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.

The disclosure is directed to various ways of improving the functioning of computer systems, information networks, data stores, search engine systems and methods, and other advantages. Among other things, provided herein are methods, systems, components, processes, modules, blocks, circuits, sub-systems, articles, and other elements (collectively referred to in some cases as the “platform” or the “system”) that collectively enable, in one or more datastores (e.g., where each datastore may include one or more databases) and systems, the creation, development, maintenance, and use of a set of custom objects for use in a wide range of activities, including sales activities, marketing activities, service activities, content development activities, and others, as well as improved methods and systems for sales, marketing and services that make use of such entity resolution systems and methods as well as custom objects.

An in-memory graph query runtime is integrated inside a database management system and is capable of performing simple patter-matching queries against homogeneous graphs. The runtime efficiently combines breadth-first (BFS) and depth-first (DFS) neighbor traversal algorithms to achieve a hybrid runtime that takes the best from both sides. As a result, the hybrid runtime is able to process arbitrarily large queries with a fixed amount of memory, optimizing for memory locality.

Disclosed is a method for a hub module to perform flow-based processing, which includes: receiving a flow including at least one task; a stored procedure generation allowance step for allowing a DBMS module to generate a stored procedure based on the flow when the received flow is not a previously processed flow, wherein the stored procedure includes at least one of a flow query, meta information, and exception handling information, and the meta information includes execution time of the flow query, execution results, and user information; and a stored procedure call allowance step for allowing the DBMS module to call the stored procedure corresponding to a previously processed flow when the received flow is the previously processed flow.

The disclosure is directed to various ways of improving the functioning of computer systems, information networks, data stores, search engine systems and methods, and other advantages. Among other things, provided herein are methods, systems, components, processes, modules, blocks, circuits, sub-systems, articles, and other elements (collectively referred to in some cases as the “platform” or the “system”) that collectively enable, in one or more datastores (e.g., where each datastore may include one or more databases) and systems, the creation, development, maintenance, and use of a set of custom objects for use in a wide range of activities, including sales activities, marketing activities, service activities, content development activities, and others, as well as improved methods and systems for sales, marketing and services that make use of such entity resolution systems and methods as well as custom objects.

Techniques are described for storing and maintaining, in a materialized view, bitmap data that represents a bitmap of each possible distinct value of an expression and rewriting a query for a count of distinct values of the expression using the materialized view. The materialized view contains bitmap data that represents a bitmap of each possible distinct value of a first expression, and aggregate values of additional expressions, and is stored in memory or on disk by a database system. The database system receives a query that requests a number of distinct values, of the first expression, and an aggregate value for an additional expression. In response, the database system, rewrites the query to: compute the number of distinct values by counting the bits in the bitmap data of the materialized view that are set to the first value, and obtains the aggregate value for the additional expression in the materialized view.

Techniques are described herein for early pruning of potential graph query results. Specifically, based on determining that property values of a path through graph data cannot affect results of a query, the path is pruned from a set of potential query solutions prior to fully exploring the path. Early solution pruning is performed on prunable queries that project prunable functions including MIN, MAX, SUM, and DISTINCT, the results of which are not tied to a number of paths explored for query execution. A database system implements early solution pruning for a prunable query based on intermediate results maintained for the query during query execution. Specifically, when a system determines that property values of a given potential solution path cannot affect the query results reflected in intermediate results maintained for the query, the path is discarded from the set of possible query solutions without further exploration of the path.

A database engine receives a database query that specifies retrieving data from a data source. The database engine parses the query to build an operator tree that includes a TableScan operator configured to scan a table from the data source to produce outputs corresponding to a single data field from the table, and includes a GroupBy operator that groups rows of the table according to the data field. The database engine generates and executes code corresponding to the operator tree to retrieve a result set. When the TableScan operator is a child of the GroupBy operator and the outputs are independent of duplicate input rows from the table, execution of the TableScan operator comprises, for each storage block of rows from the table: determining a storage compression scheme for encoding the data field and, for certain encodings, using the encoding to produce the outputs without duplication of rows.

A database engine receives a human-readable database query that includes a plurality of conditional expressions. The database engine parses the database query to build an operator tree that includes a subtree corresponding to each of the conditional expressions. The database engine identifies a subexpression that appears in two or more of the conditional expressions. The subexpression is executed conditionally. The database engine hoists the subexpression outside of the conditional expression so that it is executed unconditionally. The database engine modifies the operator tree to specify computation of a value for the subexpression a first time and to reuse the computed value when the subexpression is subsequently encountered. The database engine executes the modified operator tree to form a result set corresponding to the database query, thereby evaluating the subexpression only a single time for each row of input data and returns the result set.

The present approach relates to improving query performance in a database context. Examples of query improvement are described in the context of certain query patterns, one or more of which may be observed in a given query. When a given query pattern is observed, changes may be made to the query at the application or database level to improve performance of the respective query. Query improvements may be performed in a manner transparent to the user.