In some embodiments, a method includes determining a position for a search query and a position for each audience record from multiple audience records in an embedding space. The method further includes receiving multiple device records, each associated with an audience record. The method further includes determining multiple keywords, each associated with an audience record and determining a position for each keyword in the embedding space. The method further includes calculating a first distance between the position of the search query in the embedding space and the position of each audience record in the embedding space. The method further includes calculating a second distance between the position of the search query in the embedding space and the position of each keyword in the embedding space. The method further includes ranking each audience record based on the first distance and the second distance.

A home computing system (cloud) integrates a protocols gateway, WiFi router, cloud server, and mass storage device to support one or more Internet of Things (IoT) devices, possibly with different connectively protocols, in a local environment such as a residential home. The home computing cloud often reduces the amount of data traffic sent to a public computing cloud by locally processing collected device data rather than by sending the device data to the public computer cloud for processing. The home computing cloud may download an appropriate data analytic model from the public computing cloud, locally train (for example, reinforcement learning) the model, and locally execute the trained model to obtain prediction information from collected IoT device data. The home computing cloud also allows direct access of the connected IoT devices by user applications via the internet, through a protocols gateway and an IoT message translator.

Client systems of a distributed database system execute transactions on data stored within the distributed database system. The client systems communicate directly with database nodes of the distributed database system in order to execute transactions. The client systems interact with the database nodes of the distributed database system via a client-side interface that performs various operations to execute transactions at the distributed database nodes, including retrieving records, staging mutations or insertions, committing mutations or insertions, or rolling back mutations or insertions on records stored on the distributed database nodes. Interactions between the client-side interface and the database nodes of the distributed database system are further configured to prevent conflicts between different transactions executed by one or more client systems at the database nodes.

This disclosure relates generally to a method and system for extraction of SQL queries in an application. Various conventional approaches models SQL query extraction at a specific program point problem as an instance of string expression problem. Many string analysis based solutions are not scalable for large applications and those which are scalable do not account explicitly for the heap based flow. In an embodiment, the disclosed method and system utilizes a multi-criteria slicing based model which takes into account the data flowing through heap and contributing to SQL queries generation.

The present disclosure provides an apparatus for and method of generating a context category dataset. According to some embodiments, the present disclosure provides a context category dataset generating apparatus and method which predict a context category to which a user-inputted hashtag belongs, receive from the user the user's context category to which the hashtag belongs, and generate and update the context category dataset.

A method includes determining that a parser fails to parse an invalid structured query language (SQL) statement. In response to determining that the parser fails to parse the invalid SQL statement, the method generates, by an error parser, an output corresponding to the invalid SQL statement. The output includes a plurality of data structures arranged in a tree structure. Each of the plurality of data structures corresponds to a portion of the invalid SQL statement.

A computer-implemented method according to one embodiment includes identifying a relational database; determining columns of interest within the relational database; creating an unordered group of string tokens for each row of the relational database, utilizing the determined columns of interest; assigning weights for one or more columns within the relational database to one or more string tokens within each unordered group of string tokens to create a plurality of weighted unordered groups of string tokens; and determining a meaning vector for an identifier of each row of the relational database, utilizing the plurality of weighted unordered groups of string tokens.

A system for ranking machine learning base models for transfer learning purposes is described. The system receives image data in the form an image or an image set and extracts image tags from the images. The image tags are expanded into a set of associated terms using a word embedding database and model. The associated terms are used to query a knowledge database for parent or categorical terms used to rank various matching machine learning base models that may be improved or trained by the image data.

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.

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.