A method includes generating array field distribution data for an array field of a dataset. A query expression for execution that includes a query predicate indicating the array field of the dataset is received. The array field distribution data for the array field is utilized to generate query plan data for the query expression based on the query expression including the query predicate indicating the array field. Execution of the query expression is facilitated in accordance with the query plan data.

Methods, systems, and media for interpreting queries are described herein. For example, an illustrative method may include: determining, based on a voice query received in a search domain, a first voice recognition term and a second voice recognition term; determining that at least a portion of the first voice recognition term corresponds to an entity name associated with the search domain; determining, based on the entity name, a feasibility score for the first voice recognition term; ranking, based on the feasibility score, the first voice recognition term over the second voice recognition term; and executing the voice query in the search domain using the first voice recognition term, the first voice recognition term being selected for use over the second voice recognition term based on the ranking. Corresponding systems, devices, and other implementations are also described.

Techniques are described for managing the execution of one or more groups of queries. Embodiments of the present disclosure may generally receive a group of queries to be executed against a database. Embodiments also determine, based on one or more attributes of the group of queries, an expected amount of resources that will be used in executing the group of queries against the database. Embodiments further schedule one or more queries of the group of queries for execution against the database based on the expected amount of resources to be used for the group of queries.

Systems and methods for assessing quality of input text using recurrent neural networks is disclosed. The system obtains input text from user and performs a comparison of each word from input text with words from dictionary (or trained data) to determine a closest recommended word for each word in the input text. The input text is further analyzed to determine context of each word based on at least a portion of input text, and based on determined context, at least one of correct sentences, incorrect sentences, and/or complex sentences are determined from the input text. Each word is converted to a vector based on concept(s) by comparing each word across sentences of input text to generate vectors set, and quality of the input text is assessed based on vectors set, the comparison, determined context and at least one of correct sentences, incorrect sentences, complex sentences, or combinations thereof.

Techniques are provided for sharing expression evaluation across a database management system and a storage layer. In an embodiment, a computer identifies, in a query execution plan compiled for a query, a plurality of target operators that includes an expression that computes a common value. A storage layer evaluates the expression that computes the common value during a table scan of stored data indicated by the expression. For at least one target operator of the plurality of target operators, the computer modifies a row input of the at least one target operator to add a column to receive an evaluation result comprising the common value from the storage layer. The query execution plan is performed by a database management system and the storage layer.

Techniques are described for managing the execution of one or more groups of queries. Embodiments of the present disclosure may generally receive a group of queries to be executed against a database. Embodiments also determine, based on one or more attributes of the group of queries, an expected amount of resources that will be used in executing the group of queries against the database. Embodiments further schedule one or more queries of the group of queries for execution against the database based on the expected amount of resources to be used for the group of queries.

An example method of processing a plurality of queries includes determining, at a database driver, that a first query and a second query are similar. The first query is sent from a first execution thread executing in an application server. The method also includes generating, at the database driver, a union query based on the first and second queries. The method further includes submitting the union query to a database that stores database tables from which data is selected based on the first and second queries. The method also includes receiving a combined result set of the union query from the database and submitting the first query against the combined result set. The method further includes receiving, at the database driver, a first result set of the first query in response to submitting the first query against the combined result set.

Representative embodiments disclosed compress expressions so they utilize less physical storage. An expression is placed in a standard representation, such as an expression tree. The system utilizes one or more rules to identify portions of the expression that are likely to be common to other expressions. The common portions are extracted from the expression tree as a template and the remaining portions are hoisted from the expression as unique portions. If the template does not already reside in a cache, the template is stored in the cache. A cache reference is obtained for the template and combined with the unique portions to create a bundle that reduces storage requirements for the expression. The original expression is recovered by retrieving the template from the cache using the cache reference and placing the hoisted unique portions into their original locations in the template.

A method includes generating array field distribution data for an array field of a dataset. A query expression for execution that includes a query predicate indicating the array field of the dataset is received. The array field distribution data for the array field is utilized to generate query plan data for the query expression based on the query expression including the query predicate indicating the array field. Execution of the query expression is facilitated in accordance with the query plan data.

Interleaved infrastructure can interleave fragment execution during query compilation. For example, compilation can be paused, and a plan fragment is executed. The results of the plan fragment can be fed back into the compiler, which can then restart the compilation and modify the compilation based on the results of the plan fragment execution. The interleaved workflow provides a flexible, robust infrastructure, which can enable and improve a variety of database features and query optimizations.