Techniques for request throttling in a provider network environment are described. A throttle handler controls whether requests will be processed through maintaining a token-based record, per type of request, having a token value indicative of a number of requests that can be processed over a time period. For a request, the token value of the token-based record corresponding to the request type is updated based on calculating an elapsed time between a last update time of the token-based record and the current time, calculating an intermediate token value as the existing token value plus a value of the elapsed time multiplied by a rate, and updating the token value to be the minimum between the intermediate token value and a burst value. The request is serviced when the updated token value is determined to be greater than or equal to a number of tokens needed to perform the request.

Provided are methods and systems comprising receiving a plurality of data tables, wherein each data table comprises at least one row, generating a first handle for a first value in a table of the plurality of tables, wherein the first handle comprises an indication of whether a row in each of the plurality of tables is associated or not associated with the first value, and performing one or more operations on the first handle.

Embodiments of the present disclosure may provide a data clean room architecture that restricts data included in the clean room. The data clean room architecture can implement a policy to enable data restrictions for data shared between multiple parties via a distributed database. Multiple database accounts can implement validation instances to validate queries when received from other database accounts. One or more of the database accounts can provide a query template that is congruent with the validation instance for use by the other database accounts to generate queries against the data shared in the data clean room.

System (100) comprising several edge computing devices (ECD), each comprising a sensor (S); a memory (M); a Central Processing Unit, CPU; and digital communication interface (IF), for communication across a network (NW).

The invention is characterised in that each edge computing device is arranged with a respective interpreting software function, arranged to execute on said CPU and to interpret computer code, received via said digital communication interface and stored in said memory, according to a query language having a predetermined syntax; in that said syntax is arranged to define queries the results of which are streams of data; in that a first such software function is arranged to pose a first query to a second edge computing device (120), in that a second such software function of said type is arranged to, in response thereto, generate and communicate a second stream of data; in that the first software function is arranged to preprocessing said second stream so that it adheres to a predefined global data ontology, and to perform a first calculation using said second stream.

The invention also relates to a method.

First and second database accounts respectively share first and second source datasets with one another in a data clean room. The first database account stores a first approved-statements table that contains database statements that are permitted by the first database account to be executed against a combination of the shared first source dataset and the shared second source dataset. The second database account stores a second approved-statements table that contains database statements that are permitted by the second database account to be executed against the combination of the shared first source dataset and the shared second source dataset. One or more queries received from the first database account that are included in the second approved-statements table are processed, as are one or more queries received from the second database account that are included in the first approved-statements table.

Herein are techniques that extend a software system to embed new guest programing languages (GPLs) that interoperate in a transparent, modular, and configurable way. In embodiments, a computer inserts an implementation of a GPL into a deployment of the system. A command registers the GPL, define subroutines for the GPL, generates a guest virtual environment, and adds a binding of a dependency to a guest module. In an embodiment, a native programing language invokes a guest programing language to cause importing intra- or inter-language dependencies. An embodiment defines a guest object that is implemented in a first GPL and accessed from a second GPL. In an embodiment, dependencies are retrieved from a virtual file system having several alternative implementation mechanisms that include: an archive file or an actual file system, and a memory buffer or a column of a database table.

A system, program product, and method for use with an artificial intelligence (AI) platform to dynamically amend a knowledge base responsive to query evaluating and processing. A received or detected query is subject to natural language processing to identify, annotate, and map one or more query tokens against a knowledge base. The query tokens are evaluated against the knowledge base to identify one or more query tokens absent from the knowledge base and leverage a neural network to predict a probability relationship between the query tokens absent from the knowledge base and one or more tokens populated in the knowledge base. The natural language (NL) query is translated to a structured query language (SQL) and the SQL query is executed and evaluated, and the knowledge base is selectively and dynamically amended subject to the SQL evaluation.

An example method of using ad-hoc stored procedures for OLTP operations includes: identifying, by a processing device, one or more values for updating a dataset stored in a database; creating an ad-hoc stored procedure comprising a sequence of commands specifying atomically-executable database update operations with respect to the dataset, wherein the atomically-executable database update operations utilize the one or more values that are encoded into the sequence of commands by respective inline constant values; executing the ad-hoc stored procedure, wherein the executing comprises acquiring a temporary lock of at least part of the dataset; and dropping the ad-hoc stored procedure.

The disclosed systems and methods provide a novel framework that optimizes SQL queries that are generated from a templated virtual sematic layer. The framework introduces the use of a virtual semantic layer into database management systems' operations, whereby templated SQL queries can be rewritten according to a determined and measured nesting, dimensional structure that produces an optimized search system. This enables templated SQL fragments to be translated for query optimization, thereby reducing the drain on a database's resources and minimizing a query's impact on the database's performance.

Aspects of the disclosure relate to a machine learning based identification and classification of database commands. A computing platform may retrieve, by a computing device and from a first database of a plurality of databases, a database command. Subsequently, the computing platform may identify, by the computing device and for the database command and based on a machine learning model, one or more database commands from the plurality of databases, wherein the one or more database commands perform operations similar to the database command. Then, the computing platform may determine, by the computing device and for the database command, a security score indicative of a level of vulnerability associated with the database command. Subsequently, the computing platform may provide, via an interactive graphical user interface, the database command and the security score.