The current disclosure generally relates to database management systems (DBMSs) and may be generally directed to methods and systems of using artificial intelligence (i.e. machine learning and/or anticipation functionalities, etc.) to learn a user's use of a DBMS, store this knowledge in a knowledgebase, and anticipate the user's future operating intentions. The current disclosure may also be generally directed to associative methods and systems of constructing DBMS commands. The current disclosure may also be generally directed to methods and systems of using a simplified DBMS command language (SDCL) for associative DBMS command construction. The current disclosure may also be generally directed to artificially intelligent methods and systems for associative DBMS command construction. The current disclosure may also be generally directed to methods and systems for associative DBMS command construction through voice input. Other methods, systems, features, elements and/or their embodiments are also disclosed.

Approaches presented herein enable optimization of a cache of compiled XML Path Language (XPath) expressions by removing variability from XPath expressions. More specifically, XPath expressions are identified that are the same but for one or more hardcoded values. These hardcoded values are identified and replaced in an identified XPath expression with an identifier to form a cache optimized XPath expression that lacks the hardcoded value variability of the identified XPath expressions. This cache optimized XPath expression is inserted into a cache optimized function that receives the hardcoded value as arguments and assigns the received hardcoded value to the identifier in the cache optimized XPath expression. The identified XPath expressions are then rewritten as calls to the cache optimized function or to another function wrapping the cache optimized function. Therefore, only the cache optimized XPath expression, instead of several of the identified XPath expressions, is stored in the XPath expression cache.

Methods, systems, and computer readable mediums for performing a free-form query are disclosed. According to one exemplary embodiment, a method for performing a free-form query includes receiving free-form information for requesting information about a computing system, converting the free-form information into at least one compatible query for querying at least one data set, querying, using the at least one compatible query, the at least one data set for the information about the computing system, and providing the information about the computing system.

A method of processing a query to a database from a query source is provided, comprising: receiving the query, the query in a first format supported by the query source; inputting the query into a first neural network; outputting, by the first neural network, the query in a second format, wherein the second format is a format supported by the database; receiving, from the database, a response to the query, the response in the second format; inputting the response to the query into a second neural network; outputting, by the second neural network, the response to the query in the first format; wherein each neural network is trained by inputting a first plurality of pairs of semi-structured data, each pair of semi-structured data comprising a sample query or response in the first format and the sample query or response in the second format.

Approaches presented herein enable optimization of a cache of compiled XML Path Language (XPath) expressions by removing variability from XPath expressions. More specifically, XPath expressions are identified that are the same but for one or more hardcoded values. These hardcoded values are identified and replaced in an identified XPath expression with an identifier to form a cache optimized XPath expression that lacks the hardcoded value variability of the identified XPath expressions. This cache optimized XPath expression is inserted into a definition of a cache optimized function. The optimized XPath expression receives values as arguments of the cache optimized Xpath function and passes the received values to the variable identifier in the cache optimized XPath expression. The identified XPath expressions can then be rewritten as calls to the cache optimized function. Therefore, only the cache optimized XPath expression, instead of several of the identified XPath expressions, is stored in the cache.

A computer-implemented method comprises obtaining export data representing a first classification in a first database that is to be imported into a second database, the first classification including a first set of classification markings that correspond to a first classification scheme identifier of the first database and that determine access to the first database and including a first plurality of origin classifications, each original classification of the first plurality of origin classifications including a classification scheme identifier; determining that a specific original classification of the first plurality of origin classifications has a specific classification scheme identifier matching a second classification scheme identifier of the second database; importing the specific original classification into the second database as an imported classification.

The current disclosure generally relates to database management systems (DBMSs) and may be generally directed to methods and systems of using artificial intelligence (i.e. machine learning and/or anticipation functionalities, etc.) to learn a user's use of a DBMS, store this knowledge in a knowledgebase, and anticipate the user's future operating intentions. The current disclosure may also be generally directed to associative methods and systems of constructing DBMS commands. The current disclosure may also be generally directed to methods and systems of using a simplified DBMS command language (SDCL) for associative DBMS command construction. The current disclosure may also be generally directed to artificially intelligent methods and systems for associative DBMS command construction. The current disclosure may also be generally directed to methods and systems for associative DBMS command construction through voice input. Other methods, systems, features, elements and/or their embodiments are also disclosed.

This invention is a system for integrating data sets organized in one organization type with data sets organized in a second organization type so that data queries submitted to be processed in the manner of the first organization type can be translated into queries usable by the data set in the second data organization type and the results returned to satisfy the first query.

This invention relates to query management. A query management method includes receiving a database query, generating an abstract syntax tree representation of the database path query into a set of java objects, and processing the abstract syntax tree representation of the database path query. The method further includes determining a selector upon processing the abstract syntax tree representation of the database path query and deriving a SELECT clause from the selector clause, where the SELECT clause indicates a portion of an expression from an XMeta Query Language (XMQL) query. The method yet further includes appending a FROM clause to the expression for the XMQL query, appending a WHERE clause to the expression for the XMQL query, and executing the expression for the XMQL query including the appended FROM clause and also the appended WHERE clause to access objects in an object repository.

Systems, devices, methods, and interfaces generally for use with database management systems (DBMSs) and DBMS interfaces (i.e. user interfaces, input interfaces, search interfaces, operating interfaces, etc.). In some aspects, the systems, devices, methods, and interfaces include using artificial intelligence (i.e. machine learning and/or anticipation functionalities, etc.) to learn a user's use of a DBMS or DBMS interface, store this knowledge in a knowledgebase, and anticipate the user's future operating intentions. In other aspects, the systems, devices, methods, and interfaces include disassembling user or other input into various types of portions (i.e. text, numbers, etc.) and determining one or more instructions for performing operations on a DBMS or DBMS interface based on the various types of portions. In further aspects, the systems, devices, methods, and interfaces include associative DBMS command construction. Other systems, devices, methods, interfaces, and features are also disclosed.