect anomalies in electricity demand data and electricity supply data includes receiving (i) electricity demand data comprising time series measurements of consumption of electricity by a plurality of consumers, and (ii) electricity supply data comprising time series measurements of availability of electricity by one or more producers. An input matrix is generated that comprises the electricity demand data and the electricity supply data. The CNN is applied to the input matrix to yield a probability of anomaly in the electricity demand data and the electricity supply data. If the probability of anomaly is above a threshold value, an alert message is generated for one or more system operators.

In examples, a requestor device requests data from a data platform. A response from the data platform may include a version identifier indicating a version of data used to process the request and a device identifier indicating the server device that processed the request. Accordingly, the requestor device may include the version identifier and device identifier in a subsequent request, such that the request is routed to the server device accordingly. In examples, the server device may evaluate the version identifier to determine whether the request is associated with a different version of the data, as may occur when the data of the server device has since been updated. Similarly, the requestor device may evaluate a version identifier from the data platform as compared to that of a previously received response to determine whether the response is associated with a different version of data than that of a previous response.

As typical databases evolve and the schema defining the stored data changes, difficulties arise in interfacing with the database and compatibility to applications may be lost. Provided for are methods for retrieving data stored in a database using versioned reference perspectives of edges, which define relationships between nodes. The methods provide for backwards-compatibility in accessing node-data stored in accordance with a pre-defined schema based upon a request including a version identifier. Also provided for are backend systems, frontend systems, and industrial machines for the manipulation of work products.

Techniques for custom time series analysis with large-scale datasets are described. A time series data analysis service provides an interactive development environment that is configured to accept user input to configure stages of a time series analysis data pipeline. The stages include one or more of a collection stage to place events from a dataset into groupings of ones of the events, a summary stage to generate a set of summary statistics based on the groupings of events, a fill and filter stage to add or remove summary statistics to or from the set of summary statistics, and/or an analytics stage to apply analytical functions based at least in part on the set of summary statistics. The stages can be executed at least partially in a distributed manner by a cluster of computing instances executing an analytics engine.

A method includes receiving, by a processing device, a command search query for searching a command search history, identifying, by the processing device, a command from the command search history in view of the command search query, determining, by the processing device, that the command comprises a replaceable parameter, identifying, by the processing device, a content source corresponding to the replaceable parameter, and generating, by the processing device, an updated command by replacing the replaceable parameter with a data item from the content source.

Systems and methods for providing scalable object storage query capabilities in a distributed storage system are disclosed. In one implementation, a processing device may receive, by an object-based distributed storage system, a request from a client to execute a query with respect to data stored at the distributed storage system. The processing device may execute the query to produce a result object and may store the result object at the distributed storage system. The processing device may further transmit the result object to the client. The processing device may re-execute the query at a subsequent point in time to update the result object and transmit the updated result object to the client.

Aspects of the present disclosure relate to wireless communications, and more particularly, to techniques for control signal transmission. One aspect provides a method for wireless communication by a user equipment (UE). The method generally includes receiving, from a base station (BS), a message indicating multiple configurations for physical uplink control channel (PUCCH) transmission; selecting, from a sequence database, a sequence to be used for the transmission of a PUCCH in accordance with one of the multiple configurations indicated by the message from the base station; and transmitting the PUCCH using the selected sequence.

An online system receives an identification code for a product from a third party, which includes attributes that the third party uses to identify the product. The online system normalizes the identification code according to a set of guidelines received from the third party. The normalized identification code resembles previous identification codes received from the third party. The online system identifies a cluster of identification codes that represents the product identified by the normalized identification code by applying a set of matching rules to the normalized identification code and updates the identified cluster of identification codes to include the normalized identification code. The online system identifies a universal product identifier that represents the product of the cluster of the cluster of identification codes and stores the universal product identifier with the updated cluster of identification code.

Feedback tuples are implemented in a stream computing environment to provide data flow status information for use in detecting, identifying and/or handling data flow anomalies for a stream computing application.